SAP HANA Predictive Analysis Library (PAL)

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Prediction with PMML input: SET SCHEMA DM_PAL; DROP TYPE PAL_LRMCTE_DATA_T; CREATE TYPE PAL_LRMCTE_DATA_T AS TABLE ( "ID" INTEGER, "V1" VARCHAR (50), "V2" DOUBLE, "V3" INTEGER ); DROP TYPE PAL_LRMCTE_RESULT_T; CREATE TYPE PAL_LRMCTE_RESULT_T AS TABLE ( "ID" INTEGER, "Y" varchar(100), "Lik" DOUBLE ); DROP table PAL_LRMCTEPMML_INIT_TBL; CREATE column table PAL_LRMCTEPMML_INIT_TBL ("POSITION" INT, "SCHEMA_NAME" NVARCHAR(256), "TYPE_NAME" NVARCHAR(256), "PARAMETER_TYPE" VARCHAR(7)); insert into PAL_LRMCTEPMML_INIT_TBL values (1,'DM_PAL', 'PAL_LRMCTE_DATA_T','IN'); insert into PAL_LRMCTEPMML_INIT_TBL values (2,'DM_PAL', 'PAL_CONTROL_T','IN'); insert into PAL_LRMCTEPMML_INIT_TBL values (3,'DM_PAL', 'PAL_LRMC_PMML_T','IN'); insert into PAL_LRMCTEPMML_INIT_TBL values (4,'DM_PAL', 'PAL_LRMCTE_RESULT_T','OUT'); DROP TABLE PAL_CONTROL_TBL; CREATE COLUMN TABLE PAL_CONTROL_TBL like PAL_CONTROL_T; INSERT INTO PAL_CONTROL_TBL VALUES ('MODEL_TYPE', 2, null, null); INSERT INTO PAL_CONTROL_TBL VALUES ('VERBOSE_OUTPUT', 0, null, null); DROP TABLE PAL_LRMCTE_DATA_TBL; CREATE COLUMN TABLE PAL_LRMCTE_DATA_TBL like PAL_LRMCTE_DATA_T; INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (0,'B',2.62,0); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (1,'B',2.875,0); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (2,'A',2.32,1); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (3,'A',3.215,2); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (4,'B',3.44,3); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (5,'B',3.46,0); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (6,'A',3.57,1); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (7,'B',3.19,2); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (8,'A',3.15,3); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (9,'B',3.44,0); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (10,'B',3.44,1); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (11,'A',4.07,3); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (12,'A',3.73,1); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (13,'B',3.78,2); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (14,'B',5.25,2); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (15,'A',5.424,3); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (16,'A',5.345,0); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (17,'B',2.2,1); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (18,'B',1.615,2); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (19,'A',1.835,0); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (20,'B',2.465,3); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (21,'A',3.52,1); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (22,'A',3.435,0); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (23,'B',3.84,2); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (24,'B',3.845,3); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (25,'A',1.935,1); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (26,'B',2.14,0); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (27,'B',1.513,1); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (28,'A',3.17,3); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (29,'B',2.77,0); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (30,'B',3.57,0); INSERT INTO PAL_LRMCTE_DATA_TBL VALUES (31,'A',2.78,3); 190 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('DM_PAL', 'PAL_LRMCTEPMML_PROC'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL', 'LRMCTE', 'DM_PAL', 'PAL_LRMCTEPMML_PROC', PAL_LRMCTEPMML_INIT_TBL); delete from PAL_LRMCTE_RESULT_TBL; CALL DM_PAL.PAL_LRMCTEPMML_PROC(PAL_LRMCTE_DATA_TBL, PAL_CONTROL_TBL, PAL_LRMC_PMML_TBL, PAL_LRMCTE_RESULT_TBL) with overview; select * from PAL_LRMCTE_RESULT_TBL; Expected Result SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 191
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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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Page 143 and 144: CREATE TYPE PAL_C45_DATA_T AS TABLE
Page 145 and 146: CART This function is used for clas
Page 149 and 150: Table Column Column Data Type Descr
Page 151 and 152: Prerequisites ● ● The target co
Page 157 and 158: PAL_CHAID_TREEMODEL_TBL: PAL_CHAID_
Page 161 and 162: INSERT INTO PAL_CM_DATA_TBL VALUES(
Page 163 and 164: The following parameters are option
Page 165 and 166: PAL_KNN_RESULTS_TBL: 3.2.8 Logistic
Page 167 and 168: Procedure Calling CALL .(, , , , )
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Page 175 and 176: Procedure Generation CALL SYS.AFLLA
Page 177 and 178: Assume that: ● ● ● DM_PAL is
Page 179 and 180: PAL_FLOGISTICR_FITTED_TBL: Related
Page 183 and 184: Table 132: Table Column Column Data
Page 185 and 186: Expected Result PAL_LRMC_MODEL_TBL:
Page 187 and 188: Table 137: Name Data Type Default V
Page 189: SAP HANA Predictive Analysis Librar
Page 193 and 194: 3.2.10 Naive Bayes Naive Bayes is a
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Page 199 and 200: Table 144: Table Column Column Data
Page 201 and 202: "DOUBLEARGS" DOUBLE, "STRINGARGS" V
Page 203 and 204: Position Schema Name Table Type Nam
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
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Page 223 and 224: INSERT INTO PAL_RF_DATA_TBL VALUES
Page 227 and 228: 3.2.14 Support Vector Machine Suppo
Page 229 and 230: Table 168: Name Data Type Descripti
Page 233 and 234: 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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Related Information SAP HANA Smart
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Table 178: Name Data Type Default V
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INSERT INTO PAL_GR_PDATA_TBL values
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Procedure Calling CALL .(, , , ) wi
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3.3.2 Bi-Variate Natural Logarithmi
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CREATE LOCAL TEMPORARY COLUMN TABLE
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Where β0…βn are parameters that
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Assume we have m observation pairs
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Example 1: Fitting multiple linear
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CREATE COLUMN TABLE PAL_ENET_MLR_PD
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3.3.5 Polynomial Regression Polynom
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insert into PAL_PR_PDATA_TBL values
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The input, parameter, and output ta
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3.4 Association Algorithms This sec
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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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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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The following parameters are option
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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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Assume that: ● ● ● DM_PAL is
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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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Assume that: ● ● ● DM_PAL is
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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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