diff --git a/machine-learning/notebooks/r/OML4R Classification DT.json b/machine-learning/notebooks/r/OML4R Classification DT.json new file mode 100644 index 00000000..2428edf6 --- /dev/null +++ b/machine-learning/notebooks/r/OML4R Classification DT.json @@ -0,0 +1,1521 @@ +{ + "paragraphs": [ + { + "text": "%md\n## Classification modeling to predict Target Customers using a Decision Tree Model\nOracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.\n\nIn this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. \n\nHigh Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. \n\nThis notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.\n\nCopyright (c) 2023 Oracle Corporation\n###### The Universal Permissive License (UPL), Version 1.0\n", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:25+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "md", + "editOnDblClick": false + }, + "colWidth": 9, + "editorMode": "ace/mode/markdown", + "fontSize": 9, + "editorHide": true, + "results": {}, + "graph": { + "mode": "table", + "optionOpen": false, + "keys": [], + "values": [], + "scatter": {}, + "groups": [], + "height": 300 + }, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "HTML", + "data": "

Classification modeling to predict Target Customers using a Decision Tree Model

\n

Oracle Machine Learning for R (OML4R) makes the open source R scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4R integrates R with Oracle Autonomous Database, allowing users to run R commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using R syntax. Many familiar R functions are overloaded that translate R behavior into SQL for running in-database.

\n

In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program.

\n

High Affinity Card responders (target value = 1) are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount.

\n

This notebook builds and applies a classification decision tree model using the Sales History (SH) schema data. All processing occurs inside Oracle Autonomous Database.

\n

Copyright (c) 2023 Oracle Corporation

\n
The Universal Permissive License (UPL), Version 1.0
\n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_-1237949766", + "id": "20230307-171032_924699799", + "dateCreated": "2022-04-26T21:22:07+0000", + "dateStarted": "2023-03-07T17:40:26+0000", + "dateFinished": "2023-03-07T17:40:26+0000", + "status": "FINISHED", + "focus": true, + "$$hashKey": "object:41" + }, + { + "text": "%md\n
\n\"OML\n
", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:26+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "md", + "editOnDblClick": false + }, + "colWidth": 3, + "editorMode": "ace/mode/markdown", + "fontSize": 9, + "editorHide": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "HTML", + "data": "
\n\"OML\n
\n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_-424449825", + "id": "20230307-171032_1493630709", + "dateCreated": "2022-06-27T22:35:07+0000", + "dateStarted": "2023-03-07T17:40:26+0000", + "dateFinished": "2023-03-07T17:40:26+0000", + "status": "FINISHED", + "$$hashKey": "object:42" + }, + { + "title": "For more information ...", + "text": "%md\nOracle ADW Documentation: https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\nOML folder on Oracle Github : https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\nOML web page: https://www.oracle.com/machine-learning\nOML Classification: https://www.oracle.com/goto/ml-classification\nOML Decision Tree: https://oracle.com/goto/ml-decision-tree", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:27+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "md", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/markdown", + "fontSize": 9, + "editorHide": true, + "title": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "HTML", + "data": "

Oracle ADW Documentation: https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\n
OML folder on Oracle Github : https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\n
OML web page: https://www.oracle.com/machine-learning\n
OML Classification: https://www.oracle.com/goto/ml-classification\n
OML Decision Tree: https://oracle.com/goto/ml-decision-tree

\n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_1032488691", + "id": "20230307-171032_756203566", + "dateCreated": "2022-06-14T17:42:22+0000", + "dateStarted": "2023-03-07T17:40:27+0000", + "dateFinished": "2023-03-07T17:40:27+0000", + "status": "FINISHED", + "$$hashKey": "object:43" + }, + { + "title": "Import libraries", + "text": "%r\n\nlibrary(ORE)\noptions(ore.warn.order=FALSE)\nore.sync(query = c(\"SUP_DEM\" = \"select * from SH.SUPPLEMENTARY_DEMOGRAPHICS\"))\nore.attach()", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:27+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "text", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "editorHide": false, + "title": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + 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"[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]", + "tableOptionValue": { + "showAggregationFooter": false, + "showPagination": false, + "useFilter": false + }, + "tableGridState": {}, + "tableColumnTypeState": { + "names": { + "BOOKKEEPING_APPLICATION": "number", + "OS_DOC_SET_KANJI": "number", + "CUST_ID": "number", + "AFFINITY_CARD": "number", + "BULK_PACK_DISKETTES": "number", + "HOME_THEATER_PACKAGE": "number", + "COMMENTS": "string", + "EDUCATION": "string", + "Y_BOX_GAMES": "number", + "YRS_RESIDENCE": "number", + "PRINTER_SUPPLIES": "number", + "FLAT_PANEL_MONITOR": "number", + "OCCUPATION": "string", + "HOUSEHOLD_SIZE": "number" + }, + "updated": false + }, + "updated": false + } + } + } + } + }, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "TABLE", + "data": "CUST_ID\tEDUCATION\tOCCUPATION\tHOUSEHOLD_SIZE\tYRS_RESIDENCE\tAFFINITY_CARD\tBULK_PACK_DISKETTES\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tBOOKKEEPING_APPLICATION\tPRINTER_SUPPLIES\tY_BOX_GAMES\tOS_DOC_SET_KANJI\tCOMMENTS\n102547\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n101050\t10th\tOther\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n100040\t11th\tSales\t1\t0\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n102117\tHS-grad\tFarming\t1\t0\t0\t0\t0\t0\t1\t1\t1\t0\tNA\n101074\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n104179\t10th\tHandler\t1\t1\t0\t1\t1\t0\t0\t1\t1\t0\tNA\n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_-839671366", + "id": "20230307-171032_1805681818", + "dateCreated": "2022-04-26T21:23:24+0000", + "dateStarted": "2023-03-07T17:40:28+0000", + "dateFinished": "2023-03-07T17:40:28+0000", + "status": "FINISHED", + "$$hashKey": "object:45" + }, + { + "title": "Display number of rows and columns", + "text": "%r\n\nz.show(dim(SUP_DEM))", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:28+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "text", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "title": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "TEXT", + "data": "4500 14" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_-1734437451", + "id": "20230307-171032_96415309", + "dateCreated": "2022-06-02T22:35:46+0000", + "dateStarted": "2023-03-07T17:40:29+0000", + "dateFinished": "2023-03-07T17:40:29+0000", + "status": "FINISHED", + "$$hashKey": "object:46" + }, + { + "title": "Show distribution of AFFINITY_CARD responders", + "text": "%r\n\nz.show(ore.crosstab(~AFFINITY_CARD,SUP_DEM))\n", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:29+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "text", + "editOnDblClick": false + }, + "colWidth": 6, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "title": true, + "results": { + "0": { + "helium": {}, + "graph": { + "mode": "multiBarChart", + "optionOpen": false, + "keys": [ + { + "name": "AFFINITY_CARD", + "index": 0, + "aggr": "sum" + } + ], + "values": [ + { + "name": "ORE.FREQ", + "index": 1, + "aggr": "sum" + } + ], + "commonSetting": {}, + "groups": [], + "height": 300, + "setting": { + "multiBarChart": { + "rotate": { + "degree": "-45" + }, + "xLabelStatus": "default" + }, + "table": { + "initialized": false, + "tableOptionSpecHash": "[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]", + "tableOptionValue": { + "showAggregationFooter": false, + "showPagination": false, + "useFilter": false + }, + "tableGridState": {}, + "tableColumnTypeState": { + "names": { + "AFFINITY_CARD": "number", + "ORE.STRATA": "number", + "ORE.FREQ": "number", + "ORE.GROUP": "number" + }, + "updated": false + }, + "updated": false + } + } + } + }, + "1": { + "graph": { + "mode": "table", + "optionOpen": false, + "commonSetting": {}, + "height": 300, + "setting": { + "table": { + "initialized": false, + "tableOptionSpecHash": "[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]", + "tableOptionValue": { + "showAggregationFooter": false, + "showPagination": false, + "useFilter": false + }, + "tableGridState": {}, + "tableColumnTypeState": { + "names": { + "AFFINITY_CARD": "number", + "ORE.STRATA": "number", + "ORE.FREQ": "number", + "ORE.GROUP": "number" + } + }, + "updated": false + } + } + } + } + }, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "TABLE", + "data": "AFFINITY_CARD\tORE.FREQ\tORE.STRATA\tORE.GROUP\n0\t3428\t1\t1\n1\t1072\t1\t1\n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_2116069402", + "id": "20230307-171032_1041735012", + "dateCreated": "2022-06-02T22:38:41+0000", + "dateStarted": "2023-03-07T17:40:29+0000", + "dateFinished": "2023-03-07T17:40:29+0000", + "status": "FINISHED", + "$$hashKey": "object:47" + }, + { + "title": "Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders", + "text": "%r\n\nz.show(ore.crosstab(AFFINITY_CARD~HOUSEHOLD_SIZE,SUP_DEM))\n", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:29+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "text", + "editOnDblClick": false + }, + "colWidth": 6, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "editorHide": false, + "title": true, + "results": { + "0": { + "helium": {}, + "graph": { + "mode": "multiBarChart", + "optionOpen": false, + "keys": [ + { + "name": "HOUSEHOLD_SIZE", + "index": 1, + "aggr": "sum" + } + ], + "values": [ + { + "name": "ORE.FREQ", + "index": 2, + "aggr": "sum" + } + ], + "commonSetting": {}, + "groups": [ + { + "name": "AFFINITY_CARD", + "index": 0, + "aggr": "sum" + } + ], + "height": 300, + "setting": { + "multiBarChart": { + "rotate": { + "degree": "-45" + }, + "stacked": true, + "xLabelStatus": "default" + }, + "table": { + "initialized": false, + "tableOptionSpecHash": "[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]", + "tableOptionValue": { + "showAggregationFooter": false, + "showPagination": false, + "useFilter": false + }, + "tableGridState": {}, + "tableColumnTypeState": { + "names": { + "ORE$STRATA": "number", + "AFFINITY_CARD": "number", + "ORE$GROUP": "number", + "ORE$FREQ": "number" + } + }, + "updated": false + } + } + } + } + }, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "TABLE", + "data": "AFFINITY_CARD\tHOUSEHOLD_SIZE\tORE.FREQ\tORE.STRATA\tORE.GROUP\n0\t1\t681\t1\t1\n0\t2\t1040\t1\t1\n0\t3\t973\t1\t1\n0\t4-5\t112\t1\t1\n0\t6-8\t146\t1\t1\n0\t9+\t476\t1\t1\n1\t1\t11\t1\t1\n1\t2\t109\t1\t1\n1\t3\t814\t1\t1\n1\t4-5\t107\t1\t1\n1\t6-8\t2\t1\t1\n1\t9+\t29\t1\t1\n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_1108488739", + "id": "20230307-171032_148415560", + "dateCreated": "2022-04-26T21:24:19+0000", + "dateStarted": "2023-03-07T17:40:30+0000", + "dateFinished": "2023-03-07T17:40:30+0000", + "status": "FINISHED", + "$$hashKey": "object:48" + }, + { + "text": "%r\n\nDEMO_DF <- SUP_DEM[,c(\"CUST_ID\", 'AFFINITY_CARD', \"BOOKKEEPING_APPLICATION\", \"BULK_PACK_DISKETTES\", \"EDUCATION\", \"FLAT_PANEL_MONITOR\", \"HOME_THEATER_PACKAGE\", \n \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"OS_DOC_SET_KANJI\", \"PRINTER_SUPPLIES\", \"YRS_RESIDENCE\", \"Y_BOX_GAMES\")]\n ", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:30+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "text", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "title": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_861846088", + "id": "20230307-171032_1731425693", + "dateCreated": "2022-06-23T01:23:16+0000", + "dateStarted": "2023-03-07T17:40:30+0000", + "dateFinished": "2023-03-07T17:40:30+0000", + "status": "FINISHED", + "$$hashKey": "object:49", + "title": "Create a subset of the SUP_DEM data by selecting specific columns" + }, + { + "title": "Create train and test data (60/40 split)", + "text": "%r\n\nsampleSize <- .4 * nrow(DEMO_DF)\nindex <- sample(1:nrow(DEMO_DF),sampleSize)\ngroup <- as.integer(1:nrow(DEMO_DF) %in% index)\n\nrownames(DEMO_DF) <- DEMO_DF$CUST_ID\nDEMO_DF.train <- DEMO_DF[group==FALSE,]\nclass(DEMO_DF.train)\ncat(\"\\nTraining data: \")\ndim(DEMO_DF.train)\n\nDEMO_DF.test <- DEMO_DF[group==TRUE,]\nclass(DEMO_DF.test)\ncat(\"\\nTest data: \")\ndim(DEMO_DF.test)\n", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:30+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "text", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "editorHide": false, + "title": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "HTML", + "data": "'ore.frame'\nTraining data: \n" + }, + { + "type": "HTML", + "data": "\n
  1. 2700
  2. 13
\n\n" + }, + { + "type": "HTML", + "data": "'ore.frame'\nTest data: \n" + }, + { + "type": "HTML", + "data": "\n
  1. 1800
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Build a Decision Tree Model for Predicting AFFINITY_CARD

\n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_-359163628", + "id": "20230307-171032_435990745", + "dateCreated": "2022-06-02T22:43:03+0000", + "dateStarted": "2023-03-07T17:40:32+0000", + "dateFinished": "2023-03-07T17:40:32+0000", + "status": "FINISHED", + "$$hashKey": "object:51" + }, + { + "text": "%r\n\n?ore.odmDT", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:32+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "md", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "title": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "TEXT", + "data": "ore.odmDT package:OREdm R Documentation\n\nIn-Database Decision Tree Models\n\nDescription:\n\n Fits a classification tree using Oracle Data Mining.\n\nUsage:\n\n ore.odmDT(formula, \n data, \n auto.data.prep = TRUE,\n cost.matrix = NULL,\n impurity.metric = \"gini\",\n max.depth = 7, \n min.rec.split = 20,\n min.pct.split = 0.1,\n min.rec.node = 10,\n min.pct.node = 0.05,\n na.action = na.pass,\n odm.settings = NULL)\n \n \n ## S3 method for class 'ore.odmDT'\n predict(object,\n newdata, \n supplemental.cols = NULL,\n type = c(\"class\",\"raw\"),\n na.action = na.pass,\n bestN = NULL,...)\n \nArguments:\n\n formula: An object of class ‘formula’ (or one that can be coerced to\n that class): a symbolic description of the model to be\n fitted. The details of model specification are given under\n 'Details'.\n\n data: An ‘ore.frame’ object used for model building.\n\nauto.data.prep: A logical value that specifies whether Oracle Data\n Mining should invoke automatic data preparation for the\n build.\n\ncost.matrix: An optional numerical square matrix that specifies the\n costs for incorrectly predicting the target values.\n Specifying the row and column names of the matrix is\n required. The values of the names are possible target values:\n the row names represent actual target values and the column\n names represent predicted target values. The vectors of the\n row and column names must be the same. In general, the\n diagonal entries of the matrix are zeros. The default value\n is ‘NULL’.\n\nimpurity.metric: Tree impurity metric \"gini\" or \"entropy\". The default\n value is \"gini\".\n\nmax.depth: The maximum depth of the tree, from root to leaf inclusive.\n A value in the range of 2 to 20. The default value is 7.\n\nmin.rec.split: The minimum number of cases required in a node for a\n further split to be possible. The value must be positive. The\n default value is 20.\n\nmin.pct.split: The minimum number of cases required in a node for a\n further split to be possible. The value is expressed as a\n percentage of all rows in the training data, and must be in\n the range of 0 to 20. The default value is 0.1 (0.1 percent).\n\nmin.rec.node: The optional minimum number of cases required in a child\n node. The value must be positive. The default value is 10.\n\nmin.pct.node: The optional minimum number of cases required in a child\n node. The value is expressed as a percentage of the rows in\n the training data, and must be in the range of 0 to 10. The\n default value is 0.05 (0.05 percent).\n\nna.action: A function to use for handling missing values, either\n ‘na.pass’ to allow missing values or ‘na.omit’ to remove rows\n with missing values. The default value is ‘na.pass’.\n\nodm.settings: Same as ‘odm.settings’ in ‘ore.odmKMeans’.\n\n object: An object of type ‘ore.odmDT’.\n\n newdata: The data used for scoring.\n\nsupplemental.cols: The columns from ‘newdata’ to include as the columns\n in the ‘ore.frame’ prediction result.\n\n type: If set to ‘\"raw\"’, provides probability for each class\n returned. If set to ‘\"class\"’, the class with the maximum\n probability is returned. The default value is\n ‘c(\"class\",\"raw\")’.\n\n bestN: A positive integer that restricts the returned target classes\n to the specified number of those that have the highest\n probability.\n\n ...: Additional arguments affecting the predictions produced.\n\nDetails:\n\n The Decision Tree algorithm can be used for both binary and\n multiclass classification problems. The tree structure, created\n in the model build, is used for a series of simple tests. Each\n test is based on a single predictor. It is a membership test:\n either IN or NOT IN a list of values (categorical predictor); or\n LESS THAN or EQUAL TO some value (numeric predictor).\n\n The ‘formula’ specification has the form ‘response ~ terms’ where\n ‘response’ is the numeric or character response vector and ‘terms’\n is a series of terms, for example, the column names, to include in\n the model. Multiple terms are specified using ‘+’ between column\n names. Use ‘response ~ .’ if all columns in ‘data’ should be used\n for model building. Functions can be applied to ‘response’ and\n ‘terms’ to realize transformations. To exclude columns, use ‘-’\n before each column name to exclude.\n\n The function ‘predict’ computes predictions based on the input\n data and model. Results are specified in the section on Value.\n\nValue:\n\n The function ‘ore.odmDT’ returns an object of class ‘ore.odmDT’,\n which includes the following components:\n\n name: The name of the in-database model.\n\nsettings: A ‘data.frame’ of settings used to build the model.\n\nattributes: A ‘data.frame’ of attributes used to build the model. The\n columns include: name, type (numerical or categorical), data\n type, data length (size), precision and scale for numeric\n data, and whether the variable is the target.\n\ndistributions: The target class distributions at each tree node.\n\n nodes: The node summary information. See ‘summary.ore.odmDT’.\n\n formula: The ‘formula’ used for the model fitted.\n\n call: The matched call.\n The function ‘predict’ returns an ‘ore.frame’ with columns\n according to the ‘type’ and ‘supplemental.cols’ parameters. If\n ‘type’ is ‘\"class\"’, the result includes the most likely target\n class and its probability. If ‘type’ is ‘\"raw\"’, the result\n includes one column for each target class and the column values\n reflect the probability for that class. Both can be specified\n together. If ‘supplemental.cols’ are specified, the named columns\n are included in the result.\n\nAuthor(s):\n\n Oracle\n\n Maintainer: Oracle \n\nReferences:\n\n Oracle R Enterprise\n\n Oracle Data Mining Concepts\n\n Oracle Data Mining User's Guide\n\nSee Also:\n\n ‘ore.odmSVM’, ‘ore.odmGLM’, ‘ore.odmNB’, ‘partitions’\n\nExamples:\n\n m <- mtcars\n m$gear <- as.factor(m$gear)\n m$cyl <- as.factor(m$cyl)\n m$vs <- as.factor(m$vs)\n m$ID <- 1:nrow(m)\n MTCARS <- ore.push(m)\n row.names(MTCARS) <- MTCARS\n \n dt.mod <- ore.odmDT(gear ~ ., MTCARS)\n summary(dt.mod)\n \n dt.res <- predict (dt.mod, MTCARS,\"gear\")\n with(dt.res, table(gear,PREDICTION)) # generate confusion matrix\n " + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_-1978076810", + "id": "20230307-171032_1422638410", + "dateCreated": "2023-02-01T19:06:52+0000", + "dateStarted": "2023-03-07T17:40:33+0000", + "dateFinished": "2023-03-07T17:40:33+0000", + "status": "FINISHED", + "$$hashKey": "object:52", + "title": "Check the help for the ore.odmDT fuction" + }, + { + "title": "Build a Decision Tree Model using default settings and run a prediction into RES", + "text": "%r\n\nore.exec(\"BEGIN DBMS_DATA_MINING.DROP_MODEL(model_name=> 'DT_CLASSIFICATION_MODEL'); EXCEPTION WHEN others THEN null; END;\")\n\nMOD <- ore.odmDT(AFFINITY_CARD~., DEMO_DF.train, odm.settings=list(model_name=\"DT_CLASSIFICATION_MODEL\")) \n\nRES <- predict(MOD, DEMO_DF.test, \n type= c(\"raw\",\"class\"), norm.votes=TRUE, cache.model=TRUE, \n supplemental.cols=c(\"CUST_ID\", \"AFFINITY_CARD\", \"EDUCATION\", \"HOUSEHOLD_SIZE\", \"OCCUPATION\", \"YRS_RESIDENCE\")) \n", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:33+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "text", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "title": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "TEXT", + "data": "Warning message:\n“The created mining model DT_CLASSIFICATION_MODEL is explicitly named. ORE does not manage this mining model's life cycle.”\n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_1891078928", + "id": "20230307-171032_682145235", + "dateCreated": "2022-06-02T22:43:14+0000", + "dateStarted": "2023-03-07T17:40:33+0000", + "dateFinished": "2023-03-07T17:40:35+0000", + "status": "FINISHED", + "$$hashKey": "object:53" + }, + { + "title": "Confusion Matrix", + "text": "%r\n\nCMATRIX <- with(RES, table(AFFINITY_CARD,PREDICTION)) \nCMATRIX\n", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:35+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "text", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "title": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "TEXT", + "data": " PREDICTION\nAFFINITY_CARD 0 1\n 0 1290 61\n 1 257 192" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_1344652354", + "id": "20230307-171032_615891908", + "dateCreated": "2022-06-02T22:42:48+0000", + "dateStarted": "2023-03-07T17:40:36+0000", + "dateFinished": "2023-03-07T17:40:36+0000", + "status": "FINISHED", + "$$hashKey": "object:54" + }, + { + "title": "Show model accuracy", + "text": "%r\n\nACCURACY <- CMATRIX/sum(CMATRIX)\nround(sum(diag(ACCURACY)),3)\n", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:36+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "text", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "title": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "HTML", + "data": "0.823" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_-1334894842", + "id": "20230307-171032_1488180723", + "dateCreated": "2022-06-02T22:44:37+0000", + "dateStarted": "2023-03-07T17:40:37+0000", + "dateFinished": "2023-03-07T17:40:37+0000", + "status": "FINISHED", + "$$hashKey": "object:55" + }, + { + "text": "%md\n\n### Examples of possible setting overrides for Decision Tree \n\nIf the user does not override the default settings, then relevant settings are determined by the algorithm\n\nA complete list of settings can be found in the Documentation link:\n\n-- Algorithm Settings: Decision Tree \n\n-- Shared Settings: All algorithms\n\n-- Specify a row weight column \n 'ODMS_ROW_WEIGHT_COLUMN_NAME' : ''\n \n-- Specify a missing value treatment method. The default is to replace with mean (numeric features), mode (categorical features) or delete the row\n 'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'\n\n-- Specify Tree impurity metric for Decision Tree. \n Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini `TREE_IMPURITY_GINI` or entropy `TREE_IMPURITY_ENTROPY` as the purity metric. By default, the algorithm uses `TREE_IMPURITY_GINI`.\n 'TREE_IMPURITY_METRIC' : 'TREE_IMPURITY_GINI'\n \n-- Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).\n For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.\n 'TREE_TERM_MAX_DEPTH' : '7'\n \n-- Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.\n It requires a number between 0 and 10. The default is 0.05, indicating 0.05%. \n 'TREE_TERM_MINPCT_NODE' : '0.05'\n \n-- Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.\n It requires a number greater than 0, and smaller or equal to 20. The default is 0.1, indicating 0.1%. \n 'TREE_TERM_MINPCT_SPLIT' : '0.1'\n\n-- Specify The minimum number of rows in a node.\n It requires a number greater than or equal to zero. The default is 10. \n 'TREE_TERM_MINREC_NODE' : '10'\n \n-- Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value. \n No split is attempted if the number of records is below this value. It requires a number greater than 1. The default is 20. \n 'TREE_TERM_MINREC_SPLIT' : '20'\n \n-- Specify the maximum number of bins for each attribute.\n For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.\n 'CLAS_MAX_SUP_BINS' : '32'", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:40:37+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "md", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/markdown", + "fontSize": 9, + "editorHide": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "HTML", + "data": "

Examples of possible setting overrides for Decision Tree

\n

If the user does not override the default settings, then relevant settings are determined by the algorithm

\n

A complete list of settings can be found in the Documentation link:

\n

– Algorithm Settings: Decision Tree

\n

– Shared Settings: All algorithms

\n

– Specify a row weight column

\n
'ODMS_ROW_WEIGHT_COLUMN_NAME' : '<row_weight_column_name>'\n
\n

– Specify a missing value treatment method. The default is to replace with mean (numeric features), mode (categorical features) or delete the row

\n
'ODMS_MISSING_VALUE_TREATMENT' : 'ODMS_MISSING_VALUE_DELETE_ROW'\n
\n

– Specify Tree impurity metric for Decision Tree.\n
Tree algorithms seek the best test question for splitting data at each node. The best splitter and split values are those that result in the largest increase in target value homogeneity (purity) for the entities in the node. Purity is by a metric. Decision trees can use either Gini TREE_IMPURITY_GINI or entropy TREE_IMPURITY_ENTROPY as the purity metric. By default, the algorithm uses TREE_IMPURITY_GINI.

\n
'TREE_IMPURITY_METRIC' : 'TREE_IMPURITY_GINI'\n
\n

– Specify the criteria for splits regarding the maximum tree depth (the maximum number of nodes between the root and any leaf node, including the leaf node).\n
For Decision Tree, it requires a number between 2 and 20, and the default is 7. For Random Forest it is a number between 2 and 100, and the default is 16.

\n
'TREE_TERM_MAX_DEPTH' : '7'\n
\n

– Specify the minimum number of training rows in a node expressed as a percentage of the rows in the training data.\n
It requires a number between 0 and 10. The default is 0.05, indicating 0.05%.

\n
'TREE_TERM_MINPCT_NODE' : '0.05'\n
\n

– Specifyt he minimum number of rows required to consider splitting a node expressed as a percentage of the training rows.\n
It requires a number greater than 0, and smaller or equal to 20. The default is 0.1, indicating 0.1%.

\n
'TREE_TERM_MINPCT_SPLIT' : '0.1'\n
\n

– Specify The minimum number of rows in a node.\n
It requires a number greater than or equal to zero. The default is 10.

\n
'TREE_TERM_MINREC_NODE' : '10'\n
\n

– Specify the criteria for splits regarding the minimum number of records in a parent node expressed as a value.\n
No split is attempted if the number of records is below this value. It requires a number greater than 1. The default is 20.

\n
'TREE_TERM_MINREC_SPLIT' : '20'\n
\n

– Specify the maximum number of bins for each attribute.\n
For Decision Tree it requires a number between 2 and 2,147,483,647, with the default value of 32. For Random Forest it requires a number between 2 and 254, with the default value of 32.

\n
'CLAS_MAX_SUP_BINS' : '32'\n
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"20230307-171032_1013986191", + "dateCreated": "2022-06-27T20:27:13+0000", + "dateStarted": "2023-03-07T17:41:16+0000", + "dateFinished": "2023-03-07T17:41:16+0000", + "status": "FINISHED", + "$$hashKey": "object:57" + }, + { + "text": "%md\n## Display ROC Curve, Lift Chart, and Distribution Chart", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:35:39+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "md", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/markdown", + "fontSize": 9, + "editorHide": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "HTML", + "data": "

Display ROC Curve, Lift Chart, and Distribution Chart

\n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_-13298935", + "id": "20230307-171032_919698470", + "dateCreated": "2022-06-02T22:49:25+0000", + "dateStarted": "2023-03-07T17:35:39+0000", + "dateFinished": "2023-03-07T17:35:39+0000", + "status": "FINISHED", + "$$hashKey": "object:58" + }, + { + "text": "%r\n\n# BAR PLOT\nres <- ore.pull(RES)\nsensitivity <- res[order(res$\"'1'\",decreasing = TRUE), ]\nsens <- sum(sensitivity$\"'0'\")/sum(sensitivity$\"'0'\") - cumsum(sensitivity$\"'0'\")/sum(sensitivity$\"'0'\")\nspec <- cumsum(sensitivity$\"'1'\")/sum(sensitivity$\"'1'\")\n\n# LIFT CHART\ndecile2 <- quantile(sensitivity$\"'1'\", probs = seq(.1, .9, by = .1))\ndf_sens <- as.data.frame(sensitivity$\"'1'\", col.names = c(\"sens\"))\ndf_sens$decile = as.numeric(cut(1-cumsum(df_sens$sens), breaks=10))\n\n\n# DISTRIBUTION CHART\ndx <- density(res$\"'0'\")\ndx2 <- density(res$\"'1'\")\n\n# PLOTS 3x1\npar(mfrow=c(3,3))\nplot(1 - spec, sens, type = \"l\", col = \"darkred\", ylab = \"Sensitivity\", xlab = \"1 - Specificity\", main = 'ROC Curve')\nabline(c(0,0),c(1,1))\npaste(\"AUC: \", round(sum(spec*diff(c(0, 1 - sens))),3)) \n\nbarplot(table(df_sens$decile), xlab = 'Decile', ylab = 'Actual Targets', main = 'Lift Chart', col = \"darkred\")\n\nplot(dx, lwd = 2, col = \"burlywood\",\n main = \"Density\")\nlines(dx2, lwd = 2, col = \"darkred\")\n# Add the data-poins with noise in the X-axis\nrug(jitter(res$\"'0'\"),col='burlywood')\nrug(jitter(res$\"'1'\"),col='darkred')\n", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:35:39+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "text", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "editorHide": false, + "results": { + "1": { + "graph": { + "mode": "table", + "height": 829, + "optionOpen": false + } + } + }, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "HTML", + "data": "'AUC: 0.852'Warning message in rug(jitter(res$\"'1'\"), col = \"darkred\"):\n“some values will be clipped”\n\n" + }, + { + "type": "IMG", + "data": 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/5bI9QasNlsqnpRDbt379b+o0uvXk9OnACA4gcPjBRZa6WUijRqBdXWQ2IndARI\nBQBlBSZ2DdPyyjRIX7zI/PPP5zExz2NjRenpQBUoefVVal+vNn5+LbVASWMVFxfPnTv3/Pnz\nvr6+kZGR1EZbLdW5c+cAYO7cuT/88APTsSCEGODUsyfVkBQVSQoKBK7NGiJE9VBWFNLt5id2\nFlaOVEMlKSVJDUGY2eRvk0g4GjJlviGTVY2s/PnzewcOPI+JKUhIoAqU2Hfu3Pu999oHBHj6\n+/NsbRmMzQT99ddfs2bNysnJCQ4O/uabb7TXE7RgmNUh1Go5Vyd2AFD04IEXJnYGQ4/DsvnW\nLC6/mVfjCqoSO5LUqCSlXKFjMy9oZCaR2FHs7OwAoKysTOerDZmsakyiZ88ihw8XZ2dXFSgZ\nPdorIMDGy8v4kZg+jUazffv2tWvXWlpaHjlyZHYrm0QcGRl56NChZ8+epaWlAcDmzZsXLVrk\n0opnWCLUSlh7eFjY2CjKywGg+OFDr9GjmY6oxVJUFFMNC6FT86/GFf477VtZWYKJXdNpzzGv\nrYGTVY1DnJV1ctSoiry8cRERXWfNalUFShqroKBgzpw5f/zxR/fu3aOiorp37850REb13Xff\nrV279oMPPoiNjaWOODg4fPbZZ3v27GE2MISQwRGEU48eudevA0BVPQRkGMrK6sTOSg+JHYvL\nZ1sI1AoJACgrS5p/QSOrM7GbNGnS/Pnzx44dyzZWvewNGzbU82oDJ6saQWV+/slRo8ozMwMP\nH+4WHGzku5uX2NjY4ODg/Pz84ODgAwcOWLa+qi67du06derUsGHDQkNDqSPjxo0LCwvDxA6h\n1sCxWzdM7AyNJDV0+sXVR2IHAFyhY1ViJzG/xK7OriYulzt58mRPT89Vq1alpKQYMyZTJi0u\n/mn06NKnT0d//XX3t99mOhzTpVKpNmzYMGbMGKlUGhUVFRER0QqzOgBIT09/9dVXAYBeG2Rv\nb19SYn6/KRBCTeBUXZeYqnuCDEElKSM1aqqtt8ROYE81lJJSvVzQmOpM7KKionJzc1evXv37\n77/7+voOHTr0+++/r6ys1Mtd4+Pjg4ODfXx8rKyshEKhj49PcHBwfHy8Xi5uOBql8vT48cWP\nHvnv3Nlr8WKmwzFdz58/Hz58+MaNG/v375+QkDB16lSmI2KMu7s7VYibTuzOnz/fsWNHRoNC\nCBmJQ3WJK1lJCbXVENI7pVa1OQs9zYfjVE+za1GJHQA4ODgsXbr07t27CR3Rg5kAACAASURB\nVAkJvXv3Xrx4sZub28KFCx8+fNicW549e9bPzy85OXnKlCkbN24MCwubMmXKkydP/Pz8oqOj\nm3NlQ4v77LO8mzcHrFzZ76OPmI7FdJ05c6ZPnz43btwICQn5559/OnTowHRETFqyZMm8efNi\nY2MJgnj06NHOnTuXLFny/vvvMx0XQsgYHLp0odsvnjxhMJIWjC4jzOYJm78klsK1tKMaapmY\nVCv1ck2jefniidzc3N9///2PP/5gsVgTJ07MzMzs1avXtm3bPvnkk6bdcs2aNdu2bfuoVm60\na9eu1atX65xIZwoKExPjtmxx7tVrsPlXUTYQmUy2cuXK8PBwZ2fn8+fPjx07lumImPfJJ5+I\nxeKJEyeq1eoePXpYWlquWLHivffeYzouhJAx2LRrx7G0VEmlAFDy5Inn8OFMR9QC0T12ely+\nSg/FAoBSUmZeO8bW2WOnVCpPnz49fvz4du3aRUZGLl26NC8v7+jRo3/88UdMTExYWFiTb5ma\nmjp37tzax+fNm5eamtrkyxqUWqG4+PbbADD2hx/YFhZMh2OKnjx5MnDgwPDwcH9//8TERMzq\nKARBbNy48cWLFwkJCXfu3CkuLq5/kRBCyMgMOjWIYLHsO3em2iXYY2cY/66cEOptd0qOwI5u\nq6RmNhpbZ49d27Zt5XL5jBkzbty4Qc3+po0cObI51WXbtWsXHR09Z86cGsejo6O9TLUOXNzm\nzUX37w/euNGlRW+W0GQRERHvvfeeXC4PDQ1dv359QypOtyp8Pr9Pnz5MR4EQquns2bNBQUF9\n+vSZMmWKs7MzQRCFhYWXLl3y8/M7ffq0XkaQHHx9i+7dA4BSXIZoGIbosWNxeP9WPJHoLq9r\nsupM7ObNmxcaGioUCrUPPnnypEuXLgCQnZ3d5FuuW7duwYIFZ86c8ff3d3Z2BoCioqIrV65E\nR0cfOnSoyZc1nIL4+Juff+7at+/A1auZjsXkiMXiJUuWHDt2zNPT8/jx40OGDGE6ItOycOHC\n2gd5PF6HDh2mTp3q6elp5Hjq2cFFo9EYORiEGGeEqUEOvr5UowQTOwNQK6QaZdUvNK5Abz12\nAMAR2FOJncrc1k/Umdht375927ZtNQ527dqVJMlm3nLOnDkuLi47duxYs2YNtcxWKBRSKycC\nAwObeXG9U8vlF+fOJViswO+/Z3G5TIdjWhISEqZPn56amjphwoTvv//ewUGfD1XLUFxc/Ouv\nv3p6evbq1YsgiMTExKysrDFjxsTGxq5fv/7q1av9+vUzZjz17OBCrd5FqFWpZ2rQaj19k7fv\n1IlqiNLT1QoFTubRL6Xk3yWx+t0igmtpJy/LAQClVKTHyxpBI3aeqKioqNGB12SBgYGBgYEk\nSYrFYgCwtrami0GYmusbNxY/fDhk82bnV15hOhYTQpJkeHj4ypUrAWDPnj0hISEm+3+QWc7O\nzsuXL9+6dStV6FutVq9YsUIsFl+8eHH58uXLly+/fPmyMeOpZweX1rYpCEJglKlB9tU9dqRa\nLXr2THudLGo+VWVVdxrBYnMsbfR4ZXqaXUvosaO39tLe40uj0SQmJvbt21eP9yYIwsZGn/8b\n9C7/9u3b27e79e8/YOVKpmMxISKR6J133vnpp586d+4cGRmJs8fqcf78+YcPH9Lbt7DZ7DVr\n1rzyyisEQXz66afdqouXGk09O7jgzEhksgy3E5IRpgbRPXYAUJqaiomdftE7Q3As7QhCn7/E\n6IonKlk5kCSYT+eFjsTu6dOnNRoAwOVy+/Xr9+GHHxopLhOgkskuzp1LsNmBP/zA4pjQprrM\nunnz5owZM9LT0ydPnnzw4EFbW1umIzJpYrE4MzNTe5A6MzOzvLwcAAiCsLa2Zi40hMwGtROS\ng4PDnDlz5s+f37l6nWnzGWFqEN/eXuDsLCkqAlw/YQB0AWHtAiV6wbGs+nQjNWqVXMzhm3Q/\nlDYd+cq5c+cAYO7cuT/88IOxwzElNzZtepGUNPTzz51wiAoAqodfP/30Uw6Hs2fPnlaV5TdZ\nUFDQ1KlTt2zZ0r9/f5Ik4+PjV69ePXnyZAA4efLkpEmTmA4QITMQFRVVUlJy7Nixw4cPf/HF\nF0OGDJk/f/7UqVP1MjvICFOD7Dp1qkrstLpLkF4oq4di9VjrhPLfiici807sKK08qxM9e3Zn\n1y7Xvn0HfPop07GYhMLCwjlz5vz+++/dunWLiorq0aMH0xGZh2+++WbFihWzZ89WKBQAYGFh\n8c4771DLkrp3765zzSxCqDZqJyRqM6TDhw8vXrw4JCRk2rRpy5Yt08uvo0ZNDapndblO9h07\n5l6/DgBlmNjpG11kTv89djxrgsWmdqFVScvA3thFDJqsZmL3wQcfAMBXX31FNWr76quvDB6U\nCfhrxQq1XO6/ezeh7ykd5ujSpUvBwcF5eXnBwcH79+8XCARMR2Q2BALBV199tW3btmfPnhEE\n4e3tTf/0Ro4cyWxsCJkdve+EVBc7OzsAKCvTXcCsntXlEomk9kF6ml2pqRbhN1NqeYVGpaDa\nHH0ndkAQHL4NNdRrXqXsaiZ2dIG65lSqM3fZV6+m/Pxz56Agj2HDmI6FYSqVavPmzWFhYVZW\nVidOnJg+fTrTEZkZf3//y5cvCwQC7ONEqMmUSmV0dPThw4d/++23Hj16LF26dPbs2VTu9eef\nf06aNEnviZ322sHa6lldrvN7r13HjlRDnJWllsvZPJ4eQ23NlFrrVfXeYwcAHEs76hYqs6p4\nUjOx++WXX2o0WhtSo7n88cdsHm94rTJ+rU1mZubMmTOvXbvWv3//yMhIHx8fpiMyP/Hx8RUV\nFc3ZqQUhZLidkOpS/9Z/9awu13k+3WNHajRlz545du3a7AARgFZiR7DYhpgDxxHYwgsAc0vs\n6lwbPHHixJ9++qnGN5LW4FFEREF8fN+QENsOHZiOhUm//PJLnz59rl+/HhIScu3aNczqmmbs\n2LEnTpxgOgqEzNu8efPy8vIOHDigndU9qd561fTHl+geO8Bpdnqlqvy31okhypFw6IonZpXY\n1bl4QigUzp07l8vlBgUFzZ49e/jw4a2hzJWyouKftWsFLi5+a9cyHQtjZDLZypUrw8PDnZyc\nzp07N27cOKYjMmPW1tbvvvvuzz//3K1bNwutivNbt25lMCqEzIvhdkICgOzs7NjYWFtb2zFj\nxmgPpG7YsKH+fruG49na/lvxBKfZ6Y9SWjX1zRDjsADA5VdVPFHJxaRGTbDMY859nYnd8ePH\nKysrz5w5c+zYsddff93NzW3mzJmzZ8/u2bOnMeMzslvbtlXk5gbs389rreXZkpOTp0+fnpiY\nOGLEiGPHjrVt25bpiMxbRkbGyJEjVSrV/fv3mY4FoZZDXzsh3blzJyAgoKKiQq1We3p6njp1\niu4U3Lhxo74SOwCw9fGhEjvssdMjeihW/ysnqMtWl7IDklTJxFytAiimrL66u0KhcPbs2bNn\nzy4sLIyKitq/f/+2bdv08g3JNImzsm7v2OHUs+crrbUIRURExPvvvy+VSkNDQ9etW6f3Iu+t\nUGxsLNMhIGTGDL0T0tq1a6dOnRoeHl5aWvrhhx+OGjXqwoULQ4YMaf6Va7Dz8cmLiwOAsrQ0\nvV+81VJJ6B47g6Rc/yZ2ACppWUtI7CgSieTSpUu///57SkqKh4eHEWJiytVVq1RSqf/Ona2w\nxIlUKv3www+/++47Dw+P8+fPD2v1y4ERQqbA0Dsh3b59+9ixYzwez83NLSoqas2aNWPHjr1w\n4cLQoUObf3Ft9tXT7LBGsb6oFRKNSk61DTQUy+YJCTaHVKvArKbZ1ZnYqVSq2NjYY8eO/fLL\nL2w2Oygo6Pfffx8xYoQRY/uPxhaEbKy8uLjHJ074jB/vFRCgr2uai0ePHk2bNu3Ro0cBAQFH\njhxxdXVlOqIWRaVSJSUlZWZmqlQq+uCbb77JYEgImQtD74TE5XLVajX9x88++4zL5Y4bN+7i\nxYv6vZFd9fozcWamRqlkcbn6vX4rpNKqdWKgoVgA4PBtlZUvAEAlM//Ezt3dvaysbOzYsd9/\n//348eP5fL4xw6qtsQUhG4ckL3/8MYvDGb5jR3MvZW4iIiKWLFmiUChCQ0PXr1/fGpbIGFNq\nauqbb76ZnJysVqu5XK5SqQQAoVBYUVHBdGgImQ3D7YQ0YsSIq1evTpkyhT6yceNGFos1duxY\n/d6ITuw0KlX58+fa62RR0/xb64RgaY+Z6hfHsjqxk5Yb6BZ6V2diFxYWNmXKFHt7Q2XBjdXY\ngpCN8iQyMvfGjb4hIQ6+vs28lBkRiUTvvvtuVFSUl5dXZGSkn58f0xG1QMuWLRs+fHhCQgKf\nz5fJZImJiQsWLJg7dy7TcSFkBoywE9KKFStCQ0O1EzsACA0N5XA4YWFhzby4NjutilFlaWmY\n2DUfvRsEx9KGIAzVJcGxrCqP1xKGYhctWmTMOF6qsQUhG04llV5dtYrv4DAoNLSZlzIjt2/f\nnj59+rNnz4KCgg4ePEjVcEd6FxcX9/333/N4PABQq9V9+/b98ccf33rrLb1MD0KoZTPCTkj9\n+vWjRntrWLt27Vq9Fr0SuLpaWFsrxGIAKH36tP2YMXq8eOtE7xLLsTRgD5Q5lrLDvWLhzs6d\n5ZmZI/fu5Ts4MB2LMZAkGR4e/umnn7LZ7D179mCGYVAlJSUuLi4A4OTklJ+f7+np6ePjk5OT\nw3RcCJmBFrYTkp2PT2FiIuDCWD2hh2INuliV3tBCJReTpMZwXYN6VDPE7Oxs6rtRdh2YCNKA\nKvPybn7xhYOvb+8lS5iOxRiKioreeOONZcuWdezY8ebNm5jVGc2AAQO2bt369OnTLVu2dNTT\nKEx8fHxwcLCPj4+VlZVQKPTx8QkODo6Pj9fLxREyHWq1OjExkWonJSUtX7583759Zld7ix6N\nxcROL+haJ4ZbOQEAXK1SdmqZeUyzq3Ov2K1bt3bp0qXGq/QWLi3G32vXKisqhu/Y0RrWKF2+\nfHn27Nm5ubnBwcH79u3TS3lPVL93332XamzdunX8+PHffPONvb39yZMnm3/ls2fPBgUF9enT\nZ8qUKc7OzgRBFBYWXrp0yc/P7/Tp0zrnLSBkpnbu3FlWVta7d2+pVBoQEODu7n706NGioqL1\n69czHVoj0PPqsEZx82lUcrWiat2kYXvs/lPKTkSPzJqyOufY6dytRV9buJiIgoSERz/+6BUQ\n4DN+PNOxGJZKpdq8efPmzZsFAsGxY8dmzpzJdEStxf79+6lGz549MzIycnJyXF1dufr4FrFm\nzZpt27Z99NFHNY7v2rVr9erVmNihluTbb7+9dOkSAFy6dMnJyenWrVsJCQmTJ082r8SOLmUn\nevaM1GgIkylB8CIpSZSe3sbPz9LRUecJKmmZoqLYwtqVw7c2cmx1MU6tEwBg86wIFofUqABA\nKRUxXB+kYRrxD0tfW7iYjisffwwE4b9zJ9OBGFZWVtbIkSM3btzYu3fvhIQEzOqMY/HixTWO\nEATh4eGhl6wOAFJTU3Wurp03b14qbkaJWpbc3FxnZ2cA+PPPP6kvLV27ds3Pz2c6rsaxrR6K\nVclkFaYx0VatUJyfPfv77t1Pjx//rZfXo4iI2ueUJP+Z9feBgoRT2Vf3idLjjB+kTvSSWADg\nGrgXzewWxurosTP0Fi4mIvX06ay//ur17rtOLXr327Nnz86bN6+0tDQkJGT79u3a+9Ajgzpw\n4ADdXWcI7dq1i46OnjNnTo3j0dHRXl5ehrsvQsbn6+t7+PDhCRMmREVFnThxAgBSUlJ8za06\nVY2KJ9aengwGQ7ny8cePjx2j2srKyt/mz7dp185TaycCUXqcKOMW1SZJTUnKFTbf2qpNd+OH\nWgO9coLNtybYhp1JxeHbKitLwKwTO0Nv4WIK1HL5XytW8GxtB2/axHQshiKXy1esWPHll186\nOjqePXt2fEsfbm5t1q1bt2DBgjNnzvj7+1OdGUVFRVeuXImOjj506BDT0SGkT1u2bJk8efLS\npUvHjx9Pbfb19ddfv/fee0zH1TjWHh4cPl8lkwFAWVqaJ3M7OVHyb9+++8032kdItfqPd9+d\n9/AhNelcJRWVPv2nxrtKnsQKnDuyODzjBaqLSlq9S6zhJ73R0+xUZrp4Agy/hYspSAgPL0tL\nG75tm8DFhelYDCIjI2P69Ok3b94cNmzY8ePH3d3dmY4I6dmcOXNcXFx27NixZs2ayspKABAK\nhX5+ftHR0YGBgUxHh5A+jRs3rrCwsLCw0NvbmyAIAJg/f/6rr77KdFyNQ7BYtt7eLx4/BtNY\nGHsjLAxIEgC4QmGvxYvv7NwJAKUpKU+iorrNng0Aooyb1NwyIAg779fKnl0HALVCWp6ZYNfh\nNSZD/0+tE4Nvo/BvYme+PXaUFpzVSYqK4rZsse3QoW9ICNOxGMSpU6feeecdkUgUEhKyY8cO\nfU3qQo1Vz0Z8NfZQaZrAwMDAwECSJMViMQBYW1tTn3kItTxWVlZWVlb0H5ndKafJe5fbdepU\nldgxvTC2/PnzZ+fPU+1+y5YN3rgx/cIFKra7X37ZbfZsjUpRkfOQOsGqTXf7TsPkolzpiwwA\nEGfdtfP2A0Z/29CLJwy6cqLqFtWJnVpmHqXsWmOB4mvr18tFojGHDrF5DHcm651UKl21alV4\neLirq+tvv/32+uuvMx1Rq7bDKFsPEwRhY2NjhBshxBSVSvXjjz9eu3atpKRE+zhThYubvHc5\nPc2ulOkVTo9+/JHUaACAxeX2+eADgs3u++GHMYsXA0DerVvFjx7xbFUatYI62bb9AACw8epP\nJXYqWbm0JMPS0Zup4Em1SiUTU21j9tiRpEYtExtuX1p9qZnYGWELF2YVP3x4/7vvPIYN6xwU\nxHQsepaUlDR9+vQHDx6MHj36yJEjbm5uTEfU2tX17Uhf4uPj9+zZc/369YKCApIk3dzcBg0a\ntGzZsn79+hn0vggZ2Ycffnjs2LE33njDw8OD6VgAmrF3OV3xpJTpHrvHJ05QjQ5vvCF0cwOA\nrjNnXvn4Y6VEAgBPTpzoOLFqhQTPto2FtQsACJx82DwrtbwCACrzHjOY2CmlWktiDZ/Ycfn/\nfnNWScvML7FrYVu41Hb544+BJP2bvcOsqYmIiFiyZIlCoQgNDV2/fj3LZCokIQPBAsWo9Th5\n8mRsbGz//v2ZDqRKk/cup2sUKysqKvPzhQx9/S5JTi6p3m6gy7RpVMPC2rrD+PHJJ08CQOrP\nP3sMq8pmhG26Vb2NIIRuXcqf3wEASVEqkCRTo7Eqyb8dt0YYimXzrAgWm9SowUxK2dU5x06t\nVj948KB3794AkJSUdPjwYR8fn8WLFxtiEk9GRkb79u31ftnanp0//zwmpsfcua4tqG5LeXn5\nu+++GxkZ2a5duxMnTgwaNIjpiJAxNK1AcT1zgzQajYFCRaiZWCxW7Z2QzJG91naCZWlpTCV2\nadHRVIPN43V44w36eOegICqxe/HkiTi70MrdCQCELp3pE4QunanETq2Qyspy+PbMdKD+W+vE\nQsjiGL6GF0FwLM2p4kmdiZ0xt3Dx9vY2woYWGqXyyvLlXCurIVu2GPpeRnPnzp3p06enpaW9\n9dZbhw4dsrc3+HcX1EDUggbDqadA8erVq+t6Vz1zgx49eqSv2BDSr4CAgNOnT9eu2mh2bLy8\n2BYWaoUCAMqePnUfPJiRMNIvXKAaniNGWFj/u5mEd2Agi8vVKJUAkH87uaO7k4W1i/bII8/e\ng8Xla5QyAJAWpzGe2HGFRvrIayGJneG2cJk8eXI9B0+dOtXM69clcd++kidPhoSFWbVta6Bb\nGBNJkuHh4StWrGCxWHv27GkxJQZbDO0VfIbQtALF9cwN6t6d+aKjCOnE5/Pnz5//66+/duzY\nUXvUaOvWrQxG1QQEm23r7V2SnAzMrZ9QSiQ5169Tbe8xY7RfsrCxcR88OOvKFQAoiE/t+OZg\ngbOP9gkEwbJ0bF+Z/wQApC8y7DsNN1LQ/6WqNN6S2KobmVXFkzoTO8Nt4fLzzz8PGDCAujhN\nL9Uf6iErKbm+caNNu3b9P/nEoDcyjuLi4rlz554/f75Lly6RkZG9evViOiJkbE0rUFzP3CCc\nl4lMVkZGxogRI0QiUXx8PNOxNJd9p07MJna5166p5XKq7VWrckL7MWOoxK7oYbpGpbZ0bF/j\nBEtHbyqxk5fna1QyFoeBKWfGLGJH4VSXQVZprdswWXUmdobbwmXv3r07d+4MDQ0dN24cdYQg\nCKoqsuFc37RJVlIy6ssvOZaWBr2REVy5cmX27Nk5OTnBwcH79u1rYRv4ogbCAsWo9YiNjWU6\nBL2x69SJajCV2GVevkw1BK6uTt261XjVa9SovwEAQC1VlKXmdRhbc7DV0rF6QIAkZSVZApdO\nhgxWB1KtUsnpWicOxrkpl+6xk1eQGjXBYhvnvk1T53f0LVu2rFixwsvLq2/fvvrdwiUkJOSX\nX375+OOPP/jgA6lU2vwLvlRJcnLiN9+08fPrOmOGEW5nOGq1esOGDaNHjy4vLz9y5EhERARm\nda1ZYGBgbGysWCwWiUQikUgsFsfGxmJWh5Aps9dO7Aw/uby2rL/+ohrtRoyovazVtW9fC6uq\nii2lyUW1MxiOpR2nuvyHrDTLkJHqppSU0j834/fYAUma/sZidfbYGXQLlz59+ty5c+f999/v\n16/f8ePH9XLNevy1fLlGpRq5ezezlbKbKTs7e9asWVevXu3bt29kZGSnTsb+noQa7qUV7PRY\n6JsuUGy01eUIGV9kZOShQ4eePXuWlpYGAJs3b160aJGLGe4J6dC5apGpsqKiIi/PyHO+VVJp\nwZ07VNtj2LDaJxAsllMP79y4RwBQ9DBD50X4Dp4VuY+AscROq9aJERdP0G2VpMxoCWXT1JnY\ngYG3cLGysvrxxx+PHDkyevRoPV62tuexsWnnznWdObMNo1vQNFNMTExwcHBBQcGiRYvCw8N5\nLW7PjBaGkfrexlldjpDxfffdd2vXrv3ggw/oMVkHB4fPPvtsz549zAbWBHZa38lLU1KMnNjl\n3bpFrckFAI+hQ2ufoKgodOzhRSV2hXcfaVQqFqdmnsC3r0rsFOUFpFpJsI26ayW1OhUA2Hxr\nFtvwtU6oe1kIWGwLaisO059mV2diZ5wtXIKDg4cMGXLv3j09XrOGK8uXcywth37+ueFuYVAq\nlWrz5s1hYWHW1tYnT56cMmUK0xGhlzNCfW+mVpcjZHy7du06derUsGHDQkNDqSPjxo0LCwsz\nx8TO2sODY2mpkkoBoDQlxXPECGPePbd6PSzPzs5R10J4WWm2U4+qWXTKysrCxES3WnWh+XZV\nE+9IUiMX5fEd2hksXh3oHjujTbCjcAR2CnEh/HffC9NUZ2JntC1cvL29vb1fvjNJ0zZdlpeV\nFd271y042KadUf/l6cvz589nzJhx48aNAQMGREZGNuQHhVoJRlaXI8SI9PR0aiIQXevE3t6+\nRqdDkxl5az6CxbLv1Kno/n0AoJbHGhOd2LX18yN0LYSXlWXbdXRnW3DVCiUA5F67Vjux41o5\n0dXsZGU5Rk7sVNU9dkYrYkfhWFYldiqJ2SZ2xt/Cxc7ODgDKynT/yJq26bKkqAgArN3d9RGg\nsZ0+fXrBggUikSgkJGTHjh1crlG7u5G+qFSqpKSkzMxMlUpFH3zzzTebeVlGVpcjxAh3d/dH\njx7179+fTuzOnz/fUWsXhyZjZGs+B19fZhI7ksyNi6OabV97Tecp8tIcFodt7+tR/CAdAHKu\nX++rq0gqz7attPgZAMjLcgwWrm6KSmZ67OiFsWbcY2f8LVyWLVtWz6tN23RZWlwMAJZOTvoK\n0jhkMtnKlSvDw8NdXFwuXLiA6xzNV2pq6ptvvpmcnKxWq7lcrlKpBAChUFhRUdHMK4eEhAwd\nOnTGjBkXLlzYvn27pfnX8UGoLkuWLJk3b97u3bsJgnj06NFvv/22adOmz/UxwaZpW/M1k331\n+gkjJ3Zlz55Rn4kAoHPSuUomppZ8OnbzohK73DqGxfh27lWJnSjXUOHqolZINcqqYhpcobGH\nYqmGGffYGX8Llw0bNtTzatM2XZYWFQGA5X+Hq0zckydPpk2bdv/+/ZEjRx49erRNmzZMR4Sa\nbtmyZcOHD09ISODz+TKZLDExccGCBTq3AmsCI68ufymd8yXqwefzx40bx2abdEUoZAo++eQT\nsVg8ceJEtVrdo0cPS0vLFStW6KX8VtO25msmh+pOE1F6ulouZzdsMRypUctKM0mVkmfvwbao\nszujHnnV3XUEi9VmwIDaJ8hFVd1vjl2rRlfFWVnirCxrT88aZ/LsqtZ8qBUSpaSMW530NErZ\n06cFd+9ae3i09fNrYM0KZeULus0VOjbhpk1Gr4TVqORqhZRtYbrfpetM7FrGFi4Sc+uxi4iI\neO+99+RyeWho6Pr163EzAHMXFxf3/fffU6uY1Wp13759f/zxx7feektfW8AZbXV5Q1y8eLGu\n+RJ1iY6OHj9+vIHiQS0GQRAbN25cvXr148ePNRpN165d6xmoaZSmbc3XTA7Vpf5JtbosLc2x\nVpXg2uRlOYX3fqW60wg2x6Gzv027Rk8BzLt1iw6AZ6cjFZOXVXW/ufTtSR/MjYvzrZ3Y2f7b\n4yAX5TY2sdMolZeWLr337bdURbo2AwdO/PlnqwZMmqITO4LF5lo2JZtsMo7W7VTSUrNM7Ay6\nhYvRJqtSPXYCc+ixE4vFixcvPn78uKen54kTJwYztDk00q+SkhKq1JaTk1N+fr6np6ePj09O\njp5npRhhdXlDUPXGhwE05PPwOcDV6rcg1BB8Pr9Pnz76vWbTtuZrJocuXYAgqJym5MmTlyZ2\nCnFhfnyURlVVpoRUq148jgEgbNr1bdR9827epBptBg7UeYKsesKcbXtfOx+fsrQ0AMiLi/Ot\nVY2BxeFzhQ5U5RG5KM+qzctzU22/LViQdOSIdmAnR42aFRenM93UgIZ+hAAAIABJREFUpvx3\n5YSDkQvTcixt6f9rSkkpz9Z0N52vM7Ez3BYuxpysKjGTodj4+Pjp06c/ffp04sSJhw8fdnAw\n6tQBZAQDBgzYunXrRx99dPjwYb1M+q6hgavLjcAL4BWmY0AtiVQqDQ8PP336dHp6OkEQ3t7e\nQUFBS5cu5fP1sEspI1vzWVhbW3t4iLOyAOBFUlKnSZPqOZkkNUUPoumsjlaSfMnSoR3XqqHj\nUWqFojAxkWq76dprgNSoFeUFVJtn697Gz49K7Oj1FjXwbNtQaZaikdPsko4e1c7qKCXJyX8u\nWzb2hx/qf6+ysmqOoJHHYQGAYLE5fBuVVAQmP82uvgLFBmLMyarURFGBCQ/FkiQZHh6+YsUK\ngiD27NkTEhJCmPP2GKiGd999l2ps3bp1/Pjx33zzjb29/cmTJ5mNCiFzoVQqR48eHR8fP3bs\nWH9/fwB48uTJunXroqOj//zzT06t2rlNEBgYGBgYSJKkWCwGAGtrayP8Enbo0qUqsXv8uP4z\nxZkJCnER1bb1epXv2L7g7ikgSVKjfpF8ya3ftAbesej+fbVcTrXddE2wU4gLSU3Vyn2eXdu2\nfn6Pjx0DgMKEBLVCwbaoWQqYZ9uWKlMsFxeQpIYgGjRxSFlZeXXlSqotcHGZeunSnyEh1Pa1\njyIier/3ns7Jf/8GWVE1FGv8xA4AuAJ7KrFTSkqNf/eGq++pMNAWLsacrCotKuJYWnK19s8w\nKS9evJg7d+65c+c6d+4cFRXVu3dvpiNCerZ//36q0bNnz4yMjJycHFdXV6xcg1ADHThwICcn\n5/Hjx9od0mlpaf7+/gcPHly8eLG+bkRvzdcQTaurqs2xW7fnMTEA8CIpqZ7TSI2qLL3qshbW\nzva+/gTBsvUaIMq4CQDS4nRZaTbfvkG1ZvOrJ9ixeTznV3T0qtOFSwg2x8LahV42q5LJiu7d\nq93JR49FkmqVUlxkYePakDASv/mmIreqh2/0N9849egxNiLicNeuyooKIMnrGzYEXbhQ13tJ\ntVIlE1FtCyED/TVcgb30RQaYfGJXZ4r93XffUfUUnj17Rh2htnBp/i2pyaq1jxtisqq0uNhk\nV07cvHmzf//+586dCw4Ojo+Px6yuxSMIwsPDA7M6hBru1KlTn3/+eY1pBj4+Pp9//rlBe77t\n7Ozs6p7vRdVVnfpfVImGeuqqanPs2pVqlCQnk2p1XadV5D5SyyuptkNnf6pXzM5nEItbNQwt\nStc9Tlpb/u3bVMOld+/a3W+gVbiEZ+NGECyXXr041UWUdI7GWli7EKyqJe3y8ryGxKBRKuOr\nNwtxHzKkc1AQAFh7ePT/+GPqYPpvvxU/fFjX25WVL6B610SuFQM9dpzqAisqiX6KYxtInT12\nhtvCxZiTVSVFRSa4coIafv300085HM6ePXv0tUASmaCFCxfqPH7w4EEjR4KQOXr06NHIkSNr\nHx81alTt+Tx6ZIi6qtroBRMqqbTs2TN7rQ1ktZVnJlANC2tXS6cOVJvF4dm061eWdg0AJMVp\nKmkZpwHrQ+nETucEOwCQVS+J5dm6AwCLy3Xt2zfn2jWgVl0sXVrjfILFtrBylpfnA4BclGft\n8fK+iZSff6a76/zWrKGP9w0JubNjh1IiAZK8+/XXAfv26Xy7oqK4+t6EkYvYUeiKJ2qFVKOU\n0em1qakzsTPcFi7GnKwqLS6u64FhSmFhYXBw8B9//NGtW7eoqKgePXowHREyIO1CxBqNJjU1\nNTEx0UBVTxFqeUpLS511fTl3cXEpLTXgcJgh6qpqc9Lap/XFo0c6P6fk5fkKcdVqBhuv/5SM\nsPHsK0qPIzVqIElx9j37TsPrv52youLFkydUW2dip1ZI6L3teXZVpUzaDBxYldjVsX7CwrYN\nndjVHwDl/rffUg0HX19vrY97S0fHrrNnU68+Pn58xM6dXF35MZ3YcQX2BIuBFQLae10oJSUm\nuzC2zh+N4bZwAWNNVlXJZAqx2KSWxMbGxgYHB+fn5wcHB+/fv19fpZiQyYqMjKxxJCwsrKio\niJFgEDI7arVaZzlPFoulvUef2eE7OFi1bUt1XxU/fNhR1x6DFTkPqAaLw7Ny66r9EpsnFLh0\nqsx/AgAVuQ/tOw6rv/ZHQUICPeCrM7HT3kCCzlfoaXZlaWk6h794tm3EWXcBQFlRTKqVBLu+\neSaijIzMK1eodq93360R8CvvvEMldory8qdnznSdNav2FZQVVb85LayY+VjnCOwIgkWSGgBQ\nVppuYlfnHDtqC5fY2FhqC5edO3cuWbLk/fff1+O9qcmqNjY2BlqCZFJF7FQq1YYNG8aMGSOR\nSCIjIyMiIjCra52WLl165swZpqNAyGyMqIOh71vXPAp9cepZVQRY56wyktRU5lctmBW6damd\nM1m7Vy2AUMnE0pLn9d+LHoe1sLGhyyNro1dOcPjWHH7VIpK2WtuO6ey0o8sUk6RGXl0qpS6P\njx6lZsixuNzaeZtb//5O1eNXj+vYR+ffHjuGEjuCYHHoHWMrTXeaXZ09dobbwsVoTGej2MzM\nzBkzZly/fv3VV1+NjIzs0KED0xEhxuTn5zd/o1iEWom33367rpfat29v0FsfOnTIoHNhnXv2\nzPj9dwAoun+/9quyF8/Viqp1GFZtutc+wdLRm82zUssrAKAyL8nSsX0996JLE7v170/o6gGl\nSxNTE+wo1p6eVu7uFTk5AJB744ZPrdFnC6ETi22hUSsAQF6eV//63McnTlAN77FjBbrKa3Sb\nPfvqqlUAkBETI33xwtLxP8sjNCoZVWoEACysGftY5wodqSWx2pubmZo6EzvDbeFiNBLT6LE7\nc+bMggULysrKQkJCtm/fbqFrORJqqXbs2KH9x9LS0mPHjuEmWgg10A8vq1jbTEePHjXo9etB\n99iVpqaqZDLOf+stVxZUTYlj86z49jV39AIAIAihW5fy53cAoLIwxVEzhl6jWhu9mZjulRMk\nqRDlU016E1hK29deSzl1CuqaZkcQFjaustIs+O9gbm3FDx7QhV26zpih8xzfqVOvrl4NJKlR\nKp+eOdPzvz2mdDE/ALCwam7ZtSbjWjlC0VMw0x47CrWFi1qtNsedf6p67JhL7GQy2cqVK8PD\nw52dnc+fPz927FimIkFMqfGxYW9vP2fOnBUrVjAVD0JIW3BwMFO3povJaVSq4ocP3fr3//c1\nkpQUplBNoatvXfPnrNy6UomdRimTvsgQOPvoPE1SWFj+vGqsVmf5X4W4kOp1AwC+3X82bP03\nsbt1S6NSsWpVhObZtqUSO0W96yeSf/qJanAFgto9fxRbb2+3/v2pUeNUHYldIdUg2Fx6darx\n0YWRlZKShpdlNjIdMYlEor1799J/3LdvHzUT7vXXXzfoKiS9o+bYMTUUm5yc7OfnFx4e7u/v\nn5iYiFld6xQZGZmo5fLly5s2bcrOzmY6LoQQAEDv3r3v3LlD6mLoWzt27cqqrmpZ9N+NnmWl\nWWpFVWeK0K1LXVfg2bnT8+EkBcl1nUZ310Edu8TS47AEi21h46b9kvugQVRDWVlZpGs3anoJ\nrVJSRo8d10ZlhwDQ4Y03uEJhXadRle0A4HlsrFwk0n6JTuwsrJyNvEusNjqxIzVqk91YTEdi\nt3fvXlH1DzQlJSUkJGTq1Knffvttdnb2li1bjBteszA4FBsREdG/f/+HDx+GhobGxMS0bWui\na2eQoXXt2rWBBxFCxrdkyRJ6exgjY/N4dDW7wrt3tV+qrO6uY1sI+Hb1TVwTuHaufksqtVqz\nNnqCnZW7u5W7e+0T5GVVXzV5Nm41xnNd+valx4ip0ic1aK8MravoyYvHj+md0zpVp246dXrr\nLaqhVijSL17Ufonex7aBW1wYiIXWVmb0xrWmRkdid/LkyWnTqrafO3PmTIcOHQ4fPrxw4cJD\nhw79+uuvxg2vWRhZPCEWi4ODg99++207O7vLly9v2LCBza5z3gNqhSoqKoR1f2FFCBnTrFmz\nevXqpfOlmJgYQ9/dtU8fqlGQkKB9XFKYSjUELp3q750SulYtcdUopdSQaG309Did3XUAICut\nTuxqTeZjW1jQ0/J0JnYcvg2bV7VpZ13T7FJPn64+md9h3Did51DsO3emk92nWvkGSWoU1bVO\neIwmdiwun/77/lsw2cTomGP37Nkzemuv69evv/7661Q5kt69e+fk5Bg1uuaRFBURbDbfwXj1\nqe/evTtt2rTU1NQJEyYcPnzY0ZGBPU+QiaAr12uXsNdoNImJiX379mUoKITQfwiFwg8++EDn\nS6NHjzb03V369oUffgCAonv3SLWaYLMBQCEupJd/CpxfUjuWb+fBthCqFZUAIClIsXSouS0n\nqdHQtU50JnYqqUglK6++mo7+PPfBg7P//hsAqP/WxrNtS80IpGum1JBaXeDJKyDAwtq6/r9R\nx4kTqWUW6b/9plEqqdFqpbiI1FTV4asxWGx8FlZOUnkFAChNNbHT0WPn4uLy6NEjAFAqlf/8\n889rr71GHS8uLnYygdIhDSctLrZ0cNC5tNsQIiIiBg8enJGRsXXr1l9++QWzulbu6dOnT58+\npRuUrKysfv36RUREMB0dQoh5bv2q9pNQSiT0YKWk6CnVINhcS0dv3e+kEYTApWrXCnq9hbYX\nSUn0ZDXtunQ0ursOAHTWK/EYNoxqVObllT19WvsEfvVCWrkoD2rNTSzPzKT7I+mR1np0nDCh\n6mplZdlXr1a1q/eipfYxe+lFDIquokd3IpoaHT12EyZMmD9//ieffHL58mW5XD5mzBjq+K1b\nt+gkzyxIi4qMsyRWJBItWrTo5MmT7du3P3HihJ+uhwe1NufOnQOAuXPnGrpeQ6Oo1eoLFy7U\n3uMSADQa3RN0EEIG4ty7N4vD0ahUAJB/+zZVoZceh7V0bE+wX75xltC1szg7EQBUMrFclEcX\nDabk3rhBNVhcrqv2wttqstJMqmFh7cziWtY+oe2gQQSbTW1ckXX1ql2tDah41f18GpVcUVFs\nYf2fj92nv/xSVZeYw6lrPaw2twEDBK6ukoICAHh69my7UaMAQF5djcXCyrmeqi7GQf8FlZUl\npEbNeDy16fhHs2nTpuDg4AULFgiFwu+++47uedq7d+9nn31m3PCaRVJUpL0fn4HcunVr+vTp\n6enpQUFBBw8etLN7+WbMqPUwqawOAC5fvjyh+gtxDVQ/PULIaLgCgWP37tRq07xbt3rMm6eW\nV1K7r0IDxmEpfAcvFoenUckBQFKQXCOxoyfGufTqpXMDVllJVWKnu1oeAM/W1qVXL6rXLevK\nlZ7z59c8waYNvdGWvCynRmJHj8N6DB3akCnvBIvl88YbDw4fBoC0c+dG7t0LWssyLP77t2OE\nhXVVFT1So1ZWvqD/aDp0DFPa2dlFR0dXVFSUlpbO0CokGBMTM2TIECPG1iykWi0rKTHoygmS\nJPfu3TtkyJD8/Pw9e/acOnUKszpUwz///LNkyRLtI0uWLPnnn3+Yisff3//s2bMn/+uj/2/v\nvgOqqvsHjn8ve4oguBXFnTbcVpYDt5UjtTRXamaPFVqmZZb2aGWaVjS0HIWrNLWUTM2fWwvT\nQrHU3BsRBBmXzT2/Pw7ehxjXy5X58f3663LPued8Lwr3zZkTJyqlmhb/X0EAcjEf96ZvWkuO\nPm3em1nQdelyMdjZm+c05tkbaw676g8/nPe1makJ+n0UlFIuPrULWkWtTp30B5d27sxnAPYO\n5jNVU2/+61pOydHR5iPz8r0fbr7MG/biz56N+ftvU1a6+S6xzmUh7Nx9zZevS0+8zY3USkWB\nm3mdnZ1v+0xJsrALKV8psbGayVR8u2KvX78+YsSILVu2NGnSZPXq1ffeuoY4kNMrr7zyxRdf\n5Hxm1KhRL730Uli+l3Evfvb29o/ntzfko48+yvdW6wCKVfV27SK++kopFfPXXxlJSeYD7Jy9\nqpnPvrwttyoNkyKPKaUyjLHpSTFOHtlbNIzXrpmPiquRX9ilmm8yazC4eOc+8cKsdufOh+bN\nU0olXr4ce+KET+Pcl9ZzqVhT36iW84g9pdSZDRv0fbjKYLDmADudf9euDq6umSkpSqkzoaEe\n1Qabr+SS7+kdJcxg7+Do7qOfEpuWcN2j7F3N7Pb778sIC7uQkpPzuSiifq2TYrqI3Y4dO4YO\nHRoZGTls2LCFCxeWu5utocREREQ0+vcttxs1ahSR360hAdyFqt86cl3Lyrr626+a4bz+pZtf\nA+sX4uZbz2DvoGVlKqWSo044eWTvW7uSY+dAzfx2uKXEZK/OybOyvVM+B9hlv/aRR+wcHU0Z\nGUqpC//3f/mEnXet+AsHlVKZKTczUxPMl00+uW6d/qBa69aetfLf1ZuXo7t77c6dz27apJQ6\ns3Fjo0HZZ2/YObo4upeJsxKdKlTRw65sbrErN3+jW9iFlG9XFdNtJzIzM2fMmNG1a1ej0bhq\n1aply5ZRdbCgVq1au3btyvnMzp07uWA1AJ1Po0bmu92f3/aTlpWhP3arbNUBdjqDvaNrpQD9\nsfHaCfPzl3bv1h94N2jgXi3PTkxNS7lxTn/oWqmOheU7eXqadxmf/+WXvDM45zid1nw5vdTY\n2Avbt+uPLV+XOC/z3tjIAwfiTv+lPy4Lm+t05kuupCdcy3sicKkrN1vsLOxCynf+4thid+nS\npSFDhuzbt69Vq1bffvtt/TwnBwG5jBs3bvTo0bNmzXr00Uc1TduzZ8+0adOmTJlS2uMCUDYY\nDDXat9cvxnt5z57agU8rpRxcvQp7SL571cb65U7Sk2IykmIcPXyVUpdu/VVZs0OHvC9JS4wy\n3wTsttdVqdu9u7797+KOHVlpafb/PjTL3snNycNX34iVGnvJo1pTpdSpH37QN/Ipg6HRgAGF\nejv1Hn982wsvKE3TTKYLW7f7d2uu8rt+cmlxvhV2psz0jOTYMrId0azchF1hJRf1FrsNGzaM\nGjUqLi7u5Zdfnjt3rpOTU1EtGYK98sorsbGxEyZMSElJUUq5urpOnDjx1VdfLe1xAbBdYY/5\ntqxWhw562F0/fMyUkWnn6GC+NJ313Pzqm/fGJl077l3/keTr12NunepeK7+wS76effidnYNT\nQafEmtXt1WvfW28ppTKMxku7dtW5dR00Mxef2rfC7rz+zInVq/UHVVu18goIKNTb8ahevVqb\nNvrN0C7vPaKHnWuZCjuDQd9Wl3bzKmFXQrJ3xRbFFru0tLTJkyd/+umnlSpVCg0N7d27950v\nE3cJg8Hw7rvvvvnmm8ePHzcYDI0bN2bfPVDeFfaYb8tqdeyoP8hKy7hx/KLffQHulRsWdiF2\nDk5uvvWNUSeUUklX//au/8jFHTvMewlrd+6cz1CjzRfMq3vbi7FVad7co3r1pKtXlVKnN27M\nL+z8Ey7+qZTKSL6ZmRKfGme8uGOHPqnx008X9u0oper37auH3fXDZzKMqc5eFcrCtU50BntH\nJw+/9MTrSqnUm1c8apStsyfLzTF2hZVcRDeKPXny5IMPPhgcHPzoo48ePnyYqoMN3NzcWrZs\n2aJFCzc3N5PJVAL3oARQfAp7zLdlfvffbz7MLurP0/ZObrfdfpYvj+rZd1nNTLmZevPyhVu/\nZyo1aeKR57jezJSb6QnZB/5bdTyfwWA+7u30jz9qea5n7urjb76tbcqN88dXrdLPhzXY2TW+\ndff5QjGfRWvKyLx24ISLdy3zRUbKAuf/3W+jzN1qtQx9m4qWvsXuDo+xW758ecuWLSMiIqZP\nn759+/YaNcrKkZsojy5duvTf//43ICCgW7dupT0WALbTj/ke+G8235nJYGen319BKRX1x0m3\nyg3NhVQorr71zLeOSLocYT7Lwb9r17wzm8+xMBjsrLwScsNbJ0AkXb1qvjyemZ2ji3OF7C1q\nKTHn/vr6a/1x7cBAD5s+On0aNTLfYuDyvqMuFk/vKHkuFbPPF0lPijFlplqeuYSJDbvk6Ggn\nT097W6+9l5KSEhQUNHz4cHd3982bN8+YMcPevszdNgTlQkZGxrp163r27FmnTp0dO3YEBQWd\nO3eutAcFoAyp+Uhr/cHN01cNBh/bFmKws/eolr3R7uqvOxMvZ19Srm6PHnln1q97p5RyqVQn\n3zuJ5VWrUyfzTrDjq1blncHVN/sMjEt7dtw4lr38ZiNGWPsG8gjofat3D520dypbt6r/3311\nNS019lKpjiU3sWGXEhNj8wF2x44da9OmTXBwcJcuXQ4fPtw1vz93gNs6fvz4pEmTatSoMX78\n+Pvvv99kMu3atWvixIl16tQp7aEBKEN8761h0LfSadrlfYdtXo5nzfv1B1d/y75YpoOrq/kY\nPrP0hCj9+DCllLkFb8vOwaHRwIH64xPffadfQDgnN9/sG2Cc/jH7bhOulSoV9kInOdV4JHuL\nXVZ65vktu2xeTnFwcK1ovlxfyq3bspURcsMuOtq2/bDLli1r3br1iRMnpk+fvnXr1qpVqxb5\n2HA3ePjhh5s1axYREfHFF19cunRp9uzZpT0iAGWRZsoyZUV5N8reAnT6xx9tXpSTZ2X9Ym9X\nfzuuP+MfGOjgmnuDXMKlcP2BnYOTW5VCnKjR9Nbmt7SbN80nvZo5e1Wzd3JLiY6/vPdo9vwj\nRzq4uBTyTWTTNJOje6p3/exD2Y6tXGnbcoqPS6Xse3Wk3jhfqgPJTW7YxcQU9syJhISEwYMH\njxgxws/Pb8+ePTNmzOAmS7DZr7/+2rJlywkTJvTv39/R0bG0hwOgjEq+fsqUkVr94Wb6lxe3\nb0+NjbV5aRX8W6dEx8eezN4Pm/cOraaMlKTI7MuguFe7x86+EJfuqta2rd/92RsF/wwOzn1t\nXoPBrXKDk2v3aJlZSimDvX3z8eNtehNKKZUaezErPbl25+b6l5d27ow/f97mpRUH81Wd05Oi\ns1ITS3Us/yIzXNITEzNTUwu1xe7gwYPNmzf/7rvv+vfvHx4ebvNhsIDujz/+aNWq1ZAhQ+rU\nqfPOO+9culS2DsIAUEYkXj6ilKrZvpl+zkRWevo/a9bYvDT3Ko0iw07pyWWwswt4/LFcM8Sf\nP2i+v0WFWi0Lu/wWL76oP7geHn5m06ZcU7PSPc5t/l1/XL9Pb6+6t7nusQV6fdbq9IDBwV4p\npZlMf3/zjc1LKw6uleqaT3NJjjlTuoPJSeZ17PTbTlh5jJ2macHBwZMnT7azs/v444+DgoKK\neXS4K7Ro0eKLL76YN2/e2rVrFy9ePHPmTKXU6tWre/bsWaFChdIeXVlhMplCQ0NzXejVAhcX\nl169enEmE8TISL6ZEnteKeVezafyA02vh/+llPorJOT+ceNsXKLBcGlX9gY5v/sDMpPPK1XF\nPDEzNVG/qatSys2vvpNnoQ9YajJ06P4ZM5KuXFFK7X3jjbo9etg5/C8kDsz6OCs9UyllsLNr\nNjJ3U1rPlJWefO0fpZSzt0etDu0ubt+vlIpYvLjdm2/alZkdIPZObs5e1dNuXlFKGaNOetZ8\noLRHlE1m2Fl/24no6OiRI0f+/PPPjRs3Xr169X333Vf8o8NdxNXVddiwYcOGDTt16tSSJUsm\nTJgwfPjwzp07b968ubSHViaEh4d/8MEHhXpJaGhoz5498173vyB6CyqlSuAlFCcKK+HiH7e2\nrtnfO+q57S8FKaUiw8KiIyL8bPo8un74cMzf2Zcyqd25edzpva6V6mbfoEzTYv7ebN5cV7F+\nexuW7+Di0m7q1P8bP14pFfPXX2GzZj00Y4Y+6djy5SfXr9cf+3dt4eBu1LIyDfa2ZIYx8pgp\nK11/3Pw/4/WwS7py5dQPPzQaNMiGBRYT98oN9LBLjb1gyki1c7TxgMKiJTPsrLyI3c6dO4cO\nHXr16tVhw4YtWLDA3d29REaHu1GDBg1mz549a9asTZs2LV68uLSHU1akp6crpR5Vyt+KmS8o\ntUeplJSUzZs3F3Td/3yFhoYqpUrgJY89ZvsmCtyFTJmpSVeO6I/dqzau1ubRPa9PzTAalVJ/\nBgd3t+kXxZEvv9Qf2Ls61WzfTMvKjPrz+yotBjp6+N44vi0l5qw+1bPGfeYbnhbWfc89d3jh\nwpijR5VSv82c6VG9+n1jx55ct+6XsWP1GZwquDd7trspI9UYdcKjejMbVpFw4Q/9gbNXtTpd\nn/JuOCPu5Eml1MEPPyxTYedWpVHsyV1KKc2UZYw6UUY22pVO2P3xxx8ff/zxr7/+GhUVpWla\n1apVH3rooQkTJrRsWej9/flKud1tJ7KysmbOnDlr1iw3N7cVK1Y888wzRbJewDIHB4c+ffr0\n6dOntAdStvgrVahNE/qNd63JQXML6l+WzEsAKyVcOGTKzN4u5eXf2qmC1z3Dhh1ZuFApdWzF\nioffeaewl/ZNjY09tny5/rj+493sXZ2UUpmpiVd++9rOwdmUkb352cGlgk+jfG4yZiU7R8ee\n33yz6qGHstLSNJPpl+ef3zt1asqNG9mTDYa2U591ruihlIq/cMiGsEuOPp2eFK0/rlC7pcHO\nrsXLL29/8UWl1LWDB89v3Zr3hmalxdHN29mrelr8VaVU4uWIuzfsNm7c+OSTTzZv3nzgwIF+\nfn4Gg+H69evbt29v167d+vXrH79105I7kWxxi93ly5eHDBmyd+/eFi1arF69un59qy66DaBM\nKWwOlthLUL4U94aGgpgyUuLPZx/u5lqprlOFqkqpVhMnRixapGVlZaWlhb37bpcvvijUMsM/\n+0zf4KeUajd1lqbOG68dV0opTTNXnZ2jS5UWT97hTsMqLVr0/PrrTcOG6fcN+1/VKdVx7txG\nTwXG/L1FKZWecC3lxnnXQt40Iu70Pv2BvYune9UmSqlmzz7728yZyVFRSql9b71Vp1s3227O\nURw8a9ynh11a/NW0hGs2bwctQqUQdlOnTp0zZ45+W72c5s+f/8YbbxRJ2KUUfIxdaGjos88+\nGxsb+/LLL8+ZM8fZ1ltTAADKuxLY0FCQuFN7TJlp+mPv+o99TrNEAAAgAElEQVRkP2jYsPHT\nTx9fuVIpFbFo0QP/+Y9vM2u3eKXcuHHoo4/0x/5du/rdf7/S7otz944/97tmytSfd6pQtfK9\njzl6FMFdHBoPHuxUocIvzz+vn0ihlHLz8+v00UdNnnlGy8qMO7UnKz1ZKRV3anehwi7pytH0\nhGv644p12xns7JVSjm5ubV9/fefEiUqpawcP/r18edPhw+/8LRQJ92r3xJ7cqf9Txp8Lq3x/\n7uvLlLxSCLtTp06NHDky7/PPPvvsG2+8USSryPes2IyMjHfffXfmzJmenp5r1qwZMGBAkawL\nAHRZWVll86yOQr1E3U3ngpTAhoZ8pd68nHA5+w4TbpUbmu8or5R6+J13Tn7/fVZ6uikzc+uY\nMYP37ct52qkF+6ZNS7t507wQpZQyGLzrP1qhVouUG+dNGSlOFaq6eNcqwncR0Lv3c2fOnNuy\n5eaZMxX8/et27+7o4aGUMtg7eAU8GHtiu1IqLT4y6epRj+r3WrPArDTjjX926I8dXCvm3LN5\n/7hxf37yiX4pu92TJtXt0cOtcuUifC82s3Nw8qx5f/z535VSxqh/0hOinCpUue2rilUphF3t\n2rVDQ0OH58nt0NBQf39rDqG+veToaHsnJ+ccF5U4f/784MGDw8LC2rZt++2339a9g4vrAEC+\nyuxZHYV9ibprzgUpgQ0NeZkyUqMjQrNPhrV3qNT4X4e7VaxXr9UrrxyYPVspFXngwN6pUzvM\nmXPbZV74v/+L+Oor/XH9Pn2q57gUq72zh21nMFjD3tm5fn4HDVeo1TzhwqHMlHil1I0T2118\n6ji4eFpelKaZoiM2mjKyj1Wt1DhQ31ync3Bx6Th//ob+/ZVSydHRm0eM6L9pk6Fs3ETAq27b\nhEvhWlaG0rSY479UbzO0dPcUl0LYvfXWW6NHj/7hhx86derk5+enlIqOjt61a1doaOiSJUuK\nZBXZt5249Z1dt27dmDFj4uPjX3755Q8//JDbAAAoDmX8rI7Cnn1sxbzlXglsaMhFy8qICl+n\nF49Syrv+ow6uFXPN8+Dbb5/esOHG8eNKqYNz53pUq9YyzzbFnOJOndo0ZIhmMimlHN3dO93a\nIVuKDHYOlRp3iQpfp5QyZaReD19Xtc0QC3e50DRTzNGfUmIv6F+6V2viVrlBrnka9OvXcMCA\nk2vXKqXObdmyIygo8NNPi+0dFIK9k7tXndY3z/yqlEq7eeXm2f0V69lyKZmiUgphN3z48MqV\nK3/44YdTp041Go1KKXd393bt2oWGhvbo0aNIVpEcHa3vh01NTZ0yZUpwcHDlypU3b97cvcyc\nSgNAqjJ7VgcnguRVAhsacspKM14/vD71ZvZBaa6+AV7+rfPO5uDq+viaNSsffDAjKUkptfOV\nV26ePdthzpy8d31VSl07dOjHvn31UwaVUh0//PBO7vdQhNwqN/Co3izp6l9KqbSEa9cOfVfl\ngf72zh5558xKS4o++lPKrTuuOrr7+N6Tfwx0+/LLqEOH9B2y4Z99lpmcHPj55zbfjrYIVaz7\nkDHyeEZynFIq7vQ+eyd3z1rNS2swpXO5kx49evTo0UPTtMTERKWUp6enoUi3W6bExFTw9z9+\n/PhTTz119OjRwMDA5cuXV6tWrQhXAQAo70pgQ4NOy8pMvBJx88zerPTsTaFOHn6V73+ioH12\nvs2aPbFmzY/9+mWlpSmlwj/77MzGjS2Cghr07+9Vp45SSmladERExKJFR776ypSRfc3hZs8+\na/stK4qB7z3dM5Ji0hKuKaXSbl69vH+xV522HtXucXD1UkopTUtPik6KPJZ46U/zZV/snT2q\ntBho55D/eY0uPj79Nm789tFH9aMJjy5devW33x55//16jz9eurtlDfYOfvf3iTywQj9PJebY\n1rT4SO+GHeydSuH6uKV5gWKDwVAc91bKSk9Pi48PMxpHtW6dlpY2ffr0t99+265s7IkHAJQp\nxbqhwZSZmhR5PC3uUnL0GfM5sEopJ8/KVVs9ZedgaVNT3Z49+/74Y+hTT6UnJCilEi5e3PXq\nq7tefdWpQgVXHx9jVFTmv3eXN3766W63DrMrIwz2jlVaPnXt0HfpiVFKKVNGatyp3XGndts5\nONs5umSlGc2n6+oc3SpWafmUo5u3hWX63nvvwG3b1vXooV9j5cbx4z/27etZq1ZA7961Hn20\n4cCBVp5rUuScK1T1u+/x6CMbNM2klEq8EpF07XiVFgNcfYpln74FZejOExUrVlRK3bx1Uk8u\n+Z5u9ttvv+WdM/r8+VWaFh4WVrt27VWrVj388MPFMVoAgBiF2tBg/edR1J9rU+Mu53rSvVoT\n33t62jkUeMCZWd0ePYYdPLhl1Kgr+/ebn0xPSNBTz8zO0fGht99u9+abZefqbmb2Tq7V2jxz\n49jWpMi/zU+aMtNyZq7Oo3qzSk26WI5dXdVWrYb+/nvo009fO5h9IcDES5eOLFx4ZOHC1uHh\n1pxrUkzcqzQyNH8yOmKj/u60rIykK0fv6rCbMGGChak7d+4s6KyunAf5Hjp06OlBg84o1fmB\nB77fvt3Hx6eIRwmUGaV1YVVAPMsbGgr6PPL19a1SpYq+5U+XmGTUDP/bGefgXsmrdgvnijWM\nKWlK5S6bfDlUq/bYzz+f37Tpry+/vLpvn35B4P9NdXUN6Nu3xWuvedWvn5iUZM0CS4VL3Y52\nlRonXolIi4/UTwf+H3sHF+9aHtWbOblXMqZkKJVhzQLt/Pwe37r1REhI+Lx5SZf/l85RR47k\n/P6XApfKXvc/HX/hUOqN88pg0CrUMY8nNTXV14pb2N+5MhR2M27dSDhfnTp12rhxY66/kEwm\n0/PPP1+jRg2llKZpwcHBkydPtrOzmz937sRJk4p1tEDpKsULqwLiWd7QYOHz6Im9e7+qUOFV\nTZt3a+PZU3sW2ju5OXvXcq/SyNmrmlJKn/Rqrr6x6N7Bg+8dPPjcL7OVUglnK6bFx7v6+no3\naFC1det8z6goyKXdn2emJTo4e9bqMN785G3Ho69XKVW32+vWvyo3T89KNRqYMlLS4q9lJMdp\nWel2Tm5f1by/cAv595DaTni99YsvXti27fTGjVd/+01pWutXO8X89nndbq/nO2Zrllmol+TP\n07Oi7+OZqQlKGXJe5CUpKSkmJuaOlmydMhR2ltnb2+f7WfXaa68ZDIaYmJiRI0du2rSpUaNG\n33333QMPlIn7tQHFx7YLq1rYhWQymfLOf8GKkVyw+KU1r7LhJVa+ipcU9iXWzyab5Q0NFj6P\nVHy8HinmVKn5yPO5Zit0xfz7lXdyekRmeorSVGb6vw7Ou/148pvDtndh5+jq6lvXVdW9k4Xk\nfKWdg0Pdnj3r9uypf3lu62zbx2f7aPLh4FL0pxBYu+pSWWvR7kJKSEh44IEHrly5MmzYsC++\n+MLDI5+zqQFhbLuwqoVDGk6dOpXzS1dXV6XUHqvH43prs4H1L1FKeXp6FvYlNqyIlxT2JTlf\nBaB8KYWwK9pdSImJiZcuXXJ0dOzUqZOrq+urr75qMpkOHz7s5eVVtJdQyUXTtNjYWB8fn/K+\nFjFvRF9LfHz8Aw88YD4JOjk5ufhWV7psu7BqQbuQ/vnnn8mTJ+d8smfPnnnnLIiLi0vPnj2V\nUta/RH9V9+7dW7VqVaiXFHZFvMS2f5qetzaBiFccx6o+//zzDXI8zjVVn/Tcc88V9qPqy0mB\nSqmuXbtaP5Jcv3s/n9jJwd4uMyM950L067taWKy+3lzz5HqVbb/kb7vqXMxr+eq1Lvm+0Pwt\nynfMlplf0qVLl1wfJUWixD6PDFrh923foWbNmo0ePTrfXUhLly7966+/CrU0b2/vgo5vBXQf\nf/xxUFBQaY+iiC1btmz06NGPPfZYvhdWzRt8APIyb2jo3Llzzg0Nhw8ftmFDQ4cOHR7fs0cp\n9ZpSc289+Vqe2eYW8Pxtnd3yvlIqoIft9zrbv+L1ar5ekTHxDw+dbX7ytuPR15tr1Ta/i5yK\n/Fthfj7fMVuzzEK9xAYl8HlUCmHn7Ox87do1b+/cF6qJi4urWrVqWppVJwqZZWVlJfz7xO8f\nfvhh9OjRL7zwQps2be50rAX7/fffFyxYIGAtYt6IeS1Llizp16+f+UlnZ2c3N7fiW2kp2rJl\ny4cffhgWFpbzwqqTJk0q2gurAoIV7YaGvJ9HFkj6qCqxFZXiR0mRKKHPI63E1a9fPyQkJO/z\nISEhDRo0uPPlr1mzRim1Zs2aO1/U3bAWMW+kxNZS1phMpvj4+Pj4eJPJVNpjAcoZJyen2NjY\nvM/HxsY6OTkV66qF/VaU9HbK+0dJKRxjV8L35gNkK6Y7uAB3A9uOVQXKslIIuxK7Nx8AABaw\noQHylM7lTor13nwAAFiDDQ2QpzQvUMwuJABA6WJDA4QpN3eeAACgmLChAWIU5cX3ygj9gunF\nfdl0MWsR80ZKbC0AcOeE/VaU9HbK+0dJKVzHrrhlZWVt3749MDDQ3t6etZSFVQhbCwDcOWG/\nFSW9nfL+USIw7AAAAO5OAnfFAgAA3J0IOwAAACEIOwAAACEIOwAAACEIOwAAACEIOwAAACEI\nOwAAACEIOwAAACEIOwAAACHKd9gtW7asYcOGzs7OzZo1Cw0NvcPZSlGhRhgcHGwwGAYMGFAy\nYyssa95LSkrKxIkTa9eu7eLiEhAQ8NZbb2VlZZXwOFEoRqNx0aJFLVq0MBgMK1asKL4VRUZG\nBgUF1alTx8PDo0WLFitXriyOtcyaNcuQQ8WKFYtjLY0bNzb8W//+/Yt8LQkJCUFBQbVr13Z1\ndW3Xrt3+/fuLfBW4E9b8Svzzzz+feuqpqlWrVqpUqUePHuHh4SU8SOtZ+Wl15cqV4cOH+/r6\n+vn5BQUFGY3Gkhyklaz/tVb2K+JftHJr8+bNDg4OISEhsbGx8+bNc3BwOHTokM2zlaJCjTA8\nPLx27dpt2rR58sknS3KQVrLyvbz88ss1atQ4dOhQSkrKzp07K1So8P7775f8aGG9Tz/9dPTo\n0YcOHVJKLV++vPhW9NZbby1evPjy5csJCQlfffWVnZ3dli1binwtM2fObNu2bZEv1oJ//vlH\nKbVy5coiX3K/fv2aNGkSERGRmJj42Wefubu7nz59usjXAttY+Suxd+/ea9asuXbt2vXr18eM\nGePt7X358uWSH+1tWfl2bty44e/v/8QTT5w7dy4pKemLL7744YcfSn60t2Xlr7WyXxG5lOOw\nCwwM7Nevn/nLVq1aDR061ObZSpH1I0xMTGzUqNGmTZt69+5dNsPOyvfSvn370aNHm7/s1avX\ngAEDSmJ8uGPFHXa51KpVa8aMGUW+2JIPu8mTJ/v4+KSkpBTtYo1Go52d3YoVK8zPtGrVKigo\nqGjXApvZ8AGUmprq4ODw9ddfF+/IbGLl23nttdeqVq2anJxcgkO7I5Z/rZX9isilvO6K1TQt\nLCysY8eO5mcCAwN//fVX22YrRYUa4fjx47t06dKrV68SGlwhWf9enn766a1bt4aHh6elpe3Z\nsycsLGzw4MElN1CUB0ajUf8T+bHHHiuO5R85csTT09PPz693795Hjx4tjlWYZWZmLlu2bNiw\nYS4uLkW7ZP33uMFgyPnkvn37inYtsI1tH0BxcXFZWVleXl7FO7jCs/7t/Pjjj/369XN1dS25\nwRWbsl8ReZXXsEtMTDQajX5+fuZnKleufO3aNdtmK0XWj3DFihUHDx6cO3duCY6ucKx/L+PH\njx84cGCLFi1cXFw6deo0efLk4jj2COXUX3/9ZTAYPDw8xo0b9+WXX7Zs2bLIV1G1atWlS5de\nvHjx999/9/Lyat++/aVLl4p8LWabNm26du3ac889V+RLdnd379mz53vvvffXX38ZjcaFCxf+\n+eefkZGRRb4i2MC2D6CgoKBatWp17969mEdXaNa/nXPnzvn4+PTq1cvV1dXf3//VV18tm8fY\nWaPsV0Re5TXs8sr7Z+udzFaK8h3huXPnJkyYsHLlyvL1N1BB3+0pU6asW7cuLCzMaDRu27Zt\nzpw5wcHBJT88lE3NmjXTNC0uLu6TTz559tlnN27cWOSrGDNmzODBg729vevWrfvNN994eHgs\nWrSoyNditmTJkgcffLBp06bFsfCQkJC2bdt27drVz8/vl19+GTdunL29fXGsCHfuth9Ab775\n5s8//7x27Vo3N7cSG5XNCno7mqbNnTt35MiRMTExa9eu/f7774OCgkp+eMWk7FdEeQ07T09P\nd3f36Oho8zPR0dFVqlSxbbZSZOUIjxw5cuPGDf3kHYPBsGnTpnXr1hkMhvPnz5focC2y8r2Y\nTKZPP/30lVdeadu2rZubW+fOnceOHfvRRx+V7GBR1lWsWHHs2LG9evVauHBhsa7IycmpYcOG\np06dKqblR0ZGbt68uTg21+l8fX2XLFkSGRmZnJy8fv36yMjIgICAYloXCqWwH0AzZswIDg7e\nvHlz69atS2SAhWP926latWr37t0HDRrk7u7eunXrCRMmrF27tgRHWpTKfkXkVV7DzmAwtGvX\nbufOneZnduzY8dBDD9k2WymycoR9+/bNeWik+eSJOnXqlOhwLbL+H8Xe3j7nXzyaprGNAfnK\nyMgo7j+O09PTT548Wb169WJafkhIiJub26BBg4pp+TnduHHjl19+6du3bwmsC7dVqA+gd955\nZ968eT///HP79u1LaoCFY/3befjhh3N+qWmanZ3w2Chbiv30jGKT8wzk+fPn5zwDefr06V5e\nXredrYyw8o3kVGbPirXyvQwdOrR27dphYWHJyck7duzw8fF57bXXSm/UKARVzGfFDhgwYO/e\nvQkJCVFRUfPnz7ezs1uzZk1xrGX37t0JCQlnz54dPHiwm5vbiRMninwtugYNGowbN66YFq5p\n2uLFi1etWpWQkHDixImOHTs2b948NTW1+FaHQrHyV+KsWbM8PDz27NlTeiO1ipVv5/fff3dx\ncVm9erXRaPz9999r1ar1n//8p/RGfXt5f62Vr4rIpRyHnaZpISEh9evXd3Jyatq06YYNG8zP\n5+qhgmYrO6x8I2ZlNuw0695LQkLChAkT/P39XVxc6tWrN23aND6Kyrht27bl+psw5wVritCe\nPXu6devm5eXl6+vboUOH0NDQ4ljLvn379LVUq1atT58+R48eLY61aJq2e/dupdQff/xRTMvX\nNC0mJmbEiBFeXl6VK1ceN25cbGxs8a0LNrDmV6Kzs3Oun6/p06eXznBvx8pPq59++um+++5z\ndnauU6fOG2+8UeQX+ikSFn6tlbuKyMmgaVpxbQwEAABACSqvu70BAACQC2EHAAAgBGEHAAAg\nBGEHAAAgBGEHAAAgBGEHAAAgBGEHAAAgBGEHAAAgBGEHAAAgBGEHAAAgBGEHAAAgBGEHAAAg\nBGEHAAAgBGEHAAAgBGEHAAAgBGEHAAAgBGEHAAAgBGEHAAAgBGEHAAAgBGEHAAAgBGF31+nb\nt++LL76oPzaZTGPGjPHx8TEYDIcOHco5yZqXA3eVnP/5+UEACjJ06NAxY8ZYObOFHyULy+nS\npcvrr79u4/ikI+yKhtFoXLRoUYsWLQwGw4oVK2xYQnx8/CuvvFK3bl03N7dmzZrNnDkzPj6+\nyMeZy8aNG9evX3/06FFN01q1alXYlz/22GMTJkwojoEBd2jkyJEGg8FgMDg6OlauXLlz584L\nFy7MzMws7XGhjNL/w8yYMcP8zL59+wwGw7Vr1+5wyRY+HVJSUiZOnFi7dm0XF5eAgIC33nor\nKyvLPHXZsmUNGzZ0dnZu1qxZaGhozhdamHRb5h8Ng8Hg4+PTpUuXgwcP3skbLBkl9nFj5ff2\ntrMFBwcbDIYBAwaYn0lISAgKCqpdu7arq2u7du32799vnhQZGRkUFFSnTh0PD48WLVqsXLnS\nmkkFIeyKxtdff33gwIFFixbZvIRhw4Zt37597dq1N27cWLt2rclk+vrrr4twhGY//vjjZ599\npj8+ffp03bp1a9SokXeSNS8HyrIOHTpompaWlhYRETF69Oj33nuvc+fOqampNi+Q//yyubi4\nzJs3LyoqqmgXa+HT4fXXX//+++9/+OGHmzdvLl26NDg4eO7cufqkLVu2jB49etq0adeuXRs1\nalT//v3/+OOP206ykv6joWna8ePHq1Sp0qNHj/T09Dt8mzYrUz9WVn5vbzvb4cOH582b16ZN\nm5xPjhw5ctu2bZs2bYqOjh42bFj37t3PnDmjT1qwYMF99923f//+yMjIF154Yfjw4Vu3br3t\npAJpKFJKqeXLlxf2VampqQ4ODiEhIflODQwMHD169KBBg/z8/Hx8fCZPnpyVlaVPMplM8+bN\nq1evnrOzc5MmTRYtWmR+lclkmj9/foMGDVxcXFq3br137179+T59+owfP17TtCeffNL836Be\nvXo5J1nz8hEjRuT8j/Taa6/5+fmlp6ebBzBo0KB+/foV9lsBFIkRI0aYP710Z86ccXZ2/uCD\nD/Qv7+RnR8vzw1LQolBejBgxolu3bg888MALL7ygP7N3716lVGRkZFGtIu+nQ/v27UePHm3+\nslevXgMGDNAfBwYG5vz92apVq6FDh952kjVy/Wjs3LlTKXXy5En9y+eff17/le7j49OrVy/z\n8/p6x4wZM3To0CpVqvj6+r700kuZmZn6pJSUlDFjxnh6elavXv25557r06eP/r5+/vlnDw+P\njIwMTdNOnjyplBo3bpz+kqlTp3bp0kX7949SQcvJ9XFz/PhxC4O5E1Z+by3PlpiY2KhRo02b\nNvXu3fvJJ5/UnzQajXZ2ditWrMj5qqCgoHyHUatWrRkzZhR2khlb7MoEJycnd3f3nTt3ZmRk\n5DvDkiVLWrZsefLkyfXr1y9duvTjjz/Wn585c+aSJUtWrVoVGxu7YMGCqVOnfvfdd/qkt99+\n+7333ps7d25UVNTnn3+ed/vt2rVr33///ZYtW2qadvr06VxTb/vyb775pnfv3ub/l9OnT09N\nTTVvkY6Njd2wYcOoUaPu5NsCFKGAgICePXuuW7dO//JOfnZysbAolCMGg2H27NmLFi3SE6Qg\n06ZNMxRg165dhVrj008/vXXr1vDw8LS0tD179oSFhQ0ePFgppWlaWFhYx44dzXMGBgb++uuv\nlifZ4Pr160uXLq1Xr16dOnX0ZxYuXKj/Sj927FjNmjX79u2b8wCGpUuXBgYGnjp1atOmTSEh\nISEhIfrzb7zxxs6dO3fv3h0REeHm5rZhwwb9+UceeSQ1NfXQoUNKqV27dvn6+uodqX+Z811Y\nXk6uj5vGjRtbGExOhfrHsvJ7e9vZxo8f36VLl169euV6laZpBoMh55P79u3LtXCj0RgSEhIb\nG/vYY49ZPyk3y92HwlI2bbHTNG3lypXu7u6VKlV64oknPvjgg3/++cc8KTAwsHXr1uYv586d\nW6NGDU3TUlJS3N3dt23bZp70zjvv6H8DGY1GV1dX849oTjn/PDKHXa5JVr4850+apmljx47t\n3bu3/jg4OLhatWpF8icUYIO8W+w0TZsyZYqfn59WFD875scWFoVyZMSIEd27d9c0rXPnzvpW\nlhLYYqdp2sSJE/XPYjs7u9mzZ+tP6gdYr1q1yjzbvHnz3NzcLE+yUq6tX1WrVg0LC8t3ztTU\nVHt7+yNHjuhf5tpMNWTIkJEjR2qalpyc7OLisnr1av35jIyMGjVqmLdEtmnT5r333tPnnzFj\nhouLy9WrV41Go6Oj4759+7QcP0qWl5Pr46agwdwJK7+3lmdbvnx5kyZNkpOT9TGbt9hpmtar\nV6+mTZsePXo0KSlpwYIFdnZ21atXN089evSo/i/i4uKSc8Oe5Un5YotdWTFkyJALFy4EBwfX\nqlVr8eLFTZs2XbBggXlqzjMbWrdufeXKlYSEhOPHjxuNxh49ejg4ONjb29vZ2U2fPv3s2bNK\nqRMnTqSkpDz66KO2Dca2l48ZM2bLli2RkZFKqaVLlw4fPtze3t62AQDFRP+LuQh/diwsCuXR\nBx98sH79+gMHDpTAuqZMmbJu3bqwsDCj0bht27Y5c+YEBwfnO6eWZ2OPNZMKYv6bJzY2dty4\ncTkP9vr777+feOKJypUr29nZubi4ZGVlXbx40fzCBg0amB97e3vHxcUppc6ePZuammr+kHJw\ncGjevLl5to4dO+rbxnbv3t2zZ882bdrs2rVr//79jo6OuQ5Bs7ycvPIdTNGy8ntrnu3cuXMT\nJkxYuXKlq6tr3tlCQkLatm3btWtXPz+/X375Zdy4cTk/Ips1a6ZpWlxc3CeffPLss89u3LjR\nmkn5IuxKzo8//mjeCPzNN9/knaFSpUpDhgz57LPPTpw48cwzz0yaNMl8hlS+/7dMJpNSKiIi\nIjMzMysry2QyaZqm/3xqmlbQq6xh28tbt27drFmzkJCQw4cPHz58mP2wKGv++eefunXrqiL9\n2bGwKJRHrVq1GjRo0OTJkwuaoah2xZpMpk8//fSVV15p27atm5tb586dx44d+9FHHymlPD09\n3d3do6OjzTNHR0dXqVLF8iQbeHt7T58+3dHRUf9I0rcq1ahR4+DBg2lpaVlZWY6Ojjl3xVr4\nuShoUseOHffv33/s2LHExMSWLVt27Nhx586du3bteuihhxwdHa1fjm1zFuofy8rvrYXZjhw5\ncuPGDf0MaIPBsGnTpnXr1hkMhvPnzyulfH19lyxZEhkZmZycvH79+sjIyICAgFwLr1ix4tix\nY3v16rVw4ULrJ+VC2JWcvn37mreUjhw50sKcdnZ27du3T0lJSUlJ0Z/JeTr6wYMHq1evXqFC\nhSZNmri6um7evDnvEvRJe/bssW2oVr7c0dFR/1QzGzNmzNdff71kyZL27ds3bNjQtrUDxeHc\nuXObN2/u37+/uvU/vEh+diwsCuXUu++++9tvv/3000/5Tp01a1ZBu8DyHjRmgcFgsLe3z1kn\nmqbpm3A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Use SQL interface for prediction details

\n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_-1033839688", + "id": "20230307-171032_2030812675", + "dateCreated": "2022-07-29T16:54:55+0000", + "dateStarted": "2023-03-07T17:35:41+0000", + "dateFinished": "2023-03-07T17:35:41+0000", + "status": "FINISHED", + "$$hashKey": "object:60" + }, + { + "title": "Create table for use in SQL query", + "text": "%r\n\nore.drop(table = \"DT_TEST_TABLE\")\nore.create(DEMO_DF, table =\"DT_TEST_TABLE\")\n", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:35:41+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "sql", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/undefined", + "fontSize": 9, + "title": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032967_2033032692", + "id": "20230307-171032_1146754790", + "dateCreated": "2022-06-28T20:19:36+0000", + "dateStarted": "2023-03-07T17:35:41+0000", + "dateFinished": "2023-03-07T17:35:42+0000", + "status": "FINISHED", + "$$hashKey": "object:61" + }, + { + "title": "Demonstrate using the SQL interface so score data and display prediction details", + "text": "%sql\n\nSELECT CUST_ID,\n round(PREDICTION_YRS_RES,3) PRED_YRS_RES,\n RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,\n RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,\n RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE\nFROM (SELECT CUST_ID,\n PREDICTION(DT_CLASSIFICATION_MODEL USING *) PREDICTION_YRS_RES,\n PREDICTION_DETAILS(DT_CLASSIFICATION_MODEL USING *) PD\n FROM DT_TEST_TABLE\n WHERE rownum < 20\n ORDER BY CUST_ID) OUT,\n XMLTABLE('/Details'\n PASSING OUT.PD\n COLUMNS \n \"Attribute1\" XMLType PATH 'Attribute[1]',\n \"Attribute2\" XMLType PATH 'Attribute[2]',\n \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED\n ", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:35:42+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "sql", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/osql", + "fontSize": 9, + "title": true, + "results": { + "0": { + "graph": { + "mode": "table", + "optionOpen": false, + "commonSetting": {}, + "height": 300, + "setting": { + "table": { + "initialized": false, + "tableOptionSpecHash": "[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]", + "tableOptionValue": { + "showAggregationFooter": false, + "showPagination": false, + "useFilter": false + }, + "tableGridState": {}, + "tableColumnTypeState": { + "names": { + "CUST_ID": "number", + "FIRST_ATTRIBUTE": "string", + "THIRD_ATTRIBUTE": "string", + "SECOND_ATTRIBUTE": "string", + "PRED_YRS_RES": "number" + }, + "updated": false + }, + "updated": false + } + } + } + } + }, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "TABLE", + "data": "CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100100\t1\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".239\" \t\"HOUSEHOLD_SIZE\" actualValue=\"4-5\" weight=\".223\" \t\n100200\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".179\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".043\" \t\n100300\t1\t\"OCCUPATION\" actualValue=\"Prof.\" weight=\".239\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".223\" \t\n100400\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"6-8\" weight=\".179\" \t\"YRS_RESIDENCE\" actualValue=\"6\" weight=\"-.057\" \t\n100500\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".179\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".043\" \t\n100600\t0\t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".172\" \t\"OCCUPATION\" actualValue=\"Transp.\" weight=\".103\" \t\"EDUCATION\" actualValue=\"11th\" weight=\".051\" \n100700\t0\t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".065\" \t\"YRS_RESIDENCE\" actualValue=\"6\" weight=\"-.069\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\"-.223\" \n100800\t0\t\"OCCUPATION\" actualValue=\"Sales\" weight=\".065\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\"-.069\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\"-.223\" \n100900\t1\t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".239\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".223\" \t\n101000\t0\t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".065\" \t\"YRS_RESIDENCE\" actualValue=\"7\" weight=\"-.069\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\"-.223\" \n101100\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".179\" \t\"YRS_RESIDENCE\" actualValue=\"4\" weight=\"-.057\" \t\n101200\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".179\" \t\"YRS_RESIDENCE\" actualValue=\"1\" weight=\".043\" \t\n101300\t0\t\"OCCUPATION\" actualValue=\"TechSup\" weight=\".065\" \t\"YRS_RESIDENCE\" actualValue=\"4\" weight=\"-.069\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\"-.223\" \n101400\t0\t\"OCCUPATION\" actualValue=\"Machine\" weight=\".065\" \t\"YRS_RESIDENCE\" actualValue=\"4\" weight=\"-.069\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\"-.223\" \n101500\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"1\" weight=\".179\" \t\"YRS_RESIDENCE\" actualValue=\"2\" weight=\".043\" \t\n101600\t1\t\"OCCUPATION\" actualValue=\"Exec.\" weight=\".239\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\".223\" \t\n101700\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".179\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".043\" \t\n101800\t0\t\"OCCUPATION\" actualValue=\"Crafts\" weight=\".065\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\"-.069\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\"-.223\" \n101900\t0\t\"OCCUPATION\" actualValue=\"Transp.\" weight=\".065\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\"-.069\" \t\"HOUSEHOLD_SIZE\" actualValue=\"3\" weight=\"-.223\" \n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032968_-615820808", + "id": "20230307-171032_2044162567", + "dateCreated": "2022-06-14T17:03:43+0000", + "dateStarted": "2023-03-07T17:35:42+0000", + "dateFinished": "2023-03-07T17:35:42+0000", + "status": "FINISHED", + "$$hashKey": "object:62" + }, + { + "title": "Clean up model", + "text": "%script\r\rBEGIN DBMS_DATA_MINING.DROP_MODEL(model_name => 'DT_CLASSIFICATION_MODEL');\rEXCEPTION WHEN others THEN null; END;\r", + "user": "OMLUSER", + "dateUpdated": "2023-03-07T17:35:42+0000", + "progress": 0, + "config": { + "editorSetting": { + "language": "plsql", + "editOnDblClick": false + }, + "colWidth": 12, + "editorMode": "ace/mode/plsql", + "fontSize": 9, + "title": true, + "results": {}, + "enabled": true + }, + "settings": { + "params": {}, + "forms": {} + }, + "results": { + "code": "SUCCESS", + "msg": [ + { + "type": "TEXT", + "data": "\nPL/SQL procedure successfully completed.\n\n" + } + ] + }, + "apps": [], + "interrupted": false, + "runtimeInfos": {}, + "progressUpdateIntervalMs": 500, + "jobName": "paragraph_1678209032968_-736464126", + "id": "20230307-171032_1345296196", + "dateCreated": "2022-06-28T20:18:24+0000", + "dateStarted": "2023-03-07T17:35:43+0000", + "dateFinished": "2023-03-07T17:35:43+0000", + "status": 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