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\n\nLast Updated Date - September 2021
\nCopyright (c) 2021 Oracle Corporation
\nThe Universal Permissive License (UPL), Version 1.0
\n\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_1016371808","id":"20211001-190306_68494635","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:48+0000","dateFinished":"2021-09-22T20:18:48+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:386"},{"text":"%md\n","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:48+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"md","editOnDblClick":false},"editorMode":"ace/mode/markdown"},"settings":{"params":{},"forms":{}},"interrupted":false,"jobName":"paragraph_1633114986450_-1060179910","id":"20211001-190306_1958773210","dateCreated":"2021-04-23T13:27:18+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":false,"$$hashKey":"object:387"}],"id":"79628","noteParams":{},"noteForms":{},"angularObjects":{"ORA221A1BC345:OMLUSER:78556":[],"ORA36CEFD120D:OMLUSER:78556":[],"ORA9405AD2E1E:OMLUSER:78556":[],"MDW276C5A6A4D:shared_process":[]},"config":{"looknfeel":"default","personalizedMode":"false"},"info":{},"name":"Lab 1: Get Started with OML4Py on Autonomous Database"} \ No newline at end of file +{"paragraphs":[{"text":"%md\n## **Initiate a call to the Python interpreter**\nTo run Python commands in a notebook, you must first connect to the Python interpreter. This occurs as a result of running your first `%python` paragraph. To use OML4Py, you must import the `oml` module, which automatically establishes a connection to your database. In an Oracle Machine Learning notebook, you can add multiple paragraphs, and each paragraph can be connected to different interpreters such as SQL or Python. This example shows you how to:\n\n* Connect to a Python interpreter to run Python commands in a notebook\n* Import the Python modules—oml, pandas, numpy, and matplotlib\n* Check if the oml module is connected to the database\n\nNote: `z` is a reserved keyword and must not be used as a variable in %python paragraphs in Oracle Machine Learning Notebooks. You will see `z.show()` fucntion used in the examples to display Python object and proxy object content.\n","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:30+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"md","editOnDblClick":false},"editorMode":"ace/mode/markdown","editorHide":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"
Initiate a call to the Python interpreter
\nTo run Python commands in a notebook, you must first connect to the Python interpreter. This occurs as a result of running your first %python paragraph. To use OML4Py, you must import the oml module, which automatically establishes a connection to your database. In an Oracle Machine Learning notebook, you can add multiple paragraphs, and each paragraph can be connected to different interpreters such as SQL or Python. This example shows you how to:
- \n
- Connect to a Python interpreter to run Python commands in a notebook \n
- Import the Python modules—oml, pandas, numpy, and matplotlib \n
- Check if the oml module is connected to the database \n
Note: z is a reserved keyword and must not be used as a variable in %python paragraphs in Oracle Machine Learning Notebooks. You will see z.show() fucntion used in the examples to display Python object and proxy object content.
Verify Connection to the Autonomous Database
\nUsing the default interpreter bindings, OML Notebooks automatically establishes a database connection for the notebook.
\nTo verify the Python interpreter has established a database connection through the oml module, run the command shown below. If the notebook is connected, the command returns True.
View Help Files
\nThe Python help function is used to display the documentation of packages, modules, functions, classes, and keywords. The help function has the following syntax:
\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_563552517","id":"20211001-190306_759056463","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:44+0000","dateFinished":"2021-09-22T20:18:44+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:380"},{"text":"%python\n\nhelp([object])","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:44+0000","config":{"editorSetting":{"language":"text","editOnDblClick":false},"colWidth":12,"editorMode":"ace/mode/undefined","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Help on list object:\n\nclass list(object)\n | list(iterable=(), /)\n | \n | Built-in mutable sequence.\n | \n | If no argument is given, the constructor creates a new empty list.\n | The argument must be an iterable if specified.\n | \n | Methods defined here:\n | \n | __add__(self, value, /)\n | Return self+value.\n | \n | __contains__(self, key, /)\n | Return key in self.\n | \n | __delitem__(self, key, /)\n | Delete self[key].\n | \n | __eq__(self, value, /)\n | Return self==value.\n | \n | __ge__(self, value, /)\n | Return self>=value.\n | \n | __getattribute__(self, name, /)\n | Return getattr(self, name).\n | \n | __getitem__(...)\n | x.__getitem__(y) <==> x[y]\n | \n | __gt__(self, value, /)\n | Return self>value.\n | \n | __iadd__(self, value, /)\n | Implement self+=value.\n | \n | __imul__(self, value, /)\n | Implement self*=value.\n | \n | __init__(self, /, *args, **kwargs)\n | Initialize self. See help(type(self)) for accurate signature.\n | \n | __iter__(self, /)\n | Implement iter(self).\n | \n | __le__(self, value, /)\n | Return self<=value.\n | \n | __len__(self, /)\n | Return len(self).\n | \n | __lt__(self, value, /)\n | Return selfFor example,
\n- \n
- To view the help files for the
oml.createfunction, type the below code. \n
\n
To view the help files for oml module, type the code below.
Learn More
\n\nLast Updated Date - September 2021
\nCopyright (c) 2021 Oracle Corporation
\nThe Universal Permissive License (UPL), Version 1.0
\n\n"}]},"interrupted":false,"jobName":"paragraph_1633114986450_1016371808","id":"20211001-190306_68494635","dateCreated":"2021-03-22T18:18:00+0000","dateStarted":"2021-09-22T20:18:48+0000","dateFinished":"2021-09-22T20:18:48+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:386"},{"text":"%md\n","user":"OMLUSER","dateUpdated":"2021-09-22T20:18:48+0000","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"md","editOnDblClick":false},"editorMode":"ace/mode/markdown"},"settings":{"params":{},"forms":{}},"interrupted":false,"jobName":"paragraph_1633114986450_-1060179910","id":"20211001-190306_1958773210","dateCreated":"2021-04-23T13:27:18+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":false,"$$hashKey":"object:387"}],"id":"79628","noteParams":{},"noteForms":{},"angularObjects":{"ORA221A1BC345:OMLUSER:78556":[],"ORA36CEFD120D:OMLUSER:78556":[],"ORA9405AD2E1E:OMLUSER:78556":[],"MDW276C5A6A4D:shared_process":[]},"config":{"looknfeel":"default","personalizedMode":"false"},"info":{},"name":"Lab 1: Get Started with OML4Py on Autonomous Database"} \ No newline at end of file diff --git a/machine-learning/labs/oml4py-live-labs/Lab2_Select_and_manipulate_data_using_the_Transparency_Layer.json b/machine-learning/labs/oml4py-live-labs/Lab2_Select_and_manipulate_data_using_the_Transparency_Layer.json index 1ea73aba..5d2a3527 100755 --- a/machine-learning/labs/oml4py-live-labs/Lab2_Select_and_manipulate_data_using_the_Transparency_Layer.json +++ b/machine-learning/labs/oml4py-live-labs/Lab2_Select_and_manipulate_data_using_the_Transparency_Layer.json @@ -84,7 +84,7 @@ "commited": true }, { - "text": "%md\n---\n1.2. Here, you load the IRIS data and combine the target and predictors into a single DataFrame, which matches the form the data would have as a database table. You use the `oml.push` function to load this Pandas DataFrame into the database, which creates a temporary table and returns a proxy object that you assign to IRIS_TMP.\n\nSuch temporary tables will be automatically deleted when the database connection is terminated unless saved in a datastore. You learn more about datastore in lab 4.\nIn OML Notebooks, you use the notebook-context `z.show` method to display Python objects and proxy object content. \n \nDisplay the first few rows of IRIS_TMP using `z.show` for displaying DataFrame results in the notebook viewer.", + "text": "%md\n\n1.2. Here, you load the IRIS data and combine the target and predictors into a single DataFrame, which matches the form the data would have as a database table. You use the `oml.push` function to load this Pandas DataFrame into the database, which creates a temporary table and returns a proxy object that you assign to IRIS_TMP.\n\nSuch temporary tables will be automatically deleted when the database connection is terminated unless saved in a datastore. You learn more about datastore in lab 4.\nIn OML Notebooks, you use the notebook-context `z.show` method to display Python objects and proxy object content. \n \nDisplay the first few rows of IRIS_TMP using `z.show` for displaying DataFrame results in the notebook viewer.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:18+0000", "config": { @@ -338,7 +338,7 @@ "commited": true }, { - "text": "%md\n---\n1.4. Use the transparency layer function `shape` to view the shape, or number of rows and columns, of the IRIS table.", + "text": "%md\n\n1.4. Use the transparency layer function `shape` to view the shape, or number of rows and columns, of the IRIS table.", "user": "OMLUSER", "dateUpdated": "2021-10-01T19:04:33+0000", "config": { @@ -416,7 +416,7 @@ "commited": true }, { - "text": "%md\n---\n1.5. Use the transparency layer function `head()` to view the first three rows of the IRIS table.\n", + "text": "%md\n\n1.5. Use the transparency layer function `head()` to view the first three rows of the IRIS table.\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:23+0000", "config": { @@ -493,7 +493,7 @@ "commited": true }, { - "text": "%md\n---\n1.6. Use the transparency layer function `describe()` to calculate descriptive statistics that summarize the central tendency, dispersion, and shape of the IRIS table in each numeric column.\n", + "text": "%md\n\n1.6. Use the transparency layer function `describe()` to calculate descriptive statistics that summarize the central tendency, dispersion, and shape of the IRIS table in each numeric column.\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:25+0000", "config": { @@ -706,7 +706,7 @@ "commited": true }, { - "text": "%md\n---\n2.2 Run the following script to select specific columns by name and return the first three records with the specified column names.\n", + "text": "%md\n\n2.2 Run the following script to select specific columns by name and return the first three records with the specified column names.\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:29+0000", "config": { @@ -812,7 +812,7 @@ "commited": true }, { - "text": "%md\n---\n2.3. Select the first four columns in `CUST_DF` using an index range. Note that Python uses `0` as a starting element for indexing. The starting element is the first column, `MARITAL_STATUS`. The second element is the second column, `STATE`, and so on. The script returns the columns by specified by the index range.", + "text": "%md\n\n2.3. Select the first four columns in `CUST_DF` using an index range. Note that Python uses `0` as a starting element for indexing. The starting element is the first column, `MARITAL_STATUS`. The second element is the second column, `STATE`, and so on. The script returns the columns by specified by the index range.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:30+0000", "config": { @@ -922,7 +922,7 @@ "commited": true }, { - "text": "%md\n---\n2.4. Run the following script to select columns by data type, specifying the data type as `oml.string`. The script returns the MARITAL_STATUS, STATE, CUSTOMER_ID, GENDER, PROFESSION, REGION, BUY_INSURANCE, LTV_BIN, FIRST_NAME, LAST_NAME columns.", + "text": "%md\n\n2.4. Run the following script to select columns by data type, specifying the data type as `oml.string`. The script returns the MARITAL_STATUS, STATE, CUSTOMER_ID, GENDER, PROFESSION, REGION, BUY_INSURANCE, LTV_BIN, FIRST_NAME, LAST_NAME columns.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:32+0000", "config": { @@ -1148,7 +1148,7 @@ "commited": true }, { - "text": "%md\n---\n3.2. This step shows an example of a compound row selection using the `OR` filtering condition, denoted by the `|` symbol. Run the following scripts to select all rows in which `MORTGAGE_AMOUNT` is `less than 1500` OR `STATE` is `equal to MA`:", + "text": "%md\n\n3.2. This step shows an example of a compound row selection using the `OR` filtering condition, denoted by the `|` symbol. Run the following scripts to select all rows in which `MORTGAGE_AMOUNT` is `less than 1500` OR `STATE` is `equal to MA`:", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:35+0000", "config": { @@ -1288,7 +1288,7 @@ "commited": true }, { - "text": "%md\n---\n3.3. This step shows an example of a compound row selection using `AND`, denoted by the `\u0026` symbol. Run the following script to select all rows in which `MORTGAGE_AMOUNT` is `less than 1500` AND `BANK_FUNDS` is `greater than 5000`:\n", + "text": "%md\n\n3.3. This step shows an example of a compound row selection using `AND`, denoted by the `\u0026` symbol. Run the following script to select all rows in which `MORTGAGE_AMOUNT` is `less than 1500` AND `BANK_FUNDS` is `greater than 5000`:\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:36+0000", "config": { @@ -1468,7 +1468,7 @@ "commited": true }, { - "text": "%md\n---\n4.1.1. In this step, you use the `append()` function to append an `oml.Float` series object to another, and then append an `oml.DataFrame` object to another.\n\n**Note:** An `oml.Float` is numeric series data class that represents a single column of `NUMBER`, `BINARY_DOUBLE`, or `BINARY_FLOAT` database data types.\n", + "text": "%md\n\n4.1.1. In this step, you use the `append()` function to append an `oml.Float` series object to another, and then append an `oml.DataFrame` object to another.\n\n**Note:** An `oml.Float` is numeric series data class that represents a single column of `NUMBER`, `BINARY_DOUBLE`, or `BINARY_FLOAT` database data types.\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:38+0000", "config": { @@ -1579,7 +1579,7 @@ "commited": true }, { - "text": "%md\n---\n### 4.2. Use the replace () function\n\n4.2.1. In this step, you use the `replace()` function to replace a particular value in columns of an OML dataframe. ", + "text": "%md\n\n### 4.2. Use the replace () function\n\n4.2.1. In this step, you use the `replace()` function to replace a particular value in columns of an OML dataframe. ", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:40+0000", "config": { @@ -1865,7 +1865,7 @@ "commited": true }, { - "text": "%md\n---\n4.3.2. This step shows how to create an `oml.Float` object by taking product of `MORTGAGE_AMOUNT` column and `NUM_MORTGAGES` column, and then concatenate it with the preivous `oml.DataFrame`, with a new column name `MORTGAGE_TOTAL`. \n**Note:** An oml.Float is a numeric series data class that represents a single column of `NUMBER`, `BINARY_DOUBLE`, or `BINARY_FLOAT` database data types.\n\n", + "text": "%md\n\n4.3.2. This step shows how to create an `oml.Float` object by taking product of `MORTGAGE_AMOUNT` column and `NUM_MORTGAGES` column, and then concatenate it with the preivous `oml.DataFrame`, with a new column name `MORTGAGE_TOTAL`. \n**Note:** An oml.Float is a numeric series data class that represents a single column of `NUMBER`, `BINARY_DOUBLE`, or `BINARY_FLOAT` database data types.\n\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:44+0000", "config": { @@ -1974,7 +1974,7 @@ "commited": true }, { - "text": "%md\n---\n4.3.3. Concatenate object CUST_DF with ID2 and turn on automatic name conflict resolution. In this example, `auto_name\u003dTrue` controls whether to call automatic name conflict resolution if one or more column names are duplicates in the two data frames:\n", + "text": "%md\n\n4.3.3. Concatenate object CUST_DF with ID2 and turn on automatic name conflict resolution. In this example, `auto_name\u003dTrue` controls whether to call automatic name conflict resolution if one or more column names are duplicates in the two data frames:\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:45+0000", "config": { @@ -2081,7 +2081,7 @@ "commited": true }, { - "text": "%md\n---\n4.3.4. Run the following script to concatenate multiple OML data objects and perform customized renaming. ", + "text": "%md\n\n4.3.4. Run the following script to concatenate multiple OML data objects and perform customized renaming. ", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:47+0000", "config": { @@ -2299,7 +2299,7 @@ "commited": true }, { - "text": "%md\n---\n4.4.2. Run the following script to perform a left outer join on SUBSET1 with the `oml.DataFrame` object on the shared column `CUSTOMER_ID` and apply the suffixes `.l` and `.r` to column names on the left and right side, respectively.\n", + "text": "%md\n\n4.4.2. Run the following script to perform a left outer join on SUBSET1 with the `oml.DataFrame` object on the shared column `CUSTOMER_ID` and apply the suffixes `.l` and `.r` to column names on the left and right side, respectively.\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:55+0000", "config": { @@ -2583,7 +2583,7 @@ "commited": true }, { - "text": "%md\n---\n4.5.2 Run the script below to check the number of rows with `NULL` for `MARITAL_STATUS` in the `CUSTOMER_INSURANCE_LTV_NOISE` proxy object. Then we check the top 5 records.", + "text": "%md\n\n4.5.2 Run the script below to check the number of rows with `NULL` for `MARITAL_STATUS` in the `CUSTOMER_INSURANCE_LTV_NOISE` proxy object. Then we check the top 5 records.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:23:58+0000", "config": { @@ -2779,7 +2779,7 @@ "commited": true }, { - "text": "%md\n---\n4.5.3. Run the script to drop rows with any missing values and create a new proxy object `RES_DF`. Verify its shape and first 100 records.", + "text": "%md\n\n4.5.3. Run the script to drop rows with any missing values and create a new proxy object `RES_DF`. Verify its shape and first 100 records.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:24:00+0000", "config": { @@ -2976,7 +2976,7 @@ "commited": true }, { - "text": "%md\n---\n4.5.4. Run the following script to drop rows with missing values for a particular subset of colummns and create a new proxy object `RES_DF`. Verify its shape and first 100 records. ", + "text": "%md\n\n4.5.4. Run the following script to drop rows with missing values for a particular subset of colummns and create a new proxy object `RES_DF`. Verify its shape and first 100 records. ", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:24:01+0000", "config": { @@ -3313,7 +3313,7 @@ "commited": true }, { - "text": "%md\n---\n4.5.6. Use the `drop_duplicates()` function to drop duplicate rows. In this case, because we are requesting only the columns `STATE` and `MARITAL_STATUS`, the final unique combination of these columns has a much smaller count than the original data. ", + "text": "%md\n\n4.5.6. Use the `drop_duplicates()` function to drop duplicate rows. In this case, because we are requesting only the columns `STATE` and `MARITAL_STATUS`, the final unique combination of these columns has a much smaller count than the original data. ", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:24:04+0000", "config": { @@ -3452,7 +3452,7 @@ "commited": true }, { - "text": "%md\n---\n4.5.7. Run the following script to drop specific columns and create a new proxy object `DROPPED_DF`. Verify its shape and first 100 records.", + "text": "%md\n\n4.5.7. Run the following script to drop specific columns and create a new proxy object `DROPPED_DF`. Verify its shape and first 100 records.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:24:06+0000", "config": { @@ -3722,7 +3722,7 @@ "commited": true }, { - "text": "%md\n---\n5.2. Run the following script to split the data set into samples of 20% and 80% size.", + "text": "%md\n\n5.2. Run the following script to split the data set into samples of 20% and 80% size.", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:11:29+0000", "config": { @@ -3800,7 +3800,7 @@ "commited": true }, { - "text": "%md\n---\n5.3. Run the following script to perform stratified sampling on the column `BUY_INSURANCE`. In this example, the column in which you perform stratified sampling is `BUY_INSURANCE`. Stratified sampling divides the data into different groups based on shared characteristics prior to sampling, ensuring that members from each subgroup is included in the analysis.", + "text": "%md\n\n5.3. Run the following script to perform stratified sampling on the column `BUY_INSURANCE`. In this example, the column in which you perform stratified sampling is `BUY_INSURANCE`. Stratified sampling divides the data into different groups based on shared characteristics prior to sampling, ensuring that members from each subgroup is included in the analysis.", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:11:40+0000", "config": { @@ -3878,7 +3878,7 @@ "commited": true }, { - "text": "%md\n---\n5.4. Verify that the stratified sampling generates splits in which all of the different categories of `BUY_INSURANCE` (\u0027No\u0027 and \u0027Yes\u0027) are present in each split", + "text": "%md\n\n5.4. Verify that the stratified sampling generates splits in which all of the different categories of `BUY_INSURANCE` (\u0027No\u0027 and \u0027Yes\u0027) are present in each split", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:11:48+0000", "config": { @@ -3957,7 +3957,7 @@ "commited": true }, { - "text": "%md\n---\n5.5. Split by computing hash on the target column.", + "text": "%md\n\n5.5. Split by computing hash on the target column.", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:11:56+0000", "config": { @@ -4035,7 +4035,7 @@ "commited": true }, { - "text": "%md\n---\n5.6. Verify that the different categories of `BUY_INSURANCE` are present in only one of the splits generated by hashing on the category column:", + "text": "%md\n\n5.6. Verify that the different categories of `BUY_INSURANCE` are present in only one of the splits generated by hashing on the category column:", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:12:05+0000", "config": { @@ -4113,7 +4113,7 @@ "commited": true }, { - "text": "%md\n---\n5.7. Split the data randomly into 4 consecutive folds using the `KFold` function.", + "text": "%md\n\n5.7. Split the data randomly into 4 consecutive folds using the `KFold` function.", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:12:12+0000", "config": { @@ -4304,7 +4304,7 @@ "commited": true }, { - "text": "%md\n---\n6.2. Use the `crosstab` function to find the categories that the most entries belonged to. This example shows how to use the `crosstab` function to find the count of state and gender customers in descending order in the CUST_DF OML frame", + "text": "%md\n\n6.2. Use the `crosstab` function to find the categories that the most entries belonged to. This example shows how to use the `crosstab` function to find the count of state and gender customers in descending order in the CUST_DF OML frame", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:12:38+0000", "config": { @@ -4412,7 +4412,7 @@ "commited": true }, { - "text": "%md\n---\n6.3. Use the `crosstab` function to find the ratio of the genders for State and across entries in the `CUST_DF` dataframe.", + "text": "%md\n\n6.3. Use the `crosstab` function to find the ratio of the genders for State and across entries in the `CUST_DF` dataframe.", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:12:49+0000", "config": { @@ -4520,7 +4520,7 @@ "commited": true }, { - "text": "%md\n---\n6.4. Use the `pivot_table` function to find the mean bank funds across all State and gender combinations in the `CUST_DF` dataframe.", + "text": "%md\n\n6.4. Use the `pivot_table` function to find the mean bank funds across all State and gender combinations in the `CUST_DF` dataframe.", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:13:01+0000", "config": { @@ -4628,7 +4628,7 @@ "commited": true }, { - "text": "%md\n---\n6.5. Use `pivot_table` function to find the maximum and minimum bank funds for every State and gender combination (plus the overall for all genders) in the `CUST_DF` dataframe:\n", + "text": "%md\n\n6.5. Use `pivot_table` function to find the maximum and minimum bank funds for every State and gender combination (plus the overall for all genders) in the `CUST_DF` dataframe:\n", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:13:10+0000", "config": { @@ -4818,7 +4818,7 @@ "commited": true }, { - "text": "%md\n---\n7.2. Run the following script to render the data in a histogram for `MORTGAGE_AMOUNT`.", + "text": "%md\n\n7.2. Run the following script to render the data in a histogram for `MORTGAGE_AMOUNT`.", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:13:33+0000", "config": { @@ -5010,7 +5010,7 @@ "commited": true }, { - "text": "%md\n---\n8.1.2. Run the following script to view all persistent database tables that match the name `IRIS`.", + "text": "%md\n\n8.1.2. Run the following script to view all persistent database tables that match the name `IRIS`.", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:14:05+0000", "config": { @@ -5167,7 +5167,7 @@ "commited": true }, { - "text": "%md\n---\n9.2. To close the cursor, run `cr.close`.\n", + "text": "%md\n\n9.2. To close the cursor, run `cr.close`.\n", "user": "OMLUSER", "dateUpdated": "2021-09-24T17:18:30+0000", "config": { diff --git a/machine-learning/labs/oml4py-live-labs/Lab3_Use_in_database_algorithms_and_models.json b/machine-learning/labs/oml4py-live-labs/Lab3_Use_in_database_algorithms_and_models.json index ada4e6ec..4018b3dd 100755 --- a/machine-learning/labs/oml4py-live-labs/Lab3_Use_in_database_algorithms_and_models.json +++ b/machine-learning/labs/oml4py-live-labs/Lab3_Use_in_database_algorithms_and_models.json @@ -147,7 +147,7 @@ "commited": true }, { - "text": "%md\n---\n2.2. Run the following command to display the first few rows of table `CUST_DF`:", + "text": "%md\n\n2.2. Run the following command to display the first few rows of table `CUST_DF`:", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:09+0000", "config": { @@ -283,7 +283,7 @@ "commited": true }, { - "text": "%md\n---\n2.3. Run the following script to randomly split and select the data into 60% for train and 40% for test.", + "text": "%md\n\n2.3. Run the following script to randomly split and select the data into 60% for train and 40% for test.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:11+0000", "config": { @@ -356,7 +356,7 @@ "commited": true }, { - "text": "%md\n---\n2.4. Now, build a GLM regression model for predicting lifetime value `LTV` using the `oml.glm` function.\n\nThis method runs the `oml.glm` algorithm in-database using the given settings. The settings are supplied as key-value pairs. In this example, feature generation and feature selection are specified.\n\n**Note:** For a complete list of settings, refer to the OML4Py product documentation. You may also refer to the `oml.glm` help file.\n", + "text": "%md\n\n2.4. Now, build a GLM regression model for predicting lifetime value `LTV` using the `oml.glm` function.\n\nThis method runs the `oml.glm` algorithm in-database using the given settings. The settings are supplied as key-value pairs. In this example, feature generation and feature selection are specified.\n\n**Note:** For a complete list of settings, refer to the OML4Py product documentation. You may also refer to the `oml.glm` help file.\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:13+0000", "config": { @@ -434,7 +434,7 @@ "commited": true }, { - "text": "%md\n---\n2.5. Run the following script to view model fit details to understand the key statistics of the model. Locate the values of Root Mean Square Error `ROOT_MEAN_SQ` and R-squared `R_SQ` from the output. RMSE and R-squared are used to evaluate baseline performance of the model.\n\n* RMSE is a measure of the differences between values predicted by a model and the values observed. A good model should have a low RMSE. But at the same time, a model with very low RMSE has the potential to overfit.\n* R-Squared is a statistical measure that represents the goodness of fit of a regression model. The ideal value for R-squared is 1. The closer the value of R-squared is to 1, the better the model fit. For instance, if the R-squared of a model is 0.50, then approximately half of the observed variation can be explained by the model\u0027s inputs", + "text": "%md\n\n2.5. Run the following script to view model fit details to understand the key statistics of the model. Locate the values of Root Mean Square Error `ROOT_MEAN_SQ` and R-squared `R_SQ` from the output. RMSE and R-squared are used to evaluate baseline performance of the model.\n\n* RMSE is a measure of the differences between values predicted by a model and the values observed. A good model should have a low RMSE. But at the same time, a model with very low RMSE has the potential to overfit.\n* R-Squared is a statistical measure that represents the goodness of fit of a regression model. The ideal value for R-squared is 1. The closer the value of R-squared is to 1, the better the model fit. For instance, if the R-squared of a model is 0.50, then approximately half of the observed variation can be explained by the model\u0027s inputs", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:30+0000", "config": { @@ -541,7 +541,7 @@ "commited": true }, { - "text": "%md\n---\n2.6. Run the following command to display and view the model coefficients:", + "text": "%md\n\n2.6. Run the following command to display and view the model coefficients:", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:32+0000", "config": { @@ -652,7 +652,7 @@ "commited": true }, { - "text": "%md\n---\n2.7. Run the following script to make predictions using the test data and display the results. From the RES_DF DataFrame proxy object, the predicted values and lifetime value are displayed in the `PREDICTION` and `LTV` columns respectively.\n", + "text": "%md\n\n2.7. Run the following script to make predictions using the test data and display the results. From the RES_DF DataFrame proxy object, the predicted values and lifetime value are displayed in the `PREDICTION` and `LTV` columns respectively.\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:34+0000", "config": { @@ -760,7 +760,7 @@ "commited": true }, { - "text": "%md\n---\n2.8. Run the following command to plot the predicted versus the actual years of residence and then click the **Scatter Chart** icon to see the visualization. Click **Settings** to see how the plot was specified.", + "text": "%md\n\n2.8. Run the following command to plot the predicted versus the actual years of residence and then click the **Scatter Chart** icon to see the visualization. Click **Settings** to see how the plot was specified.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:36+0000", "config": { @@ -868,7 +868,7 @@ "commited": true }, { - "text": "%md\n---\n2.9. Using matplotlib, plot the predicted and actual years of residence and visually compare it against the perfect fit line, `y\u003dx` in `red`. The plot indicates how far the prediction deviated from actual value, which is known as the prediction error. Ideally, the predictions will converge to the perfect fit line.", + "text": "%md\n\n2.9. Using matplotlib, plot the predicted and actual years of residence and visually compare it against the perfect fit line, `y\u003dx` in `red`. The plot indicates how far the prediction deviated from actual value, which is known as the prediction error. Ideally, the predictions will converge to the perfect fit line.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:38+0000", "config": { @@ -908,7 +908,7 @@ "commited": true }, { - "text": "%md\n---\n\n\n2.10. Run the following script to plot the residuals using matplotlib. Ideally prediction errors (the residuals) would show up as small random noise around the perfect fit `y\u003d0` line in `red`. When there are strange patterns found, usually that means that some other confounding effect is at play, and further investigation is required to identify more attributes that might not be considered currently.", + "text": "%md\n\n\n\n2.10. Run the following script to plot the residuals using matplotlib. Ideally prediction errors (the residuals) would show up as small random noise around the perfect fit `y\u003d0` line in `red`. When there are strange patterns found, usually that means that some other confounding effect is at play, and further investigation is required to identify more attributes that might not be considered currently.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:39+0000", "config": { @@ -956,7 +956,7 @@ "commited": true }, { - "text": "%md\n---", + "text": "%md\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:40+0000", "config": { @@ -1071,7 +1071,7 @@ "commited": true }, { - "text": "%md\n---\n2.11. Run the following script to calculate the RMSE manually on the prediction results on the testing test and the R- squared on the testing set using the score method.\n\n**Note:** Both the RMSE and R-squared calculations are similar to the values produced by `oml.glm`.", + "text": "%md\n\n2.11. Run the following script to calculate the RMSE manually on the prediction results on the testing test and the R- squared on the testing set using the score method.\n\n**Note:** Both the RMSE and R-squared calculations are similar to the values produced by `oml.glm`.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:45+0000", "config": { @@ -1222,7 +1222,7 @@ "commited": true }, { - "text": "%md\n---\n3.2. To display and view the K-means model details, run the following command:", + "text": "%md\n\n3.2. To display and view the K-means model details, run the following command:", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:53+0000", "config": { @@ -1300,7 +1300,7 @@ "commited": true }, { - "text": "%md\n---\n3.3. To view the cluster details, run the following command. The command displays the cluster details for all clusters in the hierarchy with row counts and dispersion.\nThe dispersion value is a measure of how compact or how spread out the data is within a cluster. The dispersion value is a number greater than 0. The lower the dispersion value, the more compact the cluster, that is, the data points are closer to the centroid of the cluster. A larger dispersion value indicates that the data points are more disperse or spread out from the centroid.\n", + "text": "%md\n\n3.3. To view the cluster details, run the following command. The command displays the cluster details for all clusters in the hierarchy with row counts and dispersion.\nThe dispersion value is a measure of how compact or how spread out the data is within a cluster. The dispersion value is a number greater than 0. The lower the dispersion value, the more compact the cluster, that is, the data points are closer to the centroid of the cluster. A larger dispersion value indicates that the data points are more disperse or spread out from the centroid.\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:55+0000", "config": { @@ -1410,7 +1410,7 @@ "commited": true }, { - "text": "%md\n---\n3.4. Run the following script to display the taxonomy. The taxonomy shows the hierarchy of the child clusters in relationship to the parent clusters.", + "text": "%md\n\n3.4. Run the following script to display the taxonomy. The taxonomy shows the hierarchy of the child clusters in relationship to the parent clusters.", "user": "OMLUSER", "dateUpdated": "2021-10-01T19:24:47+0000", "config": { @@ -1517,7 +1517,7 @@ "commited": true }, { - "text": "%md\n---\n3.5. Run the following command to predict the cluster membership. The `supplemental_cols` argument carries the target column to the output to retain the relationship between the predictions and their original preditor values. These predictors may include a case id, for example to join with other tables, or multiple (or all) columns of the scoring data. You should be aware that unlike Pandas DataFrames, which are explicitly ordered in memory, results from relational databases do not have a specific order unless explicitly specified by an `ORDER BY` clause. As such, you cannot rely on results to maintain the same order across different data sets (tables and DataFrame proxy objects).", + "text": "%md\n\n3.5. Run the following command to predict the cluster membership. The `supplemental_cols` argument carries the target column to the output to retain the relationship between the predictions and their original preditor values. These predictors may include a case id, for example to join with other tables, or multiple (or all) columns of the scoring data. You should be aware that unlike Pandas DataFrames, which are explicitly ordered in memory, results from relational databases do not have a specific order unless explicitly specified by an `ORDER BY` clause. As such, you cannot rely on results to maintain the same order across different data sets (tables and DataFrame proxy objects).", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:50:59+0000", "config": { @@ -1590,7 +1590,7 @@ "commited": true }, { - "text": "%md\n---\n3.6. Run the following command to view the pred computed in step 5.", + "text": "%md\n\n3.6. Run the following command to view the pred computed in step 5.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:51:01+0000", "config": { @@ -1727,7 +1727,7 @@ "commited": true }, { - "text": "%md\n---\n3.7. Run the following command to view the cluster results using a matplotlib scatterplot. We are using a sample of 10% of the total data (by using `sample(0.1)`) to make it more visible.", + "text": "%md\n\n3.7. Run the following command to view the cluster results using a matplotlib scatterplot. We are using a sample of 10% of the total data (by using `sample(0.1)`) to make it more visible.", "user": "OMLUSER", "dateUpdated": "2021-09-22T20:59:36+0000", "config": { @@ -1878,7 +1878,7 @@ "commited": true }, { - "text": "%md\n---\n4.2. Build a partitioned model using the SVM algorithm to predict `LTV`, partitioned by `GENDER`. The script builds an SVM partitioned model. Scroll down the notebook paragraph for complete details of the model.\n", + "text": "%md\n\n4.2. Build a partitioned model using the SVM algorithm to predict `LTV`, partitioned by `GENDER`. The script builds an SVM partitioned model. Scroll down the notebook paragraph for complete details of the model.\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T19:03:34+0000", "config": { @@ -1956,7 +1956,7 @@ "commited": true }, { - "text": "%md\n---\n4.3. Run the following script to predict on the test set and display prediction result. Note the use of the top level model only.\n\nThe script makes predictions using the test data returning an OML DataFrame proxy object and displays the result in a table. The predicted values are listed in the PREDICTION column.", + "text": "%md\n\n4.3. Run the following script to predict on the test set and display prediction result. Note the use of the top level model only.\n\nThe script makes predictions using the test data returning an OML DataFrame proxy object and displays the result in a table. The predicted values are listed in the PREDICTION column.", "user": "OMLUSER", "dateUpdated": "2021-09-22T19:03:40+0000", "config": { @@ -2094,7 +2094,7 @@ "commited": true }, { - "text": "%md\n---\n4.4. Run the following command to show the model global statistics for each partitioned sub-model. The partition name column contains the values from the partition column. If multiple columns were specified, then there would be one column for each with corresponding value.\n", + "text": "%md\n\n4.4. Run the following command to show the model global statistics for each partitioned sub-model. The partition name column contains the values from the partition column. If multiple columns were specified, then there would be one column for each with corresponding value.\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T19:03:42+0000", "config": { @@ -2202,7 +2202,7 @@ "commited": true }, { - "text": "%md\n---\nUse Python to score data and display prediction details for every customer by using the `topN_attrs \u003d True` argument to the `.predict` call. The prediction details return the actual value of attributes used for scoring and the relative importance of the attributes in determining the score of each customer, in descending order of importance. ", + "text": "%md\n\nUse Python to score data and display prediction details for every customer by using the `topN_attrs \u003d True` argument to the `.predict` call. The prediction details return the actual value of attributes used for scoring and the relative importance of the attributes in determining the score of each customer, in descending order of importance. ", "user": "OMLUSER", "dateUpdated": "2021-09-22T19:03:44+0000", "config": { @@ -2325,7 +2325,7 @@ "commited": true }, { - "text": "%md\n---\n4.5. Run the following command to materialize the test dataset. The `materialize` method creates a new database table based on the content specified by an OML DataFrame proxy object.\n\nHere, you materialize the data to table `TEST_DATA` so that it can be queried from SQL.", + "text": "%md\n\n4.5. Run the following command to materialize the test dataset. The `materialize` method creates a new database table based on the content specified by an OML DataFrame proxy object.\n\nHere, you materialize the data to table `TEST_DATA` so that it can be queried from SQL.", "user": "OMLUSER", "dateUpdated": "2021-09-22T19:03:56+0000", "config": { @@ -2398,7 +2398,7 @@ "commited": true }, { - "text": "%md\n---\n4.6. Use SQL to score data and display prediction details.", + "text": "%md\n\n4.6. Use SQL to score data and display prediction details.", "user": "OMLUSER", "dateUpdated": "2021-09-22T19:03:59+0000", "config": { @@ -2581,7 +2581,7 @@ "commited": true }, { - "text": "%md\n---\n5.2. Split the data set into train and test sets.", + "text": "%md\n\n5.2. Split the data set into train and test sets.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:15:22+0000", "config": { @@ -2654,7 +2654,7 @@ "commited": true }, { - "text": "%md\n---\n5.3. Train an SVM model.", + "text": "%md\n\n5.3. Train an SVM model.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:15:28+0000", "config": { @@ -2732,7 +2732,7 @@ "commited": true }, { - "text": "%md\n---\n5.4. Create the MLX Global Feature Importance explainer gfi, using the `f1_weighted` metric.", + "text": "%md\n\n5.4. Create the MLX Global Feature Importance explainer gfi, using the `f1_weighted` metric.", "user": "OMLUSER", "dateUpdated": "2021-09-22T19:04:17+0000", "config": { @@ -2805,7 +2805,7 @@ "commited": true }, { - "text": "%md\n---\n5.5. Run the explainer `gfi.explain` to generate the global feature importance for the test data. The explainer returns the list of attributes in order of importance to the model.\n\nNote: this can take almost a minute, depending on your configuration.", + "text": "%md\n\n5.5. Run the explainer `gfi.explain` to generate the global feature importance for the test data. The explainer returns the list of attributes in order of importance to the model.\n\nNote: this can take almost a minute, depending on your configuration.", "user": "OMLUSER", "dateUpdated": "2021-10-01T19:26:56+0000", "config": { @@ -2883,7 +2883,7 @@ "commited": true }, { - "text": "%md\n---\nAnother way of seeing the Global Feature Importance results with the `z.show` function in Zeppelin", + "text": "%md\n\nAnother way of seeing the Global Feature Importance results with the `z.show` function in Zeppelin", "user": "OMLUSER", "dateUpdated": "2021-09-22T19:05:02+0000", "config": { diff --git a/machine-learning/labs/oml4py-live-labs/Lab4_Store_and_manage_Python_objects_and_user_defined_functions.json b/machine-learning/labs/oml4py-live-labs/Lab4_Store_and_manage_Python_objects_and_user_defined_functions.json index 3bc56d60..0bedba07 100755 --- a/machine-learning/labs/oml4py-live-labs/Lab4_Store_and_manage_Python_objects_and_user_defined_functions.json +++ b/machine-learning/labs/oml4py-live-labs/Lab4_Store_and_manage_Python_objects_and_user_defined_functions.json @@ -152,7 +152,7 @@ "commited": true }, { - "text": "%md\n---\n2.2. Run the following script to create the temporary Diabetes table:", + "text": "%md\n\n2.2. Run the following script to create the temporary Diabetes table:", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:30:49+0000", "config": { @@ -230,7 +230,7 @@ "commited": true }, { - "text": "%md\n---\n2.3. Run the following script to create the Boston table:", + "text": "%md\n\n2.3. Run the following script to create the Boston table:", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:30:50+0000", "config": { @@ -381,7 +381,7 @@ "commited": true }, { - "text": "%md\n---\n3.2. Save the `DIABETES_TMP` tables into the database.\n\n**Note:** The condition `append\u003dTRUE` adds the object to the datastore, if it already exists. The default is `append\u003dFalse`, and in that case, you will receive an error stating that the datastore exists and it won\u0027t be able to create it again.", + "text": "%md\n\n3.2. Save the `DIABETES_TMP` tables into the database.\n\n**Note:** The condition `append\u003dTRUE` adds the object to the datastore, if it already exists. The default is `append\u003dFalse`, and in that case, you will receive an error stating that the datastore exists and it won\u0027t be able to create it again.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:00+0000", "config": { @@ -454,7 +454,7 @@ "commited": true }, { - "text": "%md\n---\n3.3. Save the `IRIS` table to a new datastore, and then list the datastores. Notice that you see the datastore name, the number of objects in the datastore, the size in bytes consumed, when the datastore was create/updated, and any description provided by the user. The two datastores `ds_iris_data` and `ds_pydata` are present, with the latter containing the three objects you added.", + "text": "%md\n\n3.3. Save the `IRIS` table to a new datastore, and then list the datastores. Notice that you see the datastore name, the number of objects in the datastore, the size in bytes consumed, when the datastore was create/updated, and any description provided by the user. The two datastores `ds_iris_data` and `ds_pydata` are present, with the latter containing the three objects you added.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:03+0000", "config": { @@ -637,7 +637,7 @@ "commited": true }, { - "text": "%md\n---\n4.2. Run the following script to save the objects `regr1` and `regr2` to the datastore `ds_pymodels`, and allow the read privilege to be granted to them.\n\n**Note:** `overwrite\u003dTrue` indicates that the contents of the datastore should be replaced.", + "text": "%md\n\n4.2. Run the following script to save the objects `regr1` and `regr2` to the datastore `ds_pymodels`, and allow the read privilege to be granted to them.\n\n**Note:** `overwrite\u003dTrue` indicates that the contents of the datastore should be replaced.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:08+0000", "config": { @@ -747,7 +747,7 @@ "commited": true }, { - "text": "%md\n---\n4.3. Now grant the read privilege to all users by specifying `user\u003dNone`. Finally, list the datastores to which the read privilege has been granted.", + "text": "%md\n\n4.3. Now grant the read privilege to all users by specifying `user\u003dNone`. Finally, list the datastores to which the read privilege has been granted.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:09+0000", "config": { @@ -903,7 +903,7 @@ "commited": true }, { - "text": "%md\n---\n5.2. Run the following script to load the named Python object `regr2` (regression model), from the datastore to the global workspace.\n\n**Note:** Using the boolean `to_globals` parameter, you can specify whether the objects are loaded to a global workspace or to a dictionary object. If the argument is `to_globals\u003dTrue`, then `oml.ds.load` function loads the objects into the global workspace. If the argument is `to_globals\u003dFalse`, then the function returns a dict object that contains pairs of object names and values.", + "text": "%md\n\n5.2. Run the following script to load the named Python object `regr2` (regression model), from the datastore to the global workspace.\n\n**Note:** Using the boolean `to_globals` parameter, you can specify whether the objects are loaded to a global workspace or to a dictionary object. If the argument is `to_globals\u003dTrue`, then `oml.ds.load` function loads the objects into the global workspace. If the argument is `to_globals\u003dFalse`, then the function returns a dict object that contains pairs of object names and values.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:22+0000", "config": { @@ -981,7 +981,7 @@ "commited": true }, { - "text": "%md\n---\n5.3. Run the following script to view the model details", + "text": "%md\n\n5.3. Run the following script to view the model details", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:29+0000", "config": { @@ -1059,7 +1059,7 @@ "commited": true }, { - "text": "%md\n---\n5.4. Run the following script to load the named Python object `regr1`, from the datastore to the user\u0027s workspace.\n\n**Note:** Using the boolean `to_globals` parameter, you can specify whether the objects are loaded to a global workspace or to a dictionary object. If the argument is `to_globals\u003dTrue`, then `oml.ds.load` function loads the objects into the global workspace. If the argument is `to_globals\u003dFalse`, then the function returns a dict object that contains pairs of object names and values.", + "text": "%md\n\n5.4. Run the following script to load the named Python object `regr1`, from the datastore to the user\u0027s workspace.\n\n**Note:** Using the boolean `to_globals` parameter, you can specify whether the objects are loaded to a global workspace or to a dictionary object. If the argument is `to_globals\u003dTrue`, then `oml.ds.load` function loads the objects into the global workspace. If the argument is `to_globals\u003dFalse`, then the function returns a dict object that contains pairs of object names and values.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:30+0000", "config": { @@ -1245,7 +1245,7 @@ "commited": true }, { - "text": "%md\n---\n6.2. Run the following script to list the datastores to which other users have been granted the read privilege:", + "text": "%md\n\n6.2. Run the following script to list the datastores to which other users have been granted the read privilege:", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:38+0000", "config": { @@ -1512,7 +1512,7 @@ "commited": true }, { - "text": "%md\n---\n8.2. Run the following script to grant read privilege to `OMLUSER2`.\n\nNote: If you are running this Notebook on your own tenancy, make sure to follow the instructions on creationg a new OMLUSER2, otherwise you will get an error. ", + "text": "%md\n\n8.2. Run the following script to grant read privilege to `OMLUSER2`.\n\nNote: If you are running this Notebook on your own tenancy, make sure to follow the instructions on creationg a new OMLUSER2, otherwise you will get an error. ", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:46+0000", "config": { @@ -1772,7 +1772,7 @@ "commited": true }, { - "text": "%md\n---\n10.2. Run the following script to view the string that you just created:", + "text": "%md\n\n10.2. Run the following script to view the string that you just created:", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:55+0000", "config": { @@ -1921,7 +1921,7 @@ "commited": true }, { - "text": "%md\n---\n11.2. Run the `oml.script.dir` script to list the scripts to which the read privilege has been granted, and where `sctype` is set to `grant`.", + "text": "%md\n\n11.2. Run the `oml.script.dir` script to list the scripts to which the read privilege has been granted, and where `sctype` is set to `grant`.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:57+0000", "config": { @@ -1998,7 +1998,7 @@ "commited": true }, { - "text": "%md\n---\n11.3. Run the following script to load the named function `MyLM_function` into the Python engine for use as a typical Python function using `oml.script.load`.", + "text": "%md\n\n11.3. Run the following script to load the named function `MyLM_function` into the Python engine for use as a typical Python function using `oml.script.load`.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:31:59+0000", "config": { @@ -2075,7 +2075,7 @@ "commited": true }, { - "text": "%md\n---\n11.4. Extract the function text string from the function object and use this to save in the script repository using `oml.script_create`.", + "text": "%md\n\n11.4. Extract the function text string from the function object and use this to save in the script repository using `oml.script_create`.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:32:00+0000", "config": { @@ -2152,7 +2152,7 @@ "commited": true }, { - "text": "%md\n---\n11.5. Run the script `oml.script.create` to create a test function `MyTEST_function`:", + "text": "%md\n\n11.5. Run the script `oml.script.create` to create a test function `MyTEST_function`:", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:32:01+0000", "config": { @@ -2224,7 +2224,7 @@ "commited": true }, { - "text": "%md\n---\n11.6. Use `oml.script.dir` to list all the available scripts.", + "text": "%md\n\n11.6. Use `oml.script.dir` to list all the available scripts.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:32:02+0000", "config": { @@ -2333,7 +2333,7 @@ "commited": true }, { - "text": "%md\n---\n11.7. Call the `table_apply` on `build_lm_str` and `loaded_str` functions. Note that these strings represent the same function `build_lm_str` that was saved to the script repository after assigning the function to a string object. The `loaded_str` is the string representation of the function extracted using `get_source().read()`.", + "text": "%md\n\n11.7. Call the `table_apply` on `build_lm_str` and `loaded_str` functions. Note that these strings represent the same function `build_lm_str` that was saved to the script repository after assigning the function to a string object. The `loaded_str` is the string representation of the function extracted using `get_source().read()`.", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:32:04+0000", "config": { @@ -2410,7 +2410,7 @@ "commited": true }, { - "text": "%md\n---\nRun the same function on `loaded_str`:", + "text": "%md\n\nRun the same function on `loaded_str`:", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:32:05+0000", "config": { @@ -2559,7 +2559,7 @@ "commited": true }, { - "text": "%md\n---\n12.2. Call the function `build_lm3` to build the model or model `MyGlobalML_function`:", + "text": "%md\n\n12.2. Call the function `build_lm3` to build the model or model `MyGlobalML_function`:", "user": "OMLUSER", "dateUpdated": "2021-09-22T21:32:07+0000", "config": { diff --git a/machine-learning/labs/oml4py-live-labs/Lab5_Run_user_defined_functions_using_Embedded_Python_Execution.json b/machine-learning/labs/oml4py-live-labs/Lab5_Run_user_defined_functions_using_Embedded_Python_Execution.json index e7f489d9..19749839 100755 --- a/machine-learning/labs/oml4py-live-labs/Lab5_Run_user_defined_functions_using_Embedded_Python_Execution.json +++ b/machine-learning/labs/oml4py-live-labs/Lab5_Run_user_defined_functions_using_Embedded_Python_Execution.json @@ -75,7 +75,7 @@ "commited": true }, { - "text": "%md\n---\n1.2. Run the following script to obtain a proxy object to the `IRIS` table.", + "text": "%md\n\n1.2. Run the following script to obtain a proxy object to the `IRIS` table.", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:11:38+0000", "config": { @@ -233,7 +233,7 @@ "commited": true }, { - "text": "%md\n---\n2.2. Run the script to predict the petal length using the `predict` function, and show the first 10 records.", + "text": "%md\n\n2.2. Run the script to predict the petal length using the `predict` function, and show the first 10 records.", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:11:42+0000", "config": { @@ -312,7 +312,7 @@ "commited": true }, { - "text": "%md\n---\n2.3. Run the following script to assess model quality using mean squared error and R^2:", + "text": "%md\n\n2.3. Run the following script to assess model quality using mean squared error and R^2:", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:11:44+0000", "config": { @@ -391,7 +391,7 @@ "commited": true }, { - "text": "%md\n---\n2.4. Run the following script to generate a scatterplot of the data along with a plot of the regression line:", + "text": "%md\n\n2.4. Run the following script to generate a scatterplot of the data along with a plot of the regression line:", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:11:46+0000", "config": { @@ -544,7 +544,7 @@ "commited": true }, { - "text": "%md\n---\n3.2. Now, call the user-defined function `build_lm_1` to build the model and plot the petal length predictions.:", + "text": "%md\n\n3.2. Now, call the user-defined function `build_lm_1` to build the model and plot the petal length predictions.:", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:11:51+0000", "config": { @@ -697,7 +697,7 @@ "commited": true }, { - "text": "%md\n---\n3.4. By calling the `table_apply`, a Python engine is spawned and the user-defined function `build_lm_1` is called on that engine with the data referenced by IRIS being passed in as a pandas DataFrame. Part of the return value is the image, which is automatically displayed. In this example, we are passing the function object to the `table_apply` function.", + "text": "%md\n\n3.4. By calling the `table_apply`, a Python engine is spawned and the user-defined function `build_lm_1` is called on that engine with the data referenced by IRIS being passed in as a pandas DataFrame. Part of the return value is the image, which is automatically displayed. In this example, we are passing the function object to the `table_apply` function.", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:11:56+0000", "config": { @@ -810,7 +810,7 @@ "commited": true }, { - "text": "%md\n---\n3.5. Run the following script to print the object, model, type and coefficient.", + "text": "%md\n\n3.5. Run the following script to print the object, model, type and coefficient.", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:12:01+0000", "config": { @@ -963,7 +963,7 @@ "commited": true }, { - "text": "%md\n---\n3.7. Use the `row_apply` to call this user-defined function and return a single DataFrame proxy object as the result. The `row_apply` function takes as arguments the proxy object `IRIS`, that we want 10 rows scored at a time (resulting in 15 function calls), the user-defined function, the linear model object, and that we want the result to be returned as a single table by specifying the table definition.", + "text": "%md\n\n3.7. Use the `row_apply` to call this user-defined function and return a single DataFrame proxy object as the result. The `row_apply` function takes as arguments the proxy object `IRIS`, that we want 10 rows scored at a time (resulting in 15 function calls), the user-defined function, the linear model object, and that we want the result to be returned as a single table by specifying the table definition.", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:12:05+0000", "config": { @@ -1151,7 +1151,7 @@ "commited": true }, { - "text": "%md\n---\n4.2. Change the user-defined function to save the models in a datastore. The datastore allows storing Python objects in the database under the provided name. The object assumes the name it is assigned in the Python environment. Here, you construct a name concatenating `mod_` as a prefix and the corresponding `Species` value.", + "text": "%md\n\n4.2. Change the user-defined function to save the models in a datastore. The datastore allows storing Python objects in the database under the provided name. The object assumes the name it is assigned in the Python environment. Here, you construct a name concatenating `mod_` as a prefix and the corresponding `Species` value.", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:12:11+0000", "config": { @@ -1225,7 +1225,7 @@ "commited": true }, { - "text": "%md\n---\n4.3. Use `group_apply` to call the user-defined function and list the resulting models, which are a dictionary of three elements each assigned the model object name. The `group_apply` function takes the data, the index parameter that specifies the column or columns to partition on, the user-defined function, and the database to which you connect from the Python engine. Connecting to the database is necessary when using the datastore functionality.\n\nHere, the model object names are `mod_versicolor`, `mod_virginica`, and `mod_setosa`.\nWhen you load the datastore, you get the three models loaded into the client Python engine, assigned to their respective variables.\n\n**Note:** If the datastore exists, then delete it so that the `group_apply` function completes successfully.\n**Note:** Embedded Python execution can also leverage functions from third-party packages, for example, sklearn and matplotlib, as provided with the Autonomous Database environment. These packages can be used inside the user-defined function as shown here using LinearSVC.", + "text": "%md\n\n4.3. Use `group_apply` to call the user-defined function and list the resulting models, which are a dictionary of three elements each assigned the model object name. The `group_apply` function takes the data, the index parameter that specifies the column or columns to partition on, the user-defined function, and the database to which you connect from the Python engine. Connecting to the database is necessary when using the datastore functionality.\n\nHere, the model object names are `mod_versicolor`, `mod_virginica`, and `mod_setosa`.\nWhen you load the datastore, you get the three models loaded into the client Python engine, assigned to their respective variables.\n\n**Note:** If the datastore exists, then delete it so that the `group_apply` function completes successfully.\n**Note:** Embedded Python execution can also leverage functions from third-party packages, for example, sklearn and matplotlib, as provided with the Autonomous Database environment. These packages can be used inside the user-defined function as shown here using LinearSVC.", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:12:13+0000", "config": { @@ -1542,7 +1542,7 @@ "commited": true }, { - "text": "%md\n---\n5.2. Use the `oml.do_eval` function to call the function `RandomRedDots` that you created in step 1:", + "text": "%md\n\n5.2. Use the `oml.do_eval` function to call the function `RandomRedDots` that you created in step 1:", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:12:24+0000", "config": { @@ -1664,7 +1664,7 @@ "commited": true }, { - "text": "%md\n---\n5.3. In this example, you modify the function to use subplots, thereby creating separate figure objects for the scatter plots. Store this in the script repository as `RandomRedDots2` and call the function to see the results. As expected, you get both plots.\nRun the following script to define the `RandomRedDots2` function that generates two scatter plots, and returns a two column DataFrame. Note that you can pass arguments to these functions here, `num_dots_1` and `num_dots_2`.", + "text": "%md\n\n5.3. In this example, you modify the function to use subplots, thereby creating separate figure objects for the scatter plots. Store this in the script repository as `RandomRedDots2` and call the function to see the results. As expected, you get both plots.\nRun the following script to define the `RandomRedDots2` function that generates two scatter plots, and returns a two column DataFrame. Note that you can pass arguments to these functions here, `num_dots_1` and `num_dots_2`.", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:12:32+0000", "config": { @@ -1743,7 +1743,7 @@ "commited": true }, { - "text": "%md\n---\n**Note:** When you call `RandomRedDots2` using embedded Python execution, you will get both plots as shown in the result.\n\n5.4. Use the `oml.do_eval` function to call the function `RandomRedDots2`. Here, you are specifying arguments to `do_eval` for `num_dots_1` and `num_dots_2`. These are specified as you would any other argument to `do_eval`. This applies to the other embedded Python functions as well.\n", + "text": "%md\n\n**Note:** When you call `RandomRedDots2` using embedded Python execution, you will get both plots as shown in the result.\n\n5.4. Use the `oml.do_eval` function to call the function `RandomRedDots2`. Here, you are specifying arguments to `do_eval` for `num_dots_1` and `num_dots_2`. These are specified as you would any other argument to `do_eval`. This applies to the other embedded Python functions as well.\n", "user": "OMLUSER", "dateUpdated": "2021-09-22T23:12:34+0000", "config": { diff --git a/machine-learning/labs/oml4py-live-labs/Lab6_Use_AutoML.json b/machine-learning/labs/oml4py-live-labs/Lab6_Use_AutoML.json index 95e06c34..26c303dc 100755 --- a/machine-learning/labs/oml4py-live-labs/Lab6_Use_AutoML.json +++ b/machine-learning/labs/oml4py-live-labs/Lab6_Use_AutoML.json @@ -74,7 +74,7 @@ "commited": true }, { - "text": "%md\n---\n1.2. Use the `oml.sync` function to create an OML Dataframe `CUST_DF` as a proxy for the database table `CUSTOMER_INSURANCE_LTV`.", + "text": "%md\n\n1.2. Use the `oml.sync` function to create an OML Dataframe `CUST_DF` as a proxy for the database table `CUSTOMER_INSURANCE_LTV`.", "user": "OMLUSER", "dateUpdated": "2021-09-30T04:59:31+0000", "config": { @@ -226,7 +226,7 @@ "commited": true }, { - "text": "%md\n---\n2.2. Run the following script to select the top `classification` algorithms for predicting whether a customer will buy insurance (`BUY_INSURANCE`). It displays the top 4 ranked algorithms (requested by the `k\u003d4`) and their `accuracy` (the metric we requested in `score_metric`). \n\nThe script returns Random Forest (rf), Decision Tree (dt), Neural Networks (nn) and Generalized Linear Models (glm) as the Top 4 algorithms, and among these the Random Forest is ranked highest for this problem, and the string `selected_insur_alg` will be used in subsequent AutoML function calls.\n \n**Note**: AutoML also supports regression, and the AlgorithmSelection function has other options and metrics that can be found in the documentation links at the bottom of this notebook.", + "text": "%md\n\n2.2. Run the following script to select the top `classification` algorithms for predicting whether a customer will buy insurance (`BUY_INSURANCE`). It displays the top 4 ranked algorithms (requested by the `k\u003d4`) and their `accuracy` (the metric we requested in `score_metric`). \n\nThe script returns Random Forest (rf), Decision Tree (dt), Neural Networks (nn) and Generalized Linear Models (glm) as the Top 4 algorithms, and among these the Random Forest is ranked highest for this problem, and the string `selected_insur_alg` will be used in subsequent AutoML function calls.\n \n**Note**: AutoML also supports regression, and the AlgorithmSelection function has other options and metrics that can be found in the documentation links at the bottom of this notebook.", "user": "OMLUSER", "dateUpdated": "2021-09-30T04:59:35+0000", "config": { @@ -500,7 +500,7 @@ "commited": true }, { - "text": "%md\n---\n4.2. Run the following script to **list the hyperparameters and their values** that were tried for the `top two models`, along with the corresponding model\u0027s score metric value (`accuracy`).", + "text": "%md\n\n4.2. Run the following script to **list the hyperparameters and their values** that were tried for the `top two models`, along with the corresponding model\u0027s score metric value (`accuracy`).", "user": "OMLUSER", "dateUpdated": "2021-09-30T05:01:48+0000", "config": { @@ -578,7 +578,7 @@ "commited": true }, { - "text": "%md\n---\n4.3. Run the following script to **list the hyperparameters and their values** that were tried for the `top ten models`, along with the corresponding model\u0027s score metric value (`accuracy`) in a better formatted way.", + "text": "%md\n\n4.3. Run the following script to **list the hyperparameters and their values** that were tried for the `top ten models`, along with the corresponding model\u0027s score metric value (`accuracy`) in a better formatted way.", "user": "OMLUSER", "dateUpdated": "2021-09-30T05:01:50+0000", "config": { @@ -730,7 +730,7 @@ "commited": true }, { - "text": "%md\n---\n4.3. Run the following script to specify a custom search space to explore for model building using the `param_space` argument to the `tune` function. With this specification, model tuning will narrow the set of important hyperparameter values.\n**Note**: For illustration purposes we are using a different scoring metric, `f1_macro`.", + "text": "%md\n\n4.3. Run the following script to specify a custom search space to explore for model building using the `param_space` argument to the `tune` function. With this specification, model tuning will narrow the set of important hyperparameter values.\n**Note**: For illustration purposes we are using a different scoring metric, `f1_macro`.", "user": "OMLUSER", "dateUpdated": "2021-09-30T05:01:53+0000", "config": { diff --git a/machine-learning/labs/oml4py-live-labs/README.md b/machine-learning/labs/oml4py-live-labs/README.md index e5109c82..e39a7230 100644 --- a/machine-learning/labs/oml4py-live-labs/README.md +++ b/machine-learning/labs/oml4py-live-labs/README.md @@ -1,9 +1,9 @@ # Oracle Machine Learning for Python -This set of notebooks from the OML4Py workshop [Introduction to Oracle Machine Learning for Python on Autonomous Database](https://bit.ly/oml4pyhol) introduces you to Oracle Machine Learning for Python (OML4Py) on Oracle Autonomous Database. +This set of notebooks from the OML4Py workshop [Introduction to Oracle Machine Learning for Python on Autonomous AI Database](https://livelabs.oracle.com/ords/r/dbpm/livelabs/view-workshop?wid=786) introduces you to Oracle Machine Learning for Python (OML4Py) on Oracle Autonomous Database. Oracle Machine Learning for Python (OML4Py) supports scalable in-database data exploration and preparation using native Python syntax, scalable in-database algorithms for machine learning model building and scoring, and automated machine learning (AutoML). Users can also invoke user-defined Python functions from Python and REST APIs using database-spawned Python engines. OML4Py increases data scientist productivity and reduces solution deployment complexity. Join us for this tour of OML4Py. -Python is a major programming language used for data science and machine learning. OML4Py is a feature on Oracle Autonomous Database that provides Python users access to powerful in-database functionality supporting data scientists for both scalability, performance, and ease of solution deployment. +Python is a major programming language used for data science and machine learning. OML4Py is a feature on Oracle Autonomous AI Database that provides Python users access to powerful in-database functionality supporting data scientists for both scalability, performance, and ease of solution deployment. Oracle Machine Learning Notebooks is a collaborative user interface for data scientists and business and data analysts who perform machine learning in Oracle Autonomous Database. @@ -13,7 +13,7 @@ Key Features: * Enables sharing of notebooks and templates with permissions and execution scheduling * Access to 30+ parallel, scalable in-Database implementations of machine learning algorithms * Python, SQL and PL/SQL scripting language supported -* Enables and supports deployments of enterprise machine learning methodologies in Autonomous Data Warehouse (ADW), Autonomous Transactional Database (ATP) and Autonomous JSON Database (AJD) +* Enables and supports deployments of enterprise machine learning methodologies in Autonomous AI Lakehouse, Autonomous AI Transactional Database and Autonomous AI JSON Database The current folder contains the examples based on Oracle Machine Learning for Python (OML4Py) used for the Live Labs "Python Users: Build intelligent applications faster with Oracle Machine Learning": @@ -25,12 +25,12 @@ The current folder contains the examples based on Oracle Machine Learning for Py * **Lab 5 OML4Py Embedded Python Execution** - Examples of using open-source Python scripts and algorithms (like SciKit-Learn) with OML4Py * **Lab 6 OML4Py AutoML** - Examples of how to execute OML AutoML processes to identify the ideal algorithms and tune the models. -More information on the Live Labs workshop [Introduction to Oracle Machine Learning for Python on Autonomous Database](https://bit.ly/oml4pyhol) +More information on the Live Labs workshop [Introduction to Oracle Machine Learning for Python on Autonomous Database](https://livelabs.oracle.com/ords/r/dbpm/livelabs/view-workshop?wid=786) -See also [Announcing next generation OML Notebooks on Oracle Autonomous Database](https://blogs.oracle.com/machinelearning/post/announcing-next-generation-to-oml-notebooks-on-oracle-autonomous-database) blog post for more information on OML Notebooks. +See also [Announcing next generation OML Notebooks on Oracle Autonomous AI Database](https://blogs.oracle.com/machinelearning/post/announcing-next-generation-to-oml-notebooks-on-oracle-autonomous-database) blog post for more information on OML Notebooks. -Last updated: April 2023 +Last updated: November 2025 -#### Copyright (c) 2023 Oracle Corporation and/or its affilitiates. +#### Copyright (c) 2025 Oracle Corporation and/or its affilitiates. ###### [The Universal Permissive License (UPL), Version 1.0](https://oss.oracle.com/licenses/upl/) diff --git a/machine-learning/notebooks/notebooks-classic/python/OML Run-me-first.json b/machine-learning/notebooks-oml/notebooks-classic/python/OML Run-me-first.json similarity index 100% rename from machine-learning/notebooks/notebooks-classic/python/OML Run-me-first.json rename to machine-learning/notebooks-oml/notebooks-classic/python/OML Run-me-first.json diff --git a/machine-learning/notebooks/notebooks-classic/python/OML Third-Party Packages - 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"],"enabled":true,"result":{"startTime":1737138360559,"interpreter":"md.low","endTime":1737138361104,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML Run-me-first: Data preparation and creation for examples","","In this notebook, we will load data used in other notebooks that require the `CUSTOMER_INSURANCE_LTV` table, and will also prepare the dataset used by the `Data Preparation` notebooks, in particular those for data cleaning and feature selection.","","The original dataset contains customer insurance lifetime value information, including customer financial information, lifetime value, and whether or not the customer bought insurance.","","We will take the original table and will tweak the data, with some original values being replaced by `NULL`, and adding `duplicated rows` artificially to generate a resulting table we will call `CUSTOMER_INSURANCE_LTV_NOISE`. ","","The source of the data is a CSV file stored in Oracle Machine Learning GitHub folder<\/a>, which will be imported into the current user's schema using the `DBMS_CLOUD.CREATE_EXTERNAL_TABLE` function among others.","","###### `IMPORTANT` Please run this notebook before running any of the Data Preparation notebooks.","","Copyright (c) 2025 Oracle Corporation ","###### The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737138361570,"interpreter":"md.low","endTime":1737138362088,"results":[{"message":"
OML Run-me-first: Data preparation and creation for examples<\/h1>\n
In this notebook, we will load data used in other notebooks that require the The original dataset contains customer insurance lifetime value information, including customer financial information, lifetime value, and whether or not the customer bought insurance.<\/p>\n We will take the original table and will tweak the data, with some original values being replaced by The source of the data is a CSV file stored in Oracle Machine Learning GitHub folder<\/a>, which will be imported into the current user's schema using the Copyright (c) 2025 Oracle Corporation<\/p>\n This table will be used in other notebooks and allow us to build the We will pull the data from OML dataset storage in GitHub using the More details on Oracle Machine Learning Notebooks provide a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, and switch between environments to use project-specific packages. Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session. The conda environments can be used in the OML4Py Python, SQL, and REST APIs, and the OML4R R, SQL, and REST APIs. In this notebook, we demonstrate a typical workflow for third-party environment creation and package installation in OML notebooks. Section 1 contains common commands used by ADMIN while creating and testing conda environments. In section 2, the ADMIN user creates a conda environment, installs packages, and uploads the conda environment to an Object Storage bucket associated with the Autonomous Database. In the template notebooks titled, OML Third-Party Packages - R Environment Usage and OML Third-Party Packages - Python Environment Usage, the OML user downloads and activates the environment, and uses the packages in their notebook session. Copyright (c) 2025 Oracle Corporation Refer to Using Oracle Machine Learning on Autonomous Database for a table of supported conda commands. This notebook reviews the following conda commands: To get help for conda commands, run the command name followed by the The The A set of enhanced conda commands in the conda environment lifecycle management package The ADMIN user has the permissions to create conda environments and install packages. Start by listing the environments available by default. Conda contains default environments with some core system libraries and conda dependencies. The active environment is marked with an asterisk (*). This section demonstrates creating and installing packages to a conda environment, then removes the environment. We illustrate commonly used options available for environment creation and testing. The environment exists for the duration of the notebook session and does not persist between sessions unless it is saved to Object Storage. For instructions that include both creating and persisting an environment for OML users, refer to the section 2, Create a Conda Environment and Upload to Object Storage. As ADMIN user: Notes: When conda installs a package into an environment it also installs any required dependencies. While we are demonstrating that it's possible to install packages to an existing environment, it is a best practice to install all the packages that you want in a specific environment at the same time to avoid package dependency conflicts. The ADMIN user can access the conda environment from Python and R, but does not have the capability to run embedded Python and R execution commands. For help with the conda Create conda environment called myenv with Python 3.12 for OML4Py compatibility and install the keras package. Verify the myenv environment is in the list of environments. The asterisk (*) indicates active environments. The new environment is created but not activated. Activate the myenv environment and list the environments to verify the activation. The asterisk (*) next to the environment name confirms the activation. The ADMIN user can install and uninstall libraries into an environment using the For help with the conda Install the pytorch package into the activated myenv environment. Note, to avoid dependency conflicts, install all packages when the environment is built. List the packages installed in the current environment, and confirm that keras and pytorch are installed. Libraries can be uninstalled from an environment using the List packages in current environment and verify that the pytorch package was uninstalled. If you don't intend to upload the environment to Object Storage for the OML users in the database, you can simply exit the notebook session and it will go out of scope. Alternatively, it can be explicitly removed using the For help on the This section demonstrates creating two conda environments and uploading them to Object Storage. An environment saved in Object Storage can be used by all of the Autonomous Database users in the database, and will remain in Object Storage until ADMIN deletes it. As ADMIN user: Create a conda environment named mypyenv with Python 3.12 for OML4Py compatibility and install the Create a conda environment named myrenv with R-4.0.5 for OML4R compatibility and install the Upload the environments to the Object Storage bucket associated with the Autonomous Database instance using the The application tag is required for use with embedded execution. For example, OML4Py embedded Python execution works with conda environments containing the OML4PY tag, and OML4R embedded R execution works with conda environments containing the OML4R tag. There is one Object Storage bucket for each data center region. The conda environments are saved to a folder in Object Storage corresponding to the tenancy and database. The folder is managed by Autonomous Database and only available to users through OML notebooks. There is a 8G maximum size for a single conda environment, and no size limit on Object Storage. To get help for the The environments are now available for the OML user to download and will remain in Object Storage until it is deleted. Verify the environments are saved to Object storage using the If an environment is no longer in use, it can be deleted from Object Storage using the For help on the conda Note, do not run the paragraphs containing the A requirement of the SQL API for embedded R and Python execution on Autonomous Database is adding the OML user to the Access Control List (ACL). The ACL provides additional protection to your Autonomous Database by allowing only the client with specific IP addresses to connect to the database. As ADMIN, run the network access control list For example, if your username is OMLUSER and your Autonomous Database resides in the Ashburn region, the root domain is adb.us-ashburn-1.oraclecloudapps.com. Refer to the template notebooks, OML Third-Party Packages - Python Environment Usage and OML Third-Party Packages - R Environment Usage for the steps to download and use the saved environments, mypyenv and myrenv. Oracle Machine Learning Notebooks provide a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, and switch between environments to use project-specific packages. Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session. The conda environments can be used in the Python and the OML4Py Python, SQL, and REST APIs, and R and the OML4R R, SQL, and REST APIs. In this notebook, we demonstrate a typical workflow for third-party environment usage in OML notebooks using Python and OML4Py. The OML user downloads and uses the packages in conda environments that were previously created and saved to an Object Storage bucket folder associated with the Autonomous Database. The template notebook titled, OML Third-Party Packages - Environment Creation, the ADMIN user creates a conda environment, installs packages, and uploads the conda environments to Object Storage. Copyright (c) 2025 Oracle Corporation\nThe Universal Permissive License (UPL), Version 1.0 Use The application tag indicates how the environment can be used. For example, the environment In this example, we build a machine learning model that accurately predicts an iris flower species when given flower measurements. The Python packages used are Keras, TensorFlow, Seaborn, Pandas, Numpy, and Scikit-Learn, and the conda environment is mypyenv. We first run the code and create a UDF using Python, followed by invoking the UDF in OML4Py embedded Python execution from the Python, SQL and REST APIs. Note, Pandas, Numpy, and Scikit-Learn are included in OML Notebooks in Oracle Autonomous Database so it is not necessary for ADMIN to install them when creating the conda environment. Conda environments are available after they are downloaded and activated using the The Access Control List (ACL) provides additional protection to your Autonomous Database by allowing only the client with specific IP addresses to connect to the database. As ADMIN, run the network access control list Then run the token store To use the SQL and REST APIs for embedded Python execution, the UDF must be stored in the script repository, and an Oracle Machine Learning (OML) cloud account USERNAME, PASSWORD, and URL must be provided to obtain an authentication token. To use a conda environment when calling OML4Py script execution endpoints, specify the conda environment in the Obtain the token URL from the OML service console in Autonomous Database by first signing into your Oracle Cloud Infrastructure account with your OCI user name and password: The includes the location, tenancy ID, and database name separated by dashes. For example, https://qtraya2braestch-omldb.adb.us-sanjose-1.oraclecloudapps.com. Poll the job status sing the Note, replace your job ID with the one shown here. Oracle Machine Learning for Python<\/strong><\/em> (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as automated machine learning capabilities. In this notebook, we highlight the range of OML4Py features.<\/p>\n This notebook is the first of a series (0 through 5) that is intended to introduce the range of OML4Py functionality through short examples.<\/p>\n Copyright (c) 2025 Oracle Corporation<\/p>\n Note: The Oracle Database connectivity package python-oracledb<\/strong><\/em> is used to support database access.\nYou can get access to the underlying connection to use python-oracledb<\/strong><\/em> directly via the function oml.cursor<\/strong><\/em>.\nSee the general cx_Oracle<\/strong><\/em> documentation <\/a> for more information.<\/p>\n For Oracle Autonomous Database, a database connection is automatically established provided you have an appropriate interpreter\nbinding specified and import the oml<\/strong><\/em> package.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Import libraries and set display options","message":["%python","","import pandas as pd","import oml","from oml import automl","import warnings","","","pd.set_option('display.max_rows', 500)","pd.set_option('display.max_columns', 500)","pd.set_option('display.width', 1000)","warnings.simplefilter(action='ignore', category=FutureWarning)"],"enabled":true,"result":{"startTime":1737140127410,"interpreter":"python.low","endTime":1737140129899,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Get a proxy object for a database table","Here, we illustrate two ways to get proxy objects to database tables. First, we do so creating a new table with ***oml.create*** based on data from sklearn datasets. The second uses the ***oml.sync*** function to obtain an OML proxy object for the named table. A typical machine learning step is to split the data into train and test sets, followed by separating predictors from the target. This is illustrated on the OML proxy objects using the OML4Py transparency layer functionality. "],"enabled":true,"result":{"startTime":1737140130377,"interpreter":"md.low","endTime":1737140130843,"results":[{"message":" Here, we illustrate two ways to get proxy objects to database tables. First, we do so creating a new table with oml.create<\/strong><\/em> based on data from sklearn datasets. The second uses the oml.sync<\/strong><\/em> function to obtain an OML proxy object for the named table. A typical machine learning step is to split the data into train and test sets, followed by separating predictors from the target. This is illustrated on the OML proxy objects using the OML4Py transparency layer functionality.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Create table from Python DataFrame","message":["%python","","from sklearn import datasets","import pandas as pd","pd.set_option('display.width',1000)","","breastcancer = datasets.load_breast_cancer()","bc_data = breastcancer.data.astype(float)","X = pd.DataFrame(bc_data, columns = breastcancer.feature_names)","y = pd.DataFrame(breastcancer.target, columns = ['TARGET'])","","try:"," oml.drop(table='BREASTCANCER')","except:"," pass"," ","BREASTCANCER = oml.create(pd.concat([X, y], axis=1), table = 'BREASTCANCER') # returns proxy object","","print(\"Shape:\",BREASTCANCER.shape)","print(\"Description:\\n\", breastcancer.DESCR)"],"enabled":true,"result":{"startTime":1737140131337,"interpreter":"python.low","endTime":1737140132381,"results":[{"message":"Shape: (569, 31)\nDescription:\n .. _breast_cancer_dataset:\n\nBreast cancer wisconsin (diagnostic) dataset\n--------------------------------------------\n\n**Data Set Characteristics:**\n\n:Number of Instances: 569\n\n:Number of Attributes: 30 numeric, predictive attributes and the class\n\n:Attribute Information:\n - radius (mean of distances from center to points on the perimeter)\n - texture (standard deviation of gray-scale values)\n - perimeter\n - area\n - smoothness (local variation in radius lengths)\n - compactness (perimeter^2 / area - 1.0)\n - concavity (severity of concave portions of the contour)\n - concave points (number of concave portions of the contour)\n - symmetry\n - fractal dimension (\"coastline approximation\" - 1)\n\n The mean, standard error, and \"worst\" or largest (mean of the three\n worst/largest values) of these features were computed for each image,\n resulting in 30 features. For instance, field 0 is Mean Radius, field\n 10 is Radius SE, field 20 is Worst Radius.\n\n - class:\n - WDBC-Malignant\n - WDBC-Benign\n\n:Summary Statistics:\n\n===================================== ====== ======\n Min Max\n===================================== ====== ======\nradius (mean): 6.981 28.11\ntexture (mean): 9.71 39.28\nperimeter (mean): 43.79 188.5\narea (mean): 143.5 2501.0\nsmoothness (mean): 0.053 0.163\ncompactness (mean): 0.019 0.345\nconcavity (mean): 0.0 0.427\nconcave points (mean): 0.0 0.201\nsymmetry (mean): 0.106 0.304\nfractal dimension (mean): 0.05 0.097\nradius (standard error): 0.112 2.873\ntexture (standard error): 0.36 4.885\nperimeter (standard error): 0.757 21.98\narea (standard error): 6.802 542.2\nsmoothness (standard error): 0.002 0.031\ncompactness (standard error): 0.002 0.135\nconcavity (standard error): 0.0 0.396\nconcave points (standard error): 0.0 0.053\nsymmetry (standard error): 0.008 0.079\nfractal dimension (standard error): 0.001 0.03\nradius (worst): 7.93 36.04\ntexture (worst): 12.02 49.54\nperimeter (worst): 50.41 251.2\narea (worst): 185.2 4254.0\nsmoothness (worst): 0.071 0.223\ncompactness (worst): 0.027 1.058\nconcavity (worst): 0.0 1.252\nconcave points (worst): 0.0 0.291\nsymmetry (worst): 0.156 0.664\nfractal dimension (worst): 0.055 0.208\n===================================== ====== ======\n\n:Missing Attribute Values: None\n\n:Class Distribution: 212 - Malignant, 357 - Benign\n\n:Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n\n:Donor: Nick Street\n\n:Date: November, 1995\n\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\nhttps://goo.gl/U2Uwz2\n\nFeatures are computed from a digitized image of a fine needle\naspirate (FNA) of a breast mass. They describe\ncharacteristics of the cell nuclei present in the image.\n\nSeparating plane described above was obtained using\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\nConstruction Via Linear Programming.\" Proceedings of the 4th\nMidwest Artificial Intelligence and Cognitive Science Society,\npp. 97-101, 1992], a classification method which uses linear\nprogramming to construct a decision tree. Relevant features\nwere selected using an exhaustive search in the space of 1-4\nfeatures and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane\nin the 3-dimensional space is that described in:\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\nProgramming Discrimination of Two Linearly Inseparable Sets\",\nOptimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\n\nftp ftp.cs.wisc.edu\ncd math-prog/cpo-dataset/machine-learn/WDBC/\n\n|details-start|\n**References**\n|details-split|\n\n- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction\n for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on\n Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n San Jose, CA, 1993.\n- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and\n prognosis via linear programming. Operations Research, 43(4), pages 570-577,\n July-August 1995.\n- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)\n 163-171.\n\n|details-end|\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Get proxy object for named database table and split data","message":["%python","","BREASTCANCER = oml.sync(table = \"BREASTCANCER\") # returns proxy object","print(\"Full Dataset Type: \", type(BREASTCANCER))","print(\"Full Dataset Shape: \", BREASTCANCER.shape)","","BREASTCANCER_X,BREASTCANCER_y = BREASTCANCER.drop('TARGET'), BREASTCANCER['TARGET']","","BREASTCANCER_train, BREASTCANCER_test = BREASTCANCER.split(ratio=(0.6, 0.4))","","BREASTCANCER_train_X, BREASTCANCER_train_y = BREASTCANCER_train.drop('TARGET'), BREASTCANCER_train['TARGET']","BREASTCANCER_test_X, BREASTCANCER_test_y = BREASTCANCER_test.drop('TARGET'), BREASTCANCER_test['TARGET']","","print(\"Train Type: \", type(BREASTCANCER_train))","print(\"Train Shape: \", BREASTCANCER_train.shape)"],"enabled":true,"result":{"startTime":1737140132863,"interpreter":"python.low","endTime":1737140133833,"results":[{"message":"Full Dataset Type: The AutoML workflow consists of<\/p>\n Which algorithm is best to predict my target?<\/p>\n To use AutoML algorithm selection, we create an AlgorithmSelection<\/strong><\/em> object, here with the 'accuracy' score metric. Then, we invoke select<\/strong><\/em> to get the top 4 predicted algorithms by setting k=4. Algorithm Selection automatically selects the best algorithm from the set of supported in-database algorithms by using characteristics of the data set and technique. Algorithm Selection itself uses advanced machine learning and meta-learning<\/strong><\/em> techniques.<\/p>\n Users can select from a variety of score_metric<\/strong><\/em> options:<\/p>\n Automated Algorithm Selection provides a ranked list of algorithms. The first element produced the highest score metric and likely should be chosen for subsequent steps.<\/p>\n See the documentation for Automated Algorithm Selection<\/a> for more details.<\/p>\n In this example, we define a FeatureSelection<\/strong><\/em> object with score metric 'accuracy' and invoke reduce<\/strong><\/em> specifying the neural network algorithm determined above and the data set proxy object.<\/p>\n Automated Feature Selection provides a list of selected features, or columns. Use this set to produce an OML DataFrame with this column subset.<\/p>\n See the documentation for Automated Feature Selection<\/a> for more details.<\/p>\n We see the set of selected columns. Replace the We use the selected set of columns to also reduce the train and test datasets created earlier for use in our manual model building later in this script.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","fs_bc = automl.FeatureSelection(mining_function = 'classification', score_metric = 'accuracy', parallel=4)","","selected_features = fs_bc.reduce('nn', BREASTCANCER_X, BREASTCANCER_y)","","BREASTCANCER_X_reduced = BREASTCANCER_X[:,selected_features]","","BREASTCANCER_train_X_reduced = BREASTCANCER_train_X[:,selected_features]","BREASTCANCER_test_X_reduced = BREASTCANCER_test_X[:,selected_features]","","print(\"Selected columns:\",BREASTCANCER_train_X_reduced.columns)","","print(\"\\nNumber of columns:\")","\"{} reduced to {}\".format(len(BREASTCANCER_train_X.columns), len(BREASTCANCER_train_X_reduced.columns))"],"enabled":true,"result":{"startTime":1737140176412,"interpreter":"python.low","endTime":1737140275010,"results":[{"message":"UserWarning:Parallelism exceeds the DOP limit 2 (reverting to parallel=2)\nSelected columns: ['mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concave points error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points']\n\nNumber of columns:\n'30 reduced to 23'\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Automated Model Tuning","","Using data set proxy object, we define a ***ModelTuning*** object for classification. We then invoke ***tune*** to produce the tuned model using the neural network algorithm specified by the argument `'nn'`, but you should tune the same algorithm type that you used to do Feature Selection. We also provide the subset of columns determined above when passing the `reduced` attributes to the `X` and `y`. ","","The result of model tuning is a dictionary with the best model and the tuned parameters.","See the documentation for Automated Model Tuning <\/a> for more details."],"enabled":true,"result":{"startTime":1737140275513,"interpreter":"md.low","endTime":1737140275962,"results":[{"message":"CUSTOMER_INSURANCE_LTV<\/code> table, and will also prepare the dataset used by the Data Preparation<\/code> notebooks, in particular those for data cleaning and feature selection.<\/p>\nNULL<\/code>, and adding duplicated rows<\/code> artificially to generate a resulting table we will call CUSTOMER_INSURANCE_LTV_NOISE<\/code>.<\/p>\nDBMS_CLOUD.CREATE_EXTERNAL_TABLE<\/code> function among others.<\/p>\nIMPORTANT<\/code> Please run this notebook before running any of the Data Preparation notebooks.<\/h6>\nThe Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n
\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Cleanup if tables exist","message":["%script","","-- DELETE PREVIOUS DATA IF IT EXISTS","BEGIN "," BEGIN"," EXECUTE IMMEDIATE 'DROP TABLE EXT_CUSTOMER_INSURANCE_LTV';"," EXCEPTION WHEN OTHERS THEN NULL;"," END;"," BEGIN"," EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV';"," EXCEPTION WHEN OTHERS THEN NULL;"," END;"," BEGIN"," EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_NOISE';"," EXCEPTION WHEN OTHERS THEN NULL;"," END;"," BEGIN"," EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_SQL';"," EXCEPTION WHEN OTHERS THEN NULL;"," END;"," BEGIN"," EXECUTE IMMEDIATE 'DROP TABLE CUSTOMER_INSURANCE_LTV_PY';"," EXCEPTION WHEN OTHERS THEN NULL;"," END;","END; "],"enabled":true,"result":{"startTime":1737138362545,"interpreter":"script.low","endTime":1737138365251,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","#### Create the `CUSTOMER_INSURANCE_LTV` table ","","This table will be used in other notebooks and allow us to build the `CUSTOMER_INSURANCE_LTV_NOISE` that will also be used.","","We will pull the data from OML dataset storage in GitHub using the `DBMS_CLOUD` capabilities.","","More details on `DBMS_CLOUD` can be found in the Documentation<\/a>"],"enabled":true,"result":{"startTime":1737138365721,"interpreter":"md.low","endTime":1737138366172,"results":[{"message":"Create the
CUSTOMER_INSURANCE_LTV<\/code> table<\/h4>\nCUSTOMER_INSURANCE_LTV_NOISE<\/code> that will also be used.<\/p>\nDBMS_CLOUD<\/code> capabilities.<\/p>\nDBMS_CLOUD<\/code> can be found in the Documentation<\/a><\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Load the data from Object Storage","message":["%script","","-- LOADS THE DATA FROM GITHUB BY CREATING AN EXTERNAL TABLE AND THEN CREATING A TABLE WITH THAT","DECLARE"," uri_data varchar2(1000) := 'https://raw.githubusercontent.com/oracle/oracle-db-examples/master/machine-learning/datasets/CUST_INSUR_LTV_APPLY.csv';"," csv_format varchar2(1000) := '{\"dateformat\":\"YYYY-MM-DD\", \"skipheaders\":\"1\", \"delimiter\":\",\", \"ignoreblanklines\":\"true\", \"removequotes\":\"true\", \"blankasnull\":\"true\", \"trimspaces\":\"lrtrim\", \"truncatecol\":\"true\", \"ignoremissingcolumns\":\"true\"}';","BEGIN","DBMS_CLOUD.CREATE_EXTERNAL_TABLE("," TABLE_NAME => 'EXT_CUSTOMER_INSURANCE_LTV',"," FILE_URI_LIST => uri_data,"," FORMAT => csv_format,"," COLUMN_LIST => 'MARITAL_STATUS VARCHAR2(26),"," STATE CHAR(26),"," CREDIT_BALANCE NUMBER(8,0),"," CUSTOMER_TENURE NUMBER(3,0),"," MORTGAGE_AMOUNT NUMBER(7,0),"," BANK_FUNDS NUMBER(7,0),"," NUM_DEPENDENTS NUMBER(3,0),"," HAS_CHILDREN NUMBER(3,0),"," INCOME NUMBER(7,0),"," CUSTOMER_ID CHAR(26),"," GENDER CHAR(26),"," PROFESSION VARCHAR2(35),"," CREDIT_CARD_LIMITS NUMBER(6,0),"," REGION VARCHAR2(26),"," HOME_OWNERSHIP NUMBER(3,0),"," NUM_ONLINE_TRANS NUMBER(6,0),"," BUY_INSURANCE VARCHAR2(26),"," MONTHLY_CHECKS NUMBER(4,0),"," NUM_TRANS_KIOSK NUMBER(3,0),"," AGE NUMBER(4,0),"," MONEY_MONTLY_OVERDRAWN NUMBER(6,2),"," LTV NUMBER(9,2),"," TOTAL_AUTOM_PAYMENTS NUMBER(8,0),"," NUM_TRANS_TELLER NUMBER(3,0),"," CHECKING_BALANCE NUMBER(7,0),"," NUM_TRANS_ATM NUMBER(3,0),"," LTV_BIN VARCHAR2(26),"," FIRST_NAME VARCHAR2(26),"," NUM_MORTGAGES NUMBER(3,0),"," CAR_OWNERSHIP NUMBER(3,0),"," LAST_NAME VARCHAR2(26)');"," ","-- WRITE A TABLE INTO THE CURRENT USER WITH THE NAME \"CUSTOMER_INSURANCE_LTV\" "," EXECUTE IMMEDIATE 'create table CUSTOMER_INSURANCE_LTV as select * from EXT_CUSTOMER_INSURANCE_LTV';"," EXCEPTION WHEN OTHERS THEN NULL;","","END;"],"enabled":true,"result":{"startTime":1737138366645,"interpreter":"script.low","endTime":1737138369552,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count the number of records","message":["%sql","","SELECT count(*) FROM CUSTOMER_INSURANCE_LTV;"],"enabled":true,"result":{"startTime":1737138370067,"interpreter":"sql.low","endTime":1737138370613,"results":[{"message":"COUNT(*)\n13880\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Review the contents of the CUSTOMER_INSURANCE_LTV table","message":["%sql","","SELECT * ","FROM CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1737138371093,"interpreter":"sql.low","endTime":1737138371596,"results":[{"message":"MARITAL_STATUS\tSTATE\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tBANK_FUNDS\tNUM_DEPENDENTS\tHAS_CHILDREN\tINCOME\tCUSTOMER_ID\tGENDER\tPROFESSION\tCREDIT_CARD_LIMITS\tREGION\tHOME_OWNERSHIP\tNUM_ONLINE_TRANS\tBUY_INSURANCE\tMONTHLY_CHECKS\tNUM_TRANS_KIOSK\tAGE\tMONEY_MONTLY_OVERDRAWN\tLTV\tTOTAL_AUTOM_PAYMENTS\tNUM_TRANS_TELLER\tCHECKING_BALANCE\tNUM_TRANS_ATM\tLTV_BIN\tFIRST_NAME\tNUM_MORTGAGES\tCAR_OWNERSHIP\tLAST_NAME\nSINGLE\tCA \t0\t3\t0\t0\t3\t0\t65871\tCU15154 \tM \tNurse\t1000\tWest\t0\t0\tNo\t0\t1\t24\t53.06\t14367.75\t0\t0\t25\t0\tMEDIUM\tGAYLE\t0\t0\tDURANT\nSINGLE\tNY \t0\t4\t0\t290\t4\t0\t68747\tCU15155 \tM \tProgrammer/Developer\t700\tNorthEast\t0\t0\tYes\t1\t1\t35\t53.84\t14686.75\t287\t2\t25\t4\tMEDIUM\tQUINTON\t0\t1\tMASSEY\nMARRIED\tMI \t0\t3\t1000\t550\t3\t0\t68684\tCU15157 \tM \tProgrammer/Developer\t1000\tMidwest\t1\t1000\tYes\t14\t1\t26\t53.48\t25271\t132\t2\t25\t4\tHIGH\tANIBAL\t1\t1\tJIMENEZ\nMARRIED\tUT \t0\t5\t1200\t1000\t5\t0\t59354\tCU15286 \tF \tFireman\t2500\tSouthwest\t1\t1200\tNo\t4\t5\t24\t53.08\t19738.5\t628\t3\t619\t1\tMEDIUM\tJUNITA\t1\t1\tROBERTSON\nMARRIED\tUT \t0\t4\t1800\t0\t3\t0\t84801\tCU15287 \tF \tPROF-26\t2500\tSouthwest\t1\t1800\tNo\t0\t5\t47\t53.06\t31900.25\t0\t0\t25\t0\tVERY HIGH\tCHASITY\t1\t1\tELLIS\nMARRIED\tUT \t0\t1\t1400\t0\t1\t0\t73987\tCU15289 \tM \tProfessor\t2500\tSouthwest\t1\t1400\tNo\t0\t5\t46\t53.06\t31596.75\t0\t0\t25\t0\tVERY HIGH\tFRANKLIN\t1\t1\tKNOX\nSINGLE\tUT \t0\t3\t578\t0\t3\t0\t51452\tCU15290 \tM \tSales Representative\t2500\tSouthwest\t1\t578\tNo\t0\t5\t33\t53.06\t21663\t0\t0\t25\t0\tMEDIUM\tLINCOLN\t1\t1\tMATTSON\nSINGLE\tUT \t0\t3\t0\t0\t3\t0\t63181\tCU15291 \tM \tConstruction Laborer\t2500\tSouthwest\t0\t0\tNo\t1\t5\t49\t53.07\t16195.25\t0\t0\t25\t1\tMEDIUM\tSTEPHEN\t0\t0\tCARROLL\nSINGLE\tUT 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@@ -0,0 +1,2512 @@
+[
+ {
+ "name" : "OML Third-Party Packages - Environment Creation",
+ "description" : null,
+ "tags" : null,
+ "version" : "7",
+ "layout" : "jupyter",
+ "type" : "medium",
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+ "sizeX" : 0,
+ "width" : 12,
+ "title" : null,
+ "hasTitle" : false,
+ "message" : [
+ "%md",
+ "",
+ "# OML Third-Party Packages - Environment Creation",
+ "",
+ "Oracle Machine Learning Notebooks provide a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, and switch between environments to use project-specific packages. ",
+ "",
+ "Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session. The conda environments can be used in the OML4Py Python, SQL, and REST APIs, and the OML4R R, SQL, and REST APIs.",
+ "",
+ "In this notebook, we demonstrate a typical workflow for third-party environment creation and package installation in OML notebooks. Section 1 contains common commands used by ADMIN while creating and testing conda environments. In section 2, the ADMIN user creates a conda environment, installs packages, and uploads the conda environment to an Object Storage bucket associated with the Autonomous Database. ",
+ "",
+ "In the template notebooks titled, *OML Third-Party Packages - R Environment Usage* and *OML Third-Party Packages - Python Environment Usage*, the OML user downloads and activates the environment, and uses the packages in their notebook session.",
+ "",
+ "---",
+ "Copyright (c) 2025 Oracle Corporation ",
+ "###### The Universal Permissive License (UPL), Version 1.0"
+ ],
+ "selectedVisualization" : "html",
+ "visualizationConfig" : null,
+ "hideCode" : true,
+ "hideResult" : false,
+ "hideGutter" : true,
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+ "message" : "
OML Third-Party Packages - Environment Creation
\n
\nThe Universal Permissive License (UPL), Version 1.0
\n",
+ "type" : "HTML"
+ }
+ ],
+ "forms" : "[]"
+ },
+ "relations" : [ ],
+ "dynamicFormParams" : "{}"
+ },
+ {
+ "row" : 0,
+ "col" : 0,
+ "sizeX" : 0,
+ "width" : 12,
+ "title" : "Supported conda commands",
+ "hasTitle" : true,
+ "message" : [
+ "%md",
+ "",
+ "Refer to [Using Oracle Machine Learning on Autonomous Database](https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/notebooks-classic.html#GUID-5A206265-9EB0-4E49-A882-BFBF2DB5DB71) for a table of supported conda commands.",
+ "",
+ "This notebook reviews the following conda commands:",
+ "",
+ "- `--help`",
+ "- `install`",
+ "- `info`",
+ "- `search`",
+ "- `env list`",
+ "- `create`",
+ "- `activate`",
+ "- `list`",
+ "- `install`",
+ "- `uninstall`",
+ "- `remove`",
+ "- `list-saved-envs`",
+ "- `upload`",
+ "- `delete`"
+ ],
+ "selectedVisualization" : "html",
+ "visualizationConfig" : null,
+ "hideCode" : true,
+ "hideResult" : false,
+ "hideGutter" : true,
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+ }
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+ "sizeX" : 0,
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+ "title" : null,
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+ "message" : [
+ "%md",
+ "",
+ "### Section 1: Commands for Creating and Managing Environments"
+ ],
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+ "message" : "--helpinstallinfosearchenv listcreateactivatelistinstalluninstallremovelist-saved-envsuploaddeleteSection 1: Commands for Creating and Managing Environments
\n",
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+ }
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+ "hasTitle" : false,
+ "message" : [
+ "%md",
+ "---",
+ "### Conda Help",
+ "---",
+ "",
+ "To get help for conda commands, run the command name followed by the `--help` flag. The `conda` command is not run explicitly because the %conda interpreter provides the conda context."
+ ],
+ "selectedVisualization" : "html",
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+ "message" : "--help flag. The conda command is not run explicitly because the %conda interpreter provides the conda context.info command provides information about the conda installation, including the conda version and available channels.search command allows the user to search for packages and display associated information, including the package version and the channel where it resides.Enhanced Conda Commands
\nenv-lcm supports the management of environments saved to Object Storage, including uploading, downloading, listing, and deleting available environments.Creating Conda Environments
\n\n
\ncreate command to create an environment and install the Python keras package.\n
\ncreate command, enter create --help in a %conda paragraph.Installing and Uninstalling Libraries
\ninstall and uninstall commands.install and uninstall commmands, type install --help and uninstall --help in a %conda paragraph.uninstall command. Let's uninstall the pytorch package from the current environment.env remove command. Remove the myenv environment and verify it was removed. A best practice is to deactivate the environment prior to removal.env remove command, type env remove --help in the %conda interpreter.Specify Packages for Installation
\nInstall Packages from the
\nconda-forge Channel\n
\nconda command searches a set of channels. By default, packages are automatically downloaded and updated from the [default channel] (https://repo.anaconda.com/pkgs/).conda-forge channel] (https://conda-forge.org/), a community channel made up of thousands of contributors, in the following examples.Install a Specific Version of a Package
\n\n
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+ "title" : "Create an environment using conda-forge ",
+ "hasTitle" : true,
+ "message" : [
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+ "create -n mychannelenv -c conda-forge python=3.12.6",
+ "",
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Once for detailed output,\n twice for INFO logging, thrice for DEBUG logging, four\n times for TRACE logging.\n --no-plugins Disable all plugins that are not built into conda.\n -V, --version Show the conda version number and exit.\n\ncommands:\n The following built-in and plugins subcommands are available.\n\n COMMAND\n activate Activate a conda environment.\n clean Remove unused packages and caches.\n compare Compare packages between conda environments.\n config Modify configuration values in .condarc.\n content-trust Signing and verification tools for Conda\n create Create a new conda environment from a list of specified\n packages.\n deactivate Deactivate the current active conda environment.\n doctor Display a health report for your environment.\n env-lcm See `conda env-lcm --help`.\n info Display information about current conda install.\n init Initialize conda for shell interaction.\n install Install a list of packages into a specified conda\n environment.\n list List installed packages in a conda environment.\n notices Retrieve latest channel notifications.\n pack See `conda pack --help`.\n package Create low-level conda packages. (EXPERIMENTAL)\n remove (uninstall)\n Remove a list of packages from a specified conda\n environment.\n rename Rename an existing environment.\n repoquery Advanced search for repodata.\n run Run an executable in a conda environment.\n search Search for packages and display associated information\n using the MatchSpec format.\n update (upgrade) Update conda packages to the latest compatible version.\n\n\n\nConda environment 'mychannelenv' activated\n\n",
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hb9d3cd8_2 32 KB conda-forge\n libbrotlienc-1.1.0 | hb9d3cd8_2 275 KB conda-forge\n libcblas-3.9.0 |28_he106b2a_openblas 16 KB conda-forge\n libdeflate-1.23 | h4ddbbb0_0 71 KB conda-forge\n libgfortran-14.2.0 | h69a702a_1 53 KB conda-forge\n libgfortran5-14.2.0 | hd5240d6_1 1.4 MB conda-forge\n libjpeg-turbo-3.0.0 | hd590300_1 604 KB conda-forge\n liblapack-3.9.0 |28_h7ac8fdf_openblas 16 KB conda-forge\n libopenblas-0.3.28 |pthreads_h94d23a6_1 5.3 MB conda-forge\n libpng-1.6.47 | h943b412_0 282 KB conda-forge\n libstdcxx-14.2.0 | hc0a3c3a_1 3.7 MB conda-forge\n libstdcxx-ng-14.2.0 | h4852527_1 53 KB conda-forge\n libtiff-4.7.0 | hd9ff511_3 418 KB conda-forge\n libwebp-base-1.5.0 | h851e524_0 420 KB conda-forge\n libxcb-1.17.0 | h8a09558_0 387 KB conda-forge\n matplotlib-base-3.10.0 | py312hd3ec401_0 7.8 MB conda-forge\n munkres-1.1.4 | pyh9f0ad1d_0 12 KB conda-forge\n numpy-2.2.3 | py312h72c5963_0 8.1 MB conda-forge\n openjpeg-2.5.3 | h5fbd93e_0 335 KB conda-forge\n packaging-24.2 | 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+ "## Section 2: Create a Conda Environment and Upload to Object Storage",
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+ "This section demonstrates creating two conda environments and uploading them to Object Storage. An environment saved in Object Storage can be used by all of the Autonomous Database users in the database, and will remain in Object Storage until ADMIN deletes it.",
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+ "As ADMIN user:",
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+ "- Delete the environments from Object Storage. "
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+ "# OML Third-Party Packages - Python Environment Usage",
+ "",
+ "Oracle Machine Learning Notebooks provide a conda interpreter to install third-party Python and R packages in a conda environment for use within OML Notebooks sessions, as well as within OML4R and OML4Py embedded execution invocations. Conda is an open source package and environment management system that enables the use of virtual environments containing third-party R and Python packages. With conda environments, you can install and update packages and their dependencies, and switch between environments to use project-specific packages. ",
+ "",
+ "Administrators create conda environments and install packages that can then be accessed by non-administrator users and loaded into their OML Notebooks session. The conda environments can be used in the Python and the OML4Py Python, SQL, and REST APIs, and R and the OML4R R, SQL, and REST APIs.",
+ "",
+ "In this notebook, we demonstrate a typical workflow for third-party environment usage in OML notebooks using Python and OML4Py. The OML user downloads and uses the packages in conda environments that were previously created and saved to an Object Storage bucket folder associated with the Autonomous Database.",
+ "",
+ "The template notebook titled, *OML Third-Party Packages - Environment Creation*, the ADMIN user creates a conda environment, installs packages, and uploads the conda environments to Object Storage.",
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+ "Copyright (c) 2025 Oracle Corporation ",
+ "The Universal Permissive License (UPL), Version 1.0"
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\nlist-saved-envs to list and return the details for a named conda environment in Object Storage. The environment name, size, and number of packages is returned along with the description and tags provided by the ADMIN when uploading the environment.mypyenv was created for use with Python and OML4Py.download and activate functions in a %conda paragraph. An activated environment is available until it is deactivated.
pyqAppendHostAce function to enable the OML user to access network services and resources from the database, where the root domain is the data center region where Autonomous Database resides. For example, if your username is OMLUSER and your Autonomous Database resides in the Ashburn region, the root domain is adb.us-ashburn-1.oraclecloudapps.com.pyqSetAuthToken function to persist the authorization token issued by a cloud host for use with the upcoming SQL calls.env_name field when using SQL, and the envName field when using REST.\n
\npyqJobStatus function. If the job is still running, the return value will be job is still running. When the job completes, a job ID and result location are returned.End of script
\n",
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diff --git a/machine-learning/notebooks-oml/python/OML4Py -0- Tour.dsnb b/machine-learning/notebooks-oml/python/OML4Py -0- Tour.dsnb
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+[{"layout":null,"template":null,"templateConfig":null,"name":"OML4Py -0- Tour","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1737140124407,"interpreter":"md.low","endTime":1737140124936,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Oracle Machine Learning for Python (OML4Py)","","***Oracle Machine Learning for Python*** (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data. Designed for problems involving both large and small data volumes, OML4Py integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for statistical, machine learning, and visualization analyses on database tables and views using Python syntax. Many familiar Python functions are overloaded that translate Python behavior into SQL for running in-database, as well as automated machine learning capabilities. In this notebook, we highlight the range of OML4Py features.","","* Automated Machine Learning (AutoML)","* Machine learning model building and scoring","* Creating database tables","* Transparency layer functionality","* Embedded Python execution","* REST API invocation of embedded Python execution","","This notebook is the first of a series (0 through 5) that is intended to introduce the range of OML4Py functionality through short examples. ","","Copyright (c) 2025 Oracle Corporation ","","###### The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1737140125443,"interpreter":"md.low","endTime":1737140125909,"results":[{"message":"Oracle Machine Learning for Python (OML4Py)<\/h1>\n
\n
The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n
\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","# Import libraries supporting OML4Py","","","Note: The Oracle Database connectivity package ***python-oracledb*** is used to support database access. ","You can get access to the underlying connection to use ***python-oracledb*** directly via the function ***oml.cursor***. ","See the general ***cx_Oracle*** documentation <\/a> for more information. ","","For Oracle Autonomous Database, a database connection is automatically established provided you have an appropriate interpreter ","binding specified and import the ***oml*** package. "],"enabled":true,"result":{"startTime":1737140126398,"interpreter":"md.low","endTime":1737140126863,"results":[{"message":"Import libraries supporting OML4Py<\/h1>\n
Get a proxy object for a database table<\/h2>\n
Automated Machine Learning (AutoML)<\/h2>\n
\n
Automated Algorithm Selection<\/h3>\n
\n
\n
\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":null,"message":["%python","","as_bc = automl.AlgorithmSelection(mining_function='classification', score_metric='accuracy', parallel=4)","","alg_ranking = as_bc.select(BREASTCANCER_X, BREASTCANCER_y, k=4) ","","print(\"Ranked algorithms:\\n\", alg_ranking)","","best_alg = alg_ranking[0][0]","","print(\"\\nBest algorithm:\\n\", best_alg)"],"enabled":true,"result":{"startTime":1737140137770,"interpreter":"python.low","endTime":1737140175005,"results":[{"message":"UserWarning:Parallelism exceeds the DOP limit 2 (reverting to parallel=2)\nRanked algorithms:\n [('svm_gaussian', 0.9693819151783754), ('glm_ridge', 0.9687707743017476), ('svm_linear', 0.9652491110455712), ('nn', 0.9623719734339202)]\n\nBest algorithm:\n svm_gaussian\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":"{}","row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","### Automated Feature Selection","","In this example, we define a ***FeatureSelection*** object with score metric 'accuracy' and invoke ***reduce*** specifying the neural network algorithm determined above and the data set proxy object. ","","Automated Feature Selection provides a list of selected features, or columns. Use this set to produce an OML DataFrame with this column subset."," ","See the documentation for Automated Feature Selection<\/a> for more details.","","We see the set of selected columns. Replace the `'nn'` in the code below with `'svm_linear'` (in single quotes) for example to see if different columns are selected for different algorithms.","","We use the selected set of columns to also reduce the train and test datasets created earlier for use in our manual model building later in this script. "],"enabled":true,"result":{"startTime":1737140175488,"interpreter":"md.low","endTime":1737140175950,"results":[{"message":"Automated Feature Selection<\/h3>\n
'nn'<\/code> in the code below with 'svm_linear'<\/code> (in single quotes) for example to see if different columns are selected for different algorithms.<\/p>\nAutomated Model Tuning<\/h3>\n