SAP HANA Predictive Analysis Library (PAL)

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Table 203: Table Column Column Data Type Description Constraint Result 1st column Integer, varchar, or nvarchar ID Note: If the SE LECTED_FEATURES parameter is specified, the column data type must be varchar or nvarchar. 2nd column Integer or double Value A i ● ● ● A 0 : the intercept A 1 : the beta coefficient for X 1 A 2 : the beta coefficient for X 2 ● ... 3rd column Double t-value of coefficients. Note: This column is available only when the STAT_INF parameter is set to 1. 4th column Double Pr(>|t|) of coefficients. Note: This column is available only when the STAT_INF parameter is set to 1. Fitted Data 1st column Integer, bigint, varchar, or nvarchar ID 2nd column Integer or double Value Y i Significance 1st column Varchar or nvarchar Name (R^2 / F) 2nd column Double Value PMML Result 1st column Integer ID 2nd column CLOB, varchar, or nvarchar Multiple linear regression model in PMML format Example Assume that: ● ● ● DM_PAL is a schema belonging to USER1; and USER1 has been assigned the AFLPM_CREATOR_ERASER_EXECUTE role; and USER1 has been assigned the AFL__SYS_AFL_AFLPAL_EXECUTE or AFL__SYS_AFL_AFLPAL_EXECUTE_WITH_GRANT_OPTION role. 270 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
Example 1: Fitting multiple linear regression model without penalties SET SCHEMA DM_PAL; DROP TYPE PAL_MLR_DATA_T; CREATE TYPE PAL_MLR_DATA_T AS TABLE( "ID" INT,"Y" DOUBLE,"V1" DOUBLE, "V2" DOUBLE); DROP TYPE PAL_MLR_RESULT_T; CREATE TYPE PAL_MLR_RESULT_T AS TABLE("Coefficient" varchar(50),"CoefficientValue" DOUBLE); DROP TYPE PAL_MLR_FITTED_T; CREATE TYPE PAL_MLR_FITTED_T AS TABLE("ID" INT,"Fitted" DOUBLE); DROP TYPE PAL_MLR_SIGNIFICANCE_T; CREATE TYPE PAL_MLR_SIGNIFICANCE_T AS TABLE("NAME" varchar(50),"VALUE" DOUBLE); DROP TYPE PAL_MLR_PMMLMODEL_T; CREATE TYPE PAL_MLR_PMMLMODEL_T AS TABLE("ID" INT,"Model" varchar(5000)); DROP TYPE PAL_CONTROL_T; CREATE TYPE PAL_CONTROL_T AS TABLE("NAME" VARCHAR(100), "INTARGS" INT, "DOUBLEARGS" DOUBLE,"STRINGARGS" VARCHAR(100)); DROP TABLE PAL_MLR_PDATA_TBL; CREATE COLUMN TABLE PAL_MLR_PDATA_TBL("POSITION" INT, "SCHEMA_NAME" NVARCHAR(256), "TYPE_NAME" NVARCHAR(256), "PARAMETER_TYPE" VARCHAR(7)); INSERT INTO PAL_MLR_PDATA_TBL values (1,'DM_PAL','PAL_MLR_DATA_T','IN'); INSERT INTO PAL_MLR_PDATA_TBL values (2,'DM_PAL','PAL_CONTROL_T','IN'); INSERT INTO PAL_MLR_PDATA_TBL values (3,'DM_PAL','PAL_MLR_RESULT_T','OUT'); INSERT INTO PAL_MLR_PDATA_TBL values (4,'DM_PAL','PAL_MLR_FITTED_T','OUT'); INSERT INTO PAL_MLR_PDATA_TBL values (5,'DM_PAL','PAL_MLR_SIGNIFICANCE_T','OUT'); INSERT INTO PAL_MLR_PDATA_TBL values (6,'DM_PAL','PAL_MLR_PMMLMODEL_T','OUT'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('DM_PAL','PAL_LR_PROC'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL','LRREGRESSION','DM_PAL','PAL_LR_PRO C',PAL_MLR_PDATA_TBL); DROP TABLE #PAL_CONTROL_TBL; CREATE LOCAL TEMPORARY COLUMN TABLE #PAL_CONTROL_TBL ("NAME" VARCHAR(100), "INTARGS" INT, "DOUBLEARGS" DOUBLE,"STRINGARGS" VARCHAR(100)); INSERT INTO #PAL_CONTROL_TBL VALUES ('THREAD_NUMBER',8,null,null); INSERT INTO #PAL_CONTROL_TBL VALUES ('PMML_EXPORT',0,null,null); DROP TABLE PAL_MLR_DATA_TBL; CREATE COLUMN TABLE PAL_MLR_DATA_TBL ( "ID" INT,"Y" DOUBLE,"V1" DOUBLE, "V2" DOUBLE); INSERT INTO PAL_MLR_DATA_TBL VALUES (0,0.5,0.13,0.33); INSERT INTO PAL_MLR_DATA_TBL VALUES (1,0.15,0.14,0.34); INSERT INTO PAL_MLR_DATA_TBL VALUES (2,0.25,0.15,0.36); INSERT INTO PAL_MLR_DATA_TBL VALUES (3,0.35,0.16,0.35); INSERT INTO PAL_MLR_DATA_TBL VALUES (4,0.45,0.17,0.37); INSERT INTO PAL_MLR_DATA_TBL VALUES (5,0.55,0.18,0.38); INSERT INTO PAL_MLR_DATA_TBL VALUES (6,0.65,0.19,0.39); INSERT INTO PAL_MLR_DATA_TBL VALUES (7,0.75,0.19,0.31); INSERT INTO PAL_MLR_DATA_TBL VALUES (8,0.85,0.11,0.32); INSERT INTO PAL_MLR_DATA_TBL VALUES (9,0.95,0.12,0.33); DROP TABLE PAL_MLR_RESULTS_TBL; CREATE COLUMN TABLE PAL_MLR_RESULTS_TBL ("Coefficient" varchar(50),"CoefficientValue" DOUBLE); DROP TABLE PAL_MLR_FITTED_TBL; CREATE COLUMN TABLE PAL_MLR_FITTED_TBL ("ID" INT, "Fitted" DOUBLE); DROP TABLE PAL_MLR_SIGNIFICANCE_TBL; CREATE COLUMN TABLE PAL_MLR_SIGNIFICANCE_TBL ("NAME" varchar(50),"VALUE" DOUBLE); DROP TABLE PAL_MLR_PMMLMODEL_TBL; CREATE COLUMN TABLE PAL_MLR_PMMLMODEL_TBL ("ID" INT, "PMMLMODEL" VARCHAR(5000)); CALL DM_PAL.PAL_LR_PROC(PAL_MLR_DATA_TBL,"#PAL_CONTROL_TBL", PAL_MLR_RESULTS_TBL, PAL_MLR_FITTED_TBL, PAL_MLR_SIGNIFICANCE_TBL, PAL_MLR_PMMLMODEL_TBL) WITH OVERVIEW; SELECT * FROM PAL_MLR_RESULTS_TBL; SELECT * FROM PAL_MLR_FITTED_TBL; SELECT * FROM PAL_MLR_SIGNIFICANCE_TBL; Expected Results SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 271
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SAP HANA Platform SPS 12 Document V
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Naive Bayes. ......................
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1 What is PAL? SAP HANA’s SQLScri
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2 Getting Started with PAL This sec
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The result will tell you whether th
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● : The name of the schema where
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CREATE COLUMN TABLE PAL_ARIMAX_DATA
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For more information on how to use
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Category PAL Algorithm Built-in Fun
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Category PAL Algorithm Built-in Fun
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The procedure name is the same as s
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● USER1 has been assigned the AFL
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3.1.2 Agglomerate Hierarchical Clus
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Table 13: Name Data Type Default Va
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Name Data Type Default Value Descri
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INSERT INTO DATA_TBL VALUES ('17',
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Prerequisites ● ● The input dat
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INSERT INTO PAL_AD_DATA_TBL VALUES
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The signature table should contain
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CREATE TYPE PAL_KMEANS_CENTERS_T AS
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PAL_CLUSTER_ASSIGNED_TBL: For DBSCA
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For SOM: SET SCHEMA DM_PAL; DROP TY
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PAL_CLUSTER_ASSIGNED_TBL: Related I
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Table 24: Name Data Type Descriptio
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DROP TYPE PAL_CONTROL_T; CREATE TYP
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3.1.6 Gaussian Mixture Model (GMM)
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Table 29: Name Data Type Descriptio
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Output Tables Output format 0: Tabl
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INSERT INTO PAL_GMM_PDATA_TBL VALUE
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Example 2 SET SCHEMA DM_PAL; DROP T
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PAL_GMM_RESULTSMODEL_TBL: 3.1.7 K-M
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Table 43: Table Column Column Data
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INSERT INTO #PAL_CONTROL_TBL VALUES
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PAL_KMEANS_MODEL_TBL: VALIDATEKMEAN
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"V000" DOUBLE, "A0" INTEGER, "A1" I
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Expected Result PAL_SILHOUETTE_RESU
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Table Column Column Data Type Descr
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Example Assume that: ● ● ● DM
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PAL_KMEDIANS_ASSIGN_TBL: 3.1.9 K-Me
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Position Schema Name Table Type Nam
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The input, parameter, and output ta
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INSERT INTO PARAMETERS_TBL VALUES (
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DOCTOPICDIST_TBL: GENERALINFO_TBL:
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Procedure Calling CALL .(, , ) with
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Expected Result DOCTOPICDIST_TBL: 3
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The following parameters are option
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CREATE TYPE PAL_SOM_MODEL_T AS TABL
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PAL_SOM_RESASSIGN_TBL: PAL_SOM_MODE
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INSERT INTO PAL_SLIGHT_SIL_PDATA_TB
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AUC Procedure Generation CALL SYS.A
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Table 85: Name Data Type Default Va
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Example 2 SET SCHEMA DM_PAL; DROP T
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3.2.2 Back Propagation Neural Netwo
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CREATEBPNN This function trains a B
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INSERT INTO PAL_TRAIN_NN_DATA_TBL V
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PAL_TRAIN_NN_RESULT_TBL: PAL_REGRES
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Classification example: SET SCHEMA
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"NAME" VARCHAR(100), "MODEL" CLOB )
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CREATE TYPE PAL_C45_DATA_T AS TABLE
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CART This function is used for clas
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Prerequisites ● ● The target co
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PAL_CHAID_TREEMODEL_TBL: PAL_CHAID_
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INSERT INTO PAL_CM_DATA_TBL VALUES(
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PAL_KNN_RESULTS_TBL: 3.2.8 Logistic
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Procedure Calling CALL .(, , , , )
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PAL_LOGISTICR_STAT_TBL: PAL_LOGISTI
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Procedure Generation CALL SYS.AFLLA
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Assume that: ● ● ● DM_PAL is
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PAL_FLOGISTICR_FITTED_TBL: Related
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Expected Result PAL_LRMC_MODEL_TBL:
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Table 137: Name Data Type Default V
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SAP HANA Predictive Analysis Librar
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CALL SYS.AFLLANG_WRAPPER_PROCEDURE_
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3.2.10 Naive Bayes Naive Bayes is a
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● ● USER1 has been assigned the
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"DOUBLEARGS" DOUBLE, "STRINGARGS" V
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Position Schema Name Table Type Nam
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DROP TABLE CV_LR_FUNC_TBL; CREATE C
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CREATE COLUMN TABLE FUNC_TBL LIKE F
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DROP TABLE EVALUATION_RESULT_TBL; C
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INSERT INTO PAL_PARA_TBL VALUES('TR
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Page 219 and 220: 3.2.13 Random Forest The ransom for
Page 221 and 222: Table 161: Name Data Type Default V
Page 223 and 224: INSERT INTO PAL_RF_DATA_TBL VALUES
Page 225 and 226: Table Column Column Data Type Descr
Page 227 and 228: 3.2.14 Support Vector Machine Suppo
Page 229 and 230: Table 168: Name Data Type Descripti
Page 231 and 232: Name Data Type Default Value Descri
Page 233 and 234: CREATE TABLE PAL_SVM_PDATA_TBL("POS
Page 235 and 236: The signature table should contain
Page 237 and 238: STRINGARGS VARCHAR(100) ); DROP TYP
Page 239 and 240: Example 3: Support vector classific
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Page 245 and 246: INSERT INTO PAL_GR_PDATA_TBL values
Page 247 and 248: Procedure Calling CALL .(, , , ) wi
Page 249 and 250: 3.3.2 Bi-Variate Natural Logarithmi
Page 253 and 254: CREATE LOCAL TEMPORARY COLUMN TABLE
Page 257 and 258: Where β0…βn are parameters that
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Page 265 and 266: Assume we have m observation pairs
Page 269: Name Data Type Default Value Descri
Page 273 and 274: CREATE COLUMN TABLE PAL_ENET_MLR_PD
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Page 277 and 278: 3.3.5 Polynomial Regression Polynom
Page 281 and 282: insert into PAL_PR_PDATA_TBL values
Page 283 and 284: The input, parameter, and output ta
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Page 293 and 294: Optional Parameters The following p
Page 297 and 298: INSERT INTO PAL_APRIORI_DATA_TBL VA
Page 299 and 300: PAL_APRIORI_CONS_ITEMS_TBL: LITEAPR
Page 303 and 304: INSERT INTO PAL_LITEAPRIORI_DATA_TB
Page 305 and 306: Table 233: Table Column Column Data
Page 309 and 310: FPGROWTH (Relational Output) FP-Gro
Page 313 and 314: ); DROP TYPE PAL_FPGROWTH_POSTRULE_
Page 315 and 316: PAL_FPGROWTH_VALUES_TBL: 3.4.3 K-Op
Page 319 and 320: INSERT INTO PAL_KORD_DATA_TBL VALUE
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Prerequisite No missing or null dat
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. Initial P 0 can be calculated by
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Output Table (Model Table) Table 24
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DROP TYPE PAL_ARIMAX_DATA_T; CREATE
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CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
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We get And variance of the forecast
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CREATE COLUMN TABLE PAL_ARIMA_PDATA
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INSERT INTO PAL_ARIMAX_DATA_TBL VAL
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3.5.2 Auto ARIMA This function auto
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CREATE LOCAL TEMPORARY COLUMN TABLE
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Example 3: Auto Seasonal ARIMA SET
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INSERT INTO PAL_ARIMA_DATA_TBL VALU
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INSERT INTO PAL_ARIMA_DATA_TBL VALU
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F 1 = a 0 + b 0 2. Calculation: S t
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INSERT INTO PAL_BROWNSMOOTH_DATA_TB
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3.5.4 Croston's Method The Croston
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SELECT * FROM PAL_CROSTON_STATISTIC
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Table 271: Name Data Type Descripti
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PAL_FORECASTACCURACYMEASURES_RESULT
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PAL_OUTPARAMETER_TBL: PAL_FORECASTS
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PAL_OUTPARAMETER_TBL: SAP HANA Pred
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CREATE TYPE PAL_CONTROL_T AS TABLE
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PAL_FORECASTTRIPLESMOOTHING_RESULT_
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PAL_OUTPARAMETER_TBL: PAL_FORECASTM
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PAL_OUTPARAMETER_TBL: PAL_FORECASTM
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Procedure Calling CALL .(, , , ) wi
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PAL_FORECASTSLR_RESULT_TBL: PAL_FOR
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Note The algorithm is backward comp
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Note F 0 is not defined because the
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CREATE COLUMN TABLE PAL_DOUBLESMOOT
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3.5.10 Triple Exponential Smoothing
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PAL calculates the prediction inter
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Output Table Table 294: Table Colum
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PAL_TRIPLESMOOTH_RESULT_TBL: PAL_TR
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Once the trend and seasonality are
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CREATE COLUMN TABLE PAL_TSSEASONALI
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The difference-sign test counts the
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Optional Parameters The following p
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PAL_TSTREND_TREND_TBL: PAL_TSTREND_
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Parameter Table Mandatory Parameter
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3.6 Preprocessing Algorithms The re
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● ● Smoothing by bin medians: e
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3.6.2 Binning Assignment Binning as
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3.6.3 Convert Category Type to Bina
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"NAME" VARCHAR(100), "INTARGS" INTE
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Procedure Generation CALL SYS.AFLLA
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INSERT INTO PAL_IQR_TESTDT_TBL VALU
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PARTITION This function reads the i
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3.6.6 Posterior Scaling Posterior s
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Expected Result PAL_NEW_SCALING_TBL
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INSERT INTO PAL_PCA_DATA_TBL VALUES
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INSERT INTO PAL_PCAPROJ_PDATA_TBL V
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INSERT INTO PAL_DISTRRANDOM_PDATA_T
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INSERT INTO PAL_SAMPLING_DATA_TBL V
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If method is 4 and SAMPLING_SIZE is
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DROP TABLE PAL_SAMPLING_PDATA_TBL;
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Expected Result 3.6.10 Scaling Rang
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Prerequisite Each column must conta
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PAL_MISSING_VALUES_RESULT_TBL: PAL_
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INSERT INTO PAL_VT_DATA_TBL VALUES
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3.7.2 Chi-Squared Test for Independ
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DISTRPROB This function calculates
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"VALUE" DOUBLE ); DROP TABLE PDATA_
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"DOUBLEARGS" DOUBLE, "STRINGARGS" V
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"TYPE_NAME" NVARCHAR(256), "PARAMET
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2. Calculate PAL also supports the
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The Kaplan-Meier estimator can be r
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PAL_KMSURV_LOGRANK_STAT2_TBL: 3.7.7
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CREATE TYPE PAL_DISTRQUANTILE_RESUL
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where x' = x - Table 404: Definitio
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Let the sample mean of x and y be:
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CREATE TYPE PAL_VAREQUALTEST_DATA1_
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The quantity is computed as a weigh
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Prerequisites ● ● Input data ca
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"PARAMETER_TYPE" VARCHAR(7) ); INSE
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Signature Input Tables Table 425: T
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CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
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"DISTANCE" DOUBLE ); DROP TYPE PAL_
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"STRINGARGS" VARCHAR(100) ); --crea
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The projected estimates are single
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INSERT INTO PDATA VALUES (2, 'DM_PA
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----product revenue---- DELETE FROM
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---- calculate net present value of
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UPDATE PAL_DISTRPROB_DISTRPARAM_TBL
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Then we get the survival estimates
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INSERT INTO PAL_TRIAL_DATA_TBL VALU
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INSERT INTO PAL_DISTRFITCENSORED_DA
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The expected results are as follows
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); INSERT INTO PAL_DISTRPROB_PDATA_
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Important Disclaimers and Legal Inf
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