SAP HANA Predictive Analysis Library (PAL)

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Table Column Column Data Type Description 2nd column Varchar or nvarchar Function name. The function name table specifies a single wrapped classification function to use. Supported function names are: ● ● ● ● LOGISTICREGRESSION: Logistic Regression NBCTRAIN: Naive Bayes SVMTRAIN: Support Vector Machine RANDOMFOREST: Random Forest Data 1st column Integer, varchar, or nvarchar ID. Every data must have a unique ID specified in the 1st column. Thus for wrapped function that does not require an ID column, this column can be added. Other columns Function specific Function specific columns must be exactly the same as the format defined by the functions. Parameter Table Parameters supported by wrapped functions are also supported. Besides, there are some additional general parameters and parameters specific for wrapped functions. General Parameters Table 149: Name Data Type Default Value Description PARAM_SELECTION Integer 0 Determines whether automatic parameter selection is enabled. ● ● 0: Disabled 1: Enabled 204 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
Name Data Type Default Value Description PARAM_SELECTION_MEAS URE String ‘ACCURACY’ Indicates the criteria to select the optimal parameters. ● ● ‘ACCURACY’: Accuracy is used as measure ‘F1_SCORE’: The F1 score is used as measure Note: The ‘F1_SCORE’ option is not available for random forest. NR_FOLD Integer 10 (for LOGISTICREGRES SION) 3 (for SVMTRAIN) No default value for RAN DOMFOREST Specifies how many portions the training data will be divided into in cross validation. The value must be equal to or greater than 2. This is an optional parameter in LOGISTICREGRESSION or SVMTRAIN, but a mandatory parameter in NBCTRAIN. The parameter is not used in RANDOMFOREST. Note: SVMTRAIN already supports this parameter, so you do not need to specify this twice. EVALUATION_SEED Integer 0 Indicates the seed used to initialize the random number generator. ● ● 0: Uses the system time Not 0: Uses the specified seed Additional Parameters for LOGISTICREGRESSION Table 150: Name Data Type Description MIN_LAMBDA Double (Mandatory) Lower bound of the searching range for ENET_LAMBDA. This is a mandatory parameter. The value must be equal to or greater than 0. SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 205
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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
Page 153 and 154: Name Data Type Default Value Descri
Page 157 and 158: PAL_CHAID_TREEMODEL_TBL: PAL_CHAID_
Page 159 and 160: Table Column Column Data Type Descr
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 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
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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_
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: 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 233 and 234: CREATE TABLE PAL_SVM_PDATA_TBL("POS
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Page 239 and 240: Example 3: Support vector classific
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Page 245 and 246: INSERT INTO PAL_GR_PDATA_TBL values
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Page 249 and 250: 3.3.2 Bi-Variate Natural Logarithmi
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Where β0…βn are parameters that
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CREATE LOCAL TEMPORARY COLUMN TABLE
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Assume we have m observation pairs
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Table 202: Name Data Type Default V
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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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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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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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