OML4SQL Feature Extraction SVD.dsnb

[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Feature Extraction SVD","description":null,"readOnly":false,"type":"low","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715726809566,"interpreter":"md.low","endTime":1715726809624,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":0,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","# OML4SQL Feature Extraction: Singular Value Decomposition","","In this notebook, we demonstrate how to use the in-database SVD decomposion for dimensionality reduction.","","We use the customer insurance lifetime value data. This dataset contains financial information about the customer and the decision of buying insurance or not. ","","We show that Principal Component Analysis can be achieved using SVD results.","","The dataset `CUSTOMER_INSURANCE_LTV` is generated by the `\"OML Run-me-first\"` notebook, which `MUST` be run before this notebook.","","---","","##### `IMPORTANT`: The `\"OML Run-me-first\"` notebook is available under the menu Templates -> Examples and is a pre-requisite to the current notebook.","","---","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715726809697,"interpreter":"md.low","endTime":1715726809755,"results":[{"message":"<h1 id=\"oml4sql-feature-extraction-singular-value-decomposition\">OML4SQL Feature Extraction: Singular Value Decomposition<\/h1>\n<p>In this notebook, we demonstrate how to use the in-database SVD decomposion for dimensionality reduction.<\/p>\n<p>We use the customer insurance lifetime value data. This dataset contains financial information about the customer and the decision of buying insurance or not.<\/p>\n<p>We show that Principal Component Analysis can be achieved using SVD results.<\/p>\n<p>The dataset <code>CUSTOMER_INSURANCE_LTV<\/code> is generated by the <code>&quot;OML Run-me-first&quot;<\/code> notebook, which <code>MUST<\/code> be run before this notebook.<\/p>\n<hr />\n<h5 id=\"important-the-oml-run-me-first-notebook-is-available-under-the-menu-templates---examples-and-is-a-pre-requisite-to-the-current-notebook\"><code>IMPORTANT<\/code>: The <code>&quot;OML Run-me-first&quot;<\/code> notebook is available under the menu Templates -&gt; Examples and is a pre-requisite to the current notebook.<\/h5>\n<hr />\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information ...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-feature-extraction\" target=\"_blank\">OML Feature Extraction<\/a>","* <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html\" target=\"_blank\">OML4SQL SVD Documentation<\/a>"],"enabled":true,"result":{"startTime":1715726809855,"interpreter":"md.low","endTime":1715726809917,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-feature-extraction\" target=\"_blank\">OML Feature Extraction<\/a><\/li>\n<li><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html\" target=\"_blank\">OML4SQL SVD Documentation<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Overview of the data","message":["%sql","","SELECT * ","FROM CUSTOMER_INSURANCE_LTV","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726809995,"interpreter":"sql.low","endTime":1715726810071,"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 \t0\t5\t117\t0\t5\t0\t66654\tCU15292 \tF \tPROF-3\t2500\tSouthwest\t1\t117\tNo\t0\t5\t21\t53.06\t21263.5\t0\t0\t25\t0\tMEDIUM\tCEOLA\t1\t1\tHARRISON\nSINGLE\tUT \t0\t3\t0\t250\t3\t0\t61716\tCU15294 \tM \tProgrammer/Developer\t1500\tSouthwest\t0\t0\tNo\t3\t5\t26\t53.04\t13529\t0\t2\t25\t2\tLOW\tLLOYD\t0\t0\tHOLLEY\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create a view that contains a set of numerical and categorical columns","message":["%script","","CREATE OR REPLACE VIEW CUST_V AS","SELECT CUSTOMER_ID, CREDIT_BALANCE, CUSTOMER_TENURE, MORTGAGE_AMOUNT, MARITAL_STATUS, GENDER, REGION","FROM CUSTOMER_INSURANCE_LTV"],"enabled":true,"result":{"startTime":1715726810179,"interpreter":"script.low","endTime":1715726810253,"results":[{"message":"\nView CUST_V created.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from CUST_V","message":["%sql","","SELECT * FROM CUST_V","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726810330,"interpreter":"sql.low","endTime":1715726810406,"results":[{"message":"CUSTOMER_ID\tCREDIT_BALANCE\tCUSTOMER_TENURE\tMORTGAGE_AMOUNT\tMARITAL_STATUS\tGENDER\tREGION\nCU15154 \t0\t3\t0\tSINGLE\tM \tWest\nCU15155 \t0\t4\t0\tSINGLE\tM \tNorthEast\nCU15157 \t0\t3\t1000\tMARRIED\tM \tMidwest\nCU15286 \t0\t5\t1200\tMARRIED\tF \tSouthwest\nCU15287 \t0\t4\t1800\tMARRIED\tF \tSouthwest\nCU15289 \t0\t1\t1400\tMARRIED\tM \tSouthwest\nCU15290 \t0\t3\t578\tSINGLE\tM \tSouthwest\nCU15291 \t0\t3\t0\tSINGLE\tM \tSouthwest\nCU15292 \t0\t5\t117\tSINGLE\tF \tSouthwest\nCU15294 \t0\t3\t0\tSINGLE\tM \tSouthwest\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVD","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","","* Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117');\">Singular Value Decompositionn<\/a>","","* Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","","* This setting is used to prune features. Define the minimum value the eigenvalue of a feature as a share of the first eigenvalue to not to prune. Default value is data driven. "," The value required is between 0 and 1 (including the edges), and the default data driven.","> v_setlst('SVDS_TOLERANCE') := '0.05';","","* Setting to define whether to use SVD or PCA scoring for the model. When the build data is scored with SVD, the projections will be the same as the U matrix. When the build data is scored with PCA, the projections will be the product of the U and S matrices."," The value required is either SVDS_SCORING_SVD or SVDS_SCORING_PCA, and the default is SVDS_SCORING_SVD. ","> v_setlst('SVDS_SCORING_MODE') := 'SVDS_SCORING_SVD';","","* Specify the random seed value used for initializing the sampling matrix used by the Stochastic SVD solver. The SVD Solver must be set to SSVD or STEIGEN. "," The value required is between 0 and 4,294,967,296 (including the edges), and the default is 0.","> v_setlst('SVDS_RANDOM_SEED') := '0';"," ","* This setting is configures the number of columns in the sampling matrix used by the Stochastic SVD solver. The number of columns in this matrix is equal to the requested number of features plus the oversampling setting. The SVD Solver must be set to SSVD or STEIGEN. "," The value required is between 1 and 5,000 (including the edges).","> v_setlst('SVDS_OVER_SAMPLING') := '100';","","* The power iteration setting improves the accuracy of the SSVD solver. The SVD Solver must be set to SSVD or STEIGEN. "," The value required is between 0 and 20 (including the edges), and the default is 2.","> v_setlst('SVDS_POWER_ITERATIONS') := '2';"," ","* Indicates whether or not to persist the U Matrix produced by SVD."," The U matrix in SVD has as many rows as the number of rows in the build data. To avoid creating a large model, the U matrix is persisted only when SVDS_U_MATRIX_OUTPUT is enabled."," When SVDS_U_MATRIX_OUTPUT is enabled, the build data must include a case ID. If no case ID is present and the U matrix is requested, then an exception is raised."," The value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE. ","> v_setlst('SVDS_U_MATRIX_OUTPUT') := 'SVDS_U_MATRIX_DISABLE';"," ","* The setting SVDS_SOLVER indicates the solver to be used for computing SVD of the data. In the case of PCA, the solver setting indicates the type of SVD solver used to compute the PCA for the data. When this setting is not specified the solver type selection is data driven. If the number of attributes is greater than 3,240, then the default wide solver is used. Otherwise, the default narrow solver is selected."," The following are the group of solvers:"," * Narrow data solvers: for matrices with up to 11500 attributes (TSEIGEN) or up to 8100 attributes (TSSVD)."," * Wide data solvers: for matrices up to 1 million attributes.",""," For narrow data solvers:"," * Tall-Skinny SVD uses QR computation TSVD (SVDS_SOLVER_TSSVD)"," * Tall-Skinny SVD uses eigenvalue computation, TSEIGEN (SVDS_SOLVER_TSEIGEN), is the default solver for narrow data.",""," For wide data solvers:"," * Stochastic SVD uses QR computation SSVD (SVDS_SOLVER_SSVD), is the default solver for wide data solvers."," * Stochastic SVD uses eigenvalue computations, STEIGEN (SVDS_SOLVER_STEIGEN).",""," The value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE. "," v_setlst('SVDS_SOLVER') := 'SVDS_SOLVER_STEIGEN';","","---","","#### Other interesting overrides for Data Preparation for SVD","","Oracle Machine Learning supports fully Automatic Data Preparation (ADP), user-directed general data preparation, and user-specified embedded data preparation. The PREP_* settings enable the user to request fully automated or user-directed general data preparation. By default, fully Automatic Data Preparation (ON) is enabled. ","","A complete list of settings can be found in the Documentation link:","","* Automatic Data Preparation: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D');\">Automatic Data Preparation<\/a>","","* This setting is used to specify Automatic Data Preparation."," The value required is either ON or OFF, and the default is ON. ","> v_setlst('PREP_AUTO') := 'ON';"," ","The model uses heuristics to transform the build data according to the requirements of the algorithm. ","","#### User defined transformations","","Instead of fully ADP, the user can request that the data be shifted and/or scaled with the PREP_SCALE* and PREP_SHIFT* settings. The transformation instructions are stored with the model and reused whenever the model is applied. The model settings can be viewed in USER_MINING_MODEL_SETTINGS. ","","* This setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:"," PREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization."," PREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].","> v_setlst('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';","","* This setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:"," PREP_SHIFT_MEAN: Results in subtracting the average of the column from each value."," PREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.","> v_setlst('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';","","* This setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].","> v_setlst('PREP_SCALE_NNUM') := 'PREP_SCALE_MAXABS';","","---"],"enabled":true,"result":{"startTime":1715726810487,"interpreter":"md.low","endTime":1715726810561,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svd\">Examples of possible setting overrides for SVD<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-684B3705-A314-458B-A6D9-3191DF376117');\">Singular Value Decompositionn<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>This setting is used to prune features. Define the minimum value the eigenvalue of a feature as a share of the first eigenvalue to not to prune. Default value is data driven.\nThe value required is between 0 and 1 (including the edges), and the default data driven.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_TOLERANCE') := '0.05';<\/p>\n<\/blockquote>\n<ul>\n<li>Setting to define whether to use SVD or PCA scoring for the model. When the build data is scored with SVD, the projections will be the same as the U matrix. When the build data is scored with PCA, the projections will be the product of the U and S matrices.\nThe value required is either SVDS_SCORING_SVD or SVDS_SCORING_PCA, and the default is SVDS_SCORING_SVD.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_SCORING_MODE') := 'SVDS_SCORING_SVD';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify the random seed value used for initializing the sampling matrix used by the Stochastic SVD solver. The SVD Solver must be set to SSVD or STEIGEN.\nThe value required is between 0 and 4,294,967,296 (including the edges), and the default is 0.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_RANDOM_SEED') := '0';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting is configures the number of columns in the sampling matrix used by the Stochastic SVD solver. The number of columns in this matrix is equal to the requested number of features plus the oversampling setting. The SVD Solver must be set to SSVD or STEIGEN.\nThe value required is between 1 and 5,000 (including the edges).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_OVER_SAMPLING') := '100';<\/p>\n<\/blockquote>\n<ul>\n<li>The power iteration setting improves the accuracy of the SSVD solver. The SVD Solver must be set to SSVD or STEIGEN.\nThe value required is between 0 and 20 (including the edges), and the default is 2.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_POWER_ITERATIONS') := '2';<\/p>\n<\/blockquote>\n<ul>\n<li>Indicates whether or not to persist the U Matrix produced by SVD.\nThe U matrix in SVD has as many rows as the number of rows in the build data. To avoid creating a large model, the U matrix is persisted only when SVDS_U_MATRIX_OUTPUT is enabled.\nWhen SVDS_U_MATRIX_OUTPUT is enabled, the build data must include a case ID. If no case ID is present and the U matrix is requested, then an exception is raised.\nThe value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVDS_U_MATRIX_OUTPUT') := 'SVDS_U_MATRIX_DISABLE';<\/p>\n<\/blockquote>\n<ul>\n<li>\n<p>The setting SVDS_SOLVER indicates the solver to be used for computing SVD of the data. In the case of PCA, the solver setting indicates the type of SVD solver used to compute the PCA for the data. When this setting is not specified the solver type selection is data driven. If the number of attributes is greater than 3,240, then the default wide solver is used. Otherwise, the default narrow solver is selected.\nThe following are the group of solvers:<\/p>\n<ul>\n<li>Narrow data solvers: for matrices with up to 11500 attributes (TSEIGEN) or up to 8100 attributes (TSSVD).<\/li>\n<li>Wide data solvers: for matrices up to 1 million attributes.<\/li>\n<\/ul>\n<p>For narrow data solvers:<\/p>\n<ul>\n<li>Tall-Skinny SVD uses QR computation TSVD (SVDS_SOLVER_TSSVD)<\/li>\n<li>Tall-Skinny SVD uses eigenvalue computation, TSEIGEN (SVDS_SOLVER_TSEIGEN), is the default solver for narrow data.<\/li>\n<\/ul>\n<p>For wide data solvers:<\/p>\n<ul>\n<li>Stochastic SVD uses QR computation SSVD (SVDS_SOLVER_SSVD), is the default solver for wide data solvers.<\/li>\n<li>Stochastic SVD uses eigenvalue computations, STEIGEN (SVDS_SOLVER_STEIGEN).<\/li>\n<\/ul>\n<p>The value required is either SVDS_U_MATRIX_ENABLE or SVDS_U_MATRIX_DISABLE, and the default is SVDS_U_MATRIX_DISABLE.\nv_setlst('SVDS_SOLVER') := 'SVDS_SOLVER_STEIGEN';<\/p>\n<\/li>\n<\/ul>\n<hr />\n<h4 id=\"other-interesting-overrides-for-data-preparation-for-svd\">Other interesting overrides for Data Preparation for SVD<\/h4>\n<p>Oracle Machine Learning supports fully Automatic Data Preparation (ADP), user-directed general data preparation, and user-specified embedded data preparation. The PREP_* settings enable the user to request fully automated or user-directed general data preparation. By default, fully Automatic Data Preparation (ON) is enabled.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Automatic Data Preparation: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-5043274C-C753-47DE-9E60-D8528ADAC78D');\">Automatic Data Preparation<\/a><\/p>\n<\/li>\n<li>\n<p>This setting is used to specify Automatic Data Preparation.\nThe value required is either ON or OFF, and the default is ON.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_AUTO') := 'ON';<\/p>\n<\/blockquote>\n<p>The model uses heuristics to transform the build data according to the requirements of the algorithm.<\/p>\n<h4 id=\"user-defined-transformations\">User defined transformations<\/h4>\n<p>Instead of fully ADP, the user can request that the data be shifted and/or scaled with the PREP_SCALE* and PREP_SHIFT* settings. The transformation instructions are stored with the model and reused whenever the model is applied. The model settings can be viewed in USER_MINING_MODEL_SETTINGS.<\/p>\n<ul>\n<li>This setting enables scaling data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:\nPREP_SCALE_STDDEV: A request to divide the column values by the standard deviation of the column and is often provided together with PREP_SHIFT_MEAN to yield z-score normalization.\nPREP_SCALE_RANGE: A request to divide the column values by the range of values and is often provided together with PREP_SHIFT_MIN to yield a range of [0,1].<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting enables centering data preparation for two-dimensional numeric columns. PREP_AUTO must be OFF for this setting to take effect. The following are the possible values:\nPREP_SHIFT_MEAN: Results in subtracting the average of the column from each value.\nPREP_SHIFT_MIN: Results in subtracting the minimum of the column from each value.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';<\/p>\n<\/blockquote>\n<ul>\n<li>This setting enables scaling data preparation for nested numeric columns. PREP_AUTO must be OFF for this setting to take effect. If specified, then the valid value for this setting is PREP_SCALE_MAXABS, which yields data in the range of [-1,1].<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('PREP_SCALE_NNUM') := 'PREP_SCALE_MAXABS';<\/p>\n<\/blockquote>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create SVD model with manual Data Preparation options","message":["%script","","BEGIN "," DBMS_DATA_MINING.DROP_MODEL('SVD_CUST_LTV_MDL');"," EXCEPTION WHEN OTHERS THEN NULL; ","END;","/","","DECLARE"," V_SETLST DBMS_DATA_MINING.SETTING_LIST;","BEGIN"," V_SETLST('ALGO_NAME') := 'ALGO_SINGULAR_VALUE_DECOMP';"," V_SETLST('SVDS_SCORING_MODE') := 'SVDS_SCORING_PCA';"," V_SETLST('PREP_AUTO') := 'OFF';"," V_SETLST('PREP_SCALE_2DNUM') := 'PREP_SCALE_STDDEV';"," V_SETLST('PREP_SHIFT_2DNUM') := 'PREP_SHIFT_MEAN';"," DBMS_DATA_MINING.CREATE_MODEL2("," MODEL_NAME => 'SVD_CUST_LTV_MDL',"," MINING_FUNCTION => 'FEATURE_EXTRACTION',"," CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',"," DATA_QUERY => 'SELECT * FROM CUST_V',"," SET_LIST => V_SETLST);","END;"],"enabled":true,"result":{"startTime":1715726810645,"interpreter":"script.low","endTime":1715726812341,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"List the SVD model views available","message":["%sql","","SELECT 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Percentage of Cumulative Variance vs Feature #","message":["%sql","","SELECT FEATURE_ID, round(PCT_CUM_VARIANCE,3) PCT_CUM_VARIANCE","FROM DM$VESVD_CUST_LTV_MDL"],"enabled":true,"result":{"startTime":1715726812586,"interpreter":"sql.low","endTime":1715726812661,"results":[{"message":"FEATURE_ID\tPCT_CUM_VARIANCE\n1\t22.909\n2\t41.255\n3\t57.786\n4\t72.747\n5\t78.784\n6\t84.083\n7\t89.268\n8\t93.247\n9\t96.391\n10\t98.001\n11\t99.177\n12\t99.892\n13\t100\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display coefficients of the principal components mapping from original attributes","message":["%sql","","SELECT FEATURE_ID, ATTRIBUTE_NAME, ATTRIBUTE_VALUE, VALUE","FROM 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USING *),4) PCA_PROJ1,"," ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 2 USING *),4) PCA_PROJ2,"," ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 3 USING *),4) PCA_PROJ3,"," ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 4 USING *),4) PCA_PROJ4","FROM CUST_V","ORDER BY 1, 2","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726813398,"interpreter":"sql.low","endTime":1715726813516,"results":[{"message":"CUSTOMER_ID\tPCA_PROJ1\tPCA_PROJ2\tPCA_PROJ3\tPCA_PROJ4\nCU1 \t-3.9247\t2.8396\t1.6307\t4.3253\nCU10 \t-0.7384\t1.8562\t2.2444\t-1.4873\nCU1000 \t0.8149\t1.1414\t-1.1088\t0.0841\nCU10000 \t1.1401\t0.6253\t0.3622\t0.2917\nCU10001 \t0.2785\t1.0078\t-1.1183\t0.2\nCU10003 \t0.2\t0.366\t1.1067\t0.8741\nCU10004 \t1.4874\t0.3463\t0.2974\t-0.0874\nCU10005 \t1.539\t0.4053\t0.3482\t-0.0923\nCU10006 \t0.7132\t0.5235\t0.3479\t0.3988\nCU10007 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We first drop the settings table, create it, and then populate it with settings before building the model.","","---"],"enabled":true,"result":{"startTime":1715726813588,"interpreter":"md.low","endTime":1715726813645,"results":[{"message":"<h3 id=\"create-an-svd-feature-extraction-model-using-settings-table\">Create an SVD Feature Extraction Model using Settings Table<\/h3>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":" Clean up old settings table","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE SVD_CUSTOMER_LTV_SETTINGS';","EXCEPTION WHEN OTHERS THEN NULL; END;","/"],"enabled":true,"result":{"startTime":1715726813720,"interpreter":"script.low","endTime":1715726813944,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"Create settings table","message":["%sql","","CREATE TABLE SVD_CUSTOMER_LTV_SETTINGS ("," SETTING_NAME VARCHAR2(30),"," SETTING_VALUE VARCHAR2(4000));"],"enabled":true,"result":{"startTime":1715726814016,"interpreter":"sql.low","endTime":1715726814091,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Specify settings","message":["%script","","BEGIN "," INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_SINGULAR_VALUE_DECOMP);"," INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_AUTO, DBMS_DATA_MINING.PREP_AUTO_OFF); "," INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.SVDS_SCORING_MODE, DBMS_DATA_MINING.SVDS_SCORING_PCA);"," INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_SHIFT_2DNUM, DBMS_DATA_MINING.PREP_SHIFT_MEAN); "," INSERT INTO SVD_CUSTOMER_LTV_SETTINGS (SETTING_NAME, SETTING_VALUE) VALUES (DBMS_DATA_MINING.PREP_SCALE_2DNUM, DBMS_DATA_MINING.PREP_SCALE_STDDEV); ","END;"],"enabled":true,"result":{"startTime":1715726814163,"interpreter":"script.low","endTime":1715726814257,"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":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build an SVD model","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVD_CUST_LTV_MDL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN"," DBMS_DATA_MINING.CREATE_MODEL("," MODEL_NAME => 'SVD_CUST_LTV_MDL',"," MINING_FUNCTION => DBMS_DATA_MINING.FEATURE_EXTRACTION,"," DATA_TABLE_NAME => 'CUST_V',"," CASE_ID_COLUMN_NAME => 'CUSTOMER_ID',"," SETTINGS_TABLE_NAME => 'SVD_CUSTOMER_LTV_SETTINGS');","END;","/"],"enabled":true,"result":{"startTime":1715726814329,"interpreter":"script.low","endTime":1715726815623,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display coefficients of the principal components mapping from original attributes","message":["%sql","","SELECT FEATURE_ID, ATTRIBUTE_NAME, ATTRIBUTE_VALUE, VALUE","FROM DM$VVSVD_CUST_LTV_MDL","ORDER BY FEATURE_ID, ATTRIBUTE_NAME","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726815906,"interpreter":"sql.low","endTime":1715726816001,"results":[{"message":"FEATURE_ID\tATTRIBUTE_NAME\tATTRIBUTE_VALUE\tVALUE\n1\tCREDIT_BALANCE\t\t-0.3224658391288337\n1\tCUSTOMER_TENURE\t\t0.15464257875070309\n1\tGENDER\tF \t-0.05888309917813106\n1\tGENDER\tM \t0.5325070626402109\n1\tMARITAL_STATUS\tDIVORCED\t0.029778428687600597\n1\tMARITAL_STATUS\tWIDOWED\t-0.03202096481496996\n1\tMARITAL_STATUS\tSINGLE\t0.35381996357540935\n1\tMARITAL_STATUS\tOTHER\t-0.023346721190061374\n1\tMARITAL_STATUS\tMARRIED\t0.1453932572041015\n1\tMORTGAGE_AMOUNT\t\t-0.612280020773312\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display first four principal components","message":["%sql","","SELECT CUSTOMER_ID, "," ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 1 USING *),4) PROJ1,"," ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 2 USING *),4) PROJ2,"," ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 3 USING *),4) PROJ3,"," ROUND(FEATURE_VALUE(SVD_CUST_LTV_MDL, 4 USING *),4) PROJ4","FROM CUST_V","ORDER BY 1, 2","FETCH FIRST 10 ROWS ONLY"],"enabled":true,"result":{"startTime":1715726816089,"interpreter":"sql.low","endTime":1715726816212,"results":[{"message":"CUSTOMER_ID\tPROJ1\tPROJ2\tPROJ3\tPROJ4\nCU1 \t-3.9247\t2.8396\t1.6307\t4.3253\nCU10 \t-0.7384\t1.8562\t2.2444\t-1.4873\nCU1000 \t0.8149\t1.1414\t-1.1088\t0.0841\nCU10000 \t1.1401\t0.6253\t0.3622\t0.2917\nCU10001 \t0.2785\t1.0078\t-1.1183\t0.2\nCU10003 \t0.2\t0.366\t1.1067\t0.8741\nCU10004 \t1.4874\t0.3463\t0.2974\t-0.0874\nCU10005 \t1.539\t0.4053\t0.3482\t-0.0923\nCU10006 \t0.7132\t0.5235\t0.3479\t0.3988\nCU10007 \t1.4601\t0.5639\t0.3639\t0.0668\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715726816286,"interpreter":"md.low","endTime":1715726816346,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":[],"enabled":true,"result":{"startTime":1715726816430,"interpreter":"md.low","endTime":1715726816488,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]