SAP HANA Predictive Analysis Library (PAL)

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Name Data Type Default Value Description Dependency SELECTED_FEA TURES Only used when you need to indicate the needed features. Varchar No default value A string to specify the features that will be processed. The pattern is “X 1 ,…,X n ”, where X i is the corresponding column name in the data table. If this parameter is not specified, all the features will be processed. DEPENDENT_VARIA BLE Varchar No default value Column name in the data table used as dependent variable. Only used when you need to indicate the dependence. Output Table Table 123: Table Column Column Data Type Description Result 1st column Integer, varchar, or nvarchar ID Note: If the SELECTED_FEA TURES parameter is specified, the column data type must be varchar or nvarchar. 2nd column Integer or double Value A i ● ● ● ● A 0 : intercept A 1 : beta coefficient for X 1 A 2 : beta coefficient for X 2 … 3rd column Double (When STAT_INF = 1) Zscore of each coefficient parameter 4th column Double (When STAT_INF = 1) Pvalue of each coefficient parameter Statistics (optional) 1st column Varchar or nvarchar Only supports AIC 170 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
Table Column Column Data Type Description 2nd column Double AIC value PMML Result (logistic regression model) 1st column Integer ID 2nd column CLOB, varchar, or nvarchar Logistic 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. Example 1: Fitting logistic regression model without penalties SET SCHEMA DM_PAL; DROP TYPE PAL_LOGISTICR_DATA_T; CREATE TYPE PAL_LOGISTICR_DATA_T AS TABLE("V1" VARCHAR (50),"V2" DOUBLE,"V3" INTEGER,"CATEGORY" INTEGER); DROP TYPE PAL_LOGISTICR_RESULT_T; CREATE TYPE PAL_LOGISTICR_RESULT_T AS TABLE("Coefficient" varchar(50),"CoefficientValue" DOUBLE,"ZSCORE" DOUBLE, "Pr(>|Z|)" DOUBLE); DROP TYPE PAL_CONTROL_T; CREATE TYPE PAL_CONTROL_T AS TABLE("NAME" VARCHAR(50), "INTARGS" INTEGER, "DOUBLEARGS" DOUBLE, "STRINGARGS" VARCHAR(100)); DROP TYPE PAL_LOGISTICR_STAT_T; CREATE TYPE PAL_LOGISTICR_STAT_T AS TABLE( "STATISTICS" VARCHAR(20), "VALUE" DOUBLE); DROP TYPE PAL_LOGISTICR_PMMLMODEL_T; CREATE TYPE PAL_LOGISTICR_PMMLMODEL_T AS TABLE( "ID" INTEGER, "PMMLMODEL" VARCHAR(5000)); DROP table PAL_LOGISTICR_PDATA_TBL; CREATE column table PAL_LOGISTICR_PDATA_TBL("POSITION" INT, "SCHEMA_NAME" NVARCHAR(256), "TYPE_NAME" NVARCHAR(256), "PARAMETER_TYPE" VARCHAR(7)); INSERT INTO PAL_LOGISTICR_PDATA_TBL VALUES (1,'DM_PAL', 'PAL_LOGISTICR_DATA_T','IN'); INSERT INTO PAL_LOGISTICR_PDATA_TBL VALUES (2,'DM_PAL', 'PAL_CONTROL_T','IN'); INSERT INTO PAL_LOGISTICR_PDATA_TBL VALUES (3,'DM_PAL', 'PAL_LOGISTICR_RESULT_T','OUT'); INSERT INTO PAL_LOGISTICR_PDATA_TBL VALUES (4,'DM_PAL', 'PAL_LOGISTICR_STAT_T','OUT'); INSERT INTO PAL_LOGISTICR_PDATA_TBL VALUES (5,'DM_PAL', 'PAL_LOGISTICR_PMMLMODEL_T','OUT'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('DM_PAL', 'PAL_LOGISTICR_PROC'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL','LOGISTICREGRESSION', 'DM_PAL', 'PAL_LOGISTICR_PROC',PAL_LOGISTICR_PDATA_TBL); DROP TABLE PAL_LOGISTICR_DATA_TBL; CREATE COLUMN TABLE PAL_LOGISTICR_DATA_TBL LIKE PAL_LOGISTICR_DATA_T; INSERT INTO PAL_LOGISTICR_DATA_TBL VALUES ('B',2.62,0,1); INSERT INTO PAL_LOGISTICR_DATA_TBL VALUES ('B',2.875,0,1); INSERT INTO PAL_LOGISTICR_DATA_TBL VALUES ('A',2.32,1,1); INSERT INTO PAL_LOGISTICR_DATA_TBL VALUES ('A',3.215,2,0); INSERT INTO PAL_LOGISTICR_DATA_TBL VALUES ('B',3.44,3,0); INSERT INTO PAL_LOGISTICR_DATA_TBL VALUES ('B',3.46,0,0); INSERT INTO PAL_LOGISTICR_DATA_TBL VALUES ('A',3.57,1,0); INSERT INTO PAL_LOGISTICR_DATA_TBL VALUES ('B',3.19,2,0); INSERT INTO PAL_LOGISTICR_DATA_TBL VALUES ('A',3.15,3,0); SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 171
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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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Page 121 and 122: Table 85: Name Data Type Default Va
Page 123 and 124: Example 2 SET SCHEMA DM_PAL; DROP T
Page 125 and 126: 3.2.2 Back Propagation Neural Netwo
Page 127 and 128: CREATEBPNN This function trains a B
Page 129 and 130: Name Data Type Default Value Descri
Page 131 and 132: INSERT INTO PAL_TRAIN_NN_DATA_TBL V
Page 133 and 134: PAL_TRAIN_NN_RESULT_TBL: PAL_REGRES
Page 135 and 136: Classification example: SET SCHEMA
Page 137 and 138: "NAME" VARCHAR(100), "MODEL" CLOB )
Page 139 and 140: The signature table should contain
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 .(, , , , )
Page 169: Name Data Type Default Value Descri
Page 173 and 174: PAL_LOGISTICR_STAT_TBL: PAL_LOGISTI
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 181 and 182: The signature table should contain
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 and 190: SAP HANA Predictive Analysis Librar
Page 191 and 192: CALL SYS.AFLLANG_WRAPPER_PROCEDURE_
Page 193 and 194: 3.2.10 Naive Bayes Naive Bayes is a
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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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Table 161: Name Data Type Default V
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INSERT INTO PAL_RF_DATA_TBL VALUES
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3.2.14 Support Vector Machine Suppo
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Table 168: Name Data Type Descripti
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CREATE TABLE PAL_SVM_PDATA_TBL("POS
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STRINGARGS VARCHAR(100) ); DROP TYP
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Example 3: Support vector classific
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Related Information SAP HANA Smart
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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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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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