SAP HANA Predictive Analysis Library (PAL)

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DROP TYPE PAL_SVM_MODEL_T; CREATE TYPE PAL_SVM_MODEL_T AS TABLE( ID VARCHAR(50), MODEL VARCHAR(5000)); DROP TABLE PAL_SVM_PDATA_TBL; CREATE TABLE PAL_SVM_PDATA_TBL("POSITION" INT, "SCHEMA_NAME" NVARCHAR(256), "TYPE_NAME" NVARCHAR(256), "PARAMETER_TYPE" VARCHAR(7)); INSERT INTO PAL_SVM_PDATA_TBL VALUES (1,'SYSTEM','PAL_SVM_TRAINING_T','IN'); INSERT INTO PAL_SVM_PDATA_TBL VALUES (2,'SYSTEM','PAL_CONTROL_T','IN'); INSERT INTO PAL_SVM_PDATA_TBL VALUES (3,'SYSTEM','PAL_SVM_MODEL_T','OUT'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('SYSTEM','PAL_SVM_TRAIN'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL','SVMTRAIN','SYSTEM','PAL_SVM_TRAIN' ,PAL_SVM_PDATA_TBL); DROP TABLE PAL_SVM_TRAINING_TBL; CREATE COLUMN TABLE PAL_SVM_TRAINING_TBL LIKE PAL_SVM_TRAINING_T; DROP TABLE #PAL_CONTROL_TBL; CREATE LOCAL TEMPORARY COLUMN TABLE #PAL_CONTROL_TBL LIKE PAL_CONTROL_T; DROP TABLE PAL_SVM_MODEL_TBL_EX3; CREATE COLUMN TABLE PAL_SVM_MODEL_TBL_EX3 LIKE PAL_SVM_MODEL_T; INSERT INTO PAL_SVM_TRAINING_TBL VALUES(0,1,10,100,'A',1); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(1,1.1,10.1,100,'A',1); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(2,1.2,10.2,100,'A',1); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(3,1.3,10.4,100,'A',1); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(4,1.2,10.3,100,'AB',1); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(5,4,40,400,'AB',1); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(6,4.1,40.1,400,'AB',1); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(7,4.2,40.2,400,'AB',1); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(8,4.3,40.4,400,'AB',2); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(9,4.2,40.3,400,'AB',2); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(10,9,90,900,'B',2); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(11,9.1,90.1,900,'A',2); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(12,9.2,90.2,900,'B',2); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(13,9.3,90.4,900,'A',2); INSERT INTO PAL_SVM_TRAINING_TBL VALUES(14,9.2,90.3,900,'A',2); INSERT INTO #PAL_CONTROL_TBL VALUES('THREAD_NUMBER',8,null,null); INSERT INTO #PAL_CONTROL_TBL VALUES('KERNEL_TYPE',2,null,null); INSERT INTO #PAL_CONTROL_TBL VALUES('TYPE',1,null,null); INSERT INTO #PAL_CONTROL_TBL VALUES('PROBABILITY',1,null,null); CALL SYSTEM.PAL_SVM_TRAIN(PAL_SVM_TRAINING_TBL,#PAL_CONTROL_TBL,PAL_SVM_MODEL_TBL_EX3) WITH OVERVIEW; SELECT * FROM PAL_SVM_MODEL_TBL_EX3; Expected Result SVMPREDICT This function uses the training model generated by SVMTRAIN to make predictive analysis. Procedure Generation CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘SVMPREDICT’, ‘’, '', ); 234 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
The signature table should contain the following records: Table 171: Position Schema Name Table Type Name Parameter Type 1 IN 2 IN 3 IN 4 OUT Procedure Calling CALL .(, , , ) with overview; The predicting procedure name is the same as specified in the procedure generation. The tables must of the types specified in the signature table. Signature Input Table Table 172: Table Column Column Data Type Description Constraint Data 1st column Integer, bigint, varchar, or nvarchar ID This must be the first column. Other columns Integer, double, var Attributes data The varchar or char, or nvarchar nvarchar data type is available only when the input model table is not null. Last column Varchar or nvarchar Query ID Only needed for SVM ranking. Parameter Table Mandatory Parameters None. Optional Parameter The following parameter is optional. If it is not specified, PAL will use its default value. SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 235
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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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Page 185 and 186: Expected Result PAL_LRMC_MODEL_TBL:
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Page 191 and 192: CALL SYS.AFLLANG_WRAPPER_PROCEDURE_
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Page 195 and 196: Table Column Column Data Type Descr
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Page 203 and 204: Position Schema Name Table Type Nam
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Page 209 and 210: DROP TABLE CV_LR_FUNC_TBL; CREATE C
Page 211 and 212: CREATE COLUMN TABLE FUNC_TBL LIKE F
Page 213 and 214: DROP TABLE EVALUATION_RESULT_TBL; C
Page 215 and 216: INSERT INTO PAL_PARA_TBL VALUES('TR
Page 219 and 220: 3.2.13 Random Forest The ransom for
Page 223 and 224: INSERT INTO PAL_RF_DATA_TBL VALUES
Page 227 and 228: 3.2.14 Support Vector Machine Suppo
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Page 233: CREATE TABLE PAL_SVM_PDATA_TBL("POS
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 271 and 272: Example 1: Fitting multiple linear
Page 273 and 274: CREATE COLUMN TABLE PAL_ENET_MLR_PD
Page 277 and 278: 3.3.5 Polynomial Regression Polynom
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3.4 Association Algorithms This sec
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Table 220: Name Data Type Default V
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"STRINGARGS" VARCHAR (100) ); INSER
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Optional Parameters The following p
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INSERT INTO PAL_APRIORI_DATA_TBL VA
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PAL_APRIORI_CONS_ITEMS_TBL: LITEAPR
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INSERT INTO PAL_LITEAPRIORI_DATA_TB
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FPGROWTH (Relational Output) FP-Gro
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); DROP TYPE PAL_FPGROWTH_POSTRULE_
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PAL_FPGROWTH_VALUES_TBL: 3.4.3 K-Op
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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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SAP HANA Predictive Analysis Library (PAL)

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