SAP HANA Predictive Analysis Library (PAL)

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INSERT INTO PAL_UNIVARSTAT_PDATA_TBL VALUES (1,'DM_PAL', 'PAL_UNIVARSTAT_DATA_T','IN'); INSERT INTO PAL_UNIVARSTAT_PDATA_TBL VALUES (2,'DM_PAL', 'PAL_CONTROL_T','IN'); INSERT INTO PAL_UNIVARSTAT_PDATA_TBL VALUES (3,'DM_PAL', 'PAL_UNIVARSTAT_RESULT_T','OUT'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('DM_PAL', 'PAL_UNIVARIATESTAT_PROC'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL', 'UNIVARSTAT', 'DM_PAL', 'PAL_UNIVARIATESTAT_PROC', PAL_UNIVARSTAT_PDATA_TBL); DROP TABLE PAL_UNIVARSTAT_DATA_TBL; CREATE COLUMN TABLE PAL_UNIVARSTAT_DATA_TBL LIKE PAL_UNIVARSTAT_DATA_T; INSERT INTO PAL_UNIVARSTAT_DATA_TBL VALUES (1,2.4); INSERT INTO PAL_UNIVARSTAT_DATA_TBL VALUES (5,3.5); INSERT INTO PAL_UNIVARSTAT_DATA_TBL VALUES (3,8.9); INSERT INTO PAL_UNIVARSTAT_DATA_TBL VALUES (10,-1.4); INSERT INTO PAL_UNIVARSTAT_DATA_TBL VALUES (-4,-3.5); INSERT INTO PAL_UNIVARSTAT_DATA_TBL VALUES (11,32.8); DROP TABLE #PAL_CONTROL_TBL; CREATE LOCAL TEMPORARY COLUMN TABLE #PAL_CONTROL_TBL( "NAME" VARCHAR (50), "INTARGS" INTEGER, "DOUBLEARGS" DOUBLE, "STRINGARGS" VARCHAR (100) ); INSERT INTO #PAL_CONTROL_TBL VALUES ('SKEWNESS_TYPE',2,null,null); --default value is 1, it can be {0,1,2} INSERT INTO #PAL_CONTROL_TBL VALUES ('KURTOSIS_TYPE',2,null,null); --default value is 1, it can be {0,1,2} INSERT INTO #PAL_CONTROL_TBL VALUES ('DATASET_TYPE',0,null,null); --default value is 0, it can be {0,1} DROP TABLE PAL_UNIVARSTAT_RESULT_TBL; CREATE COLUMN TABLE PAL_UNIVARSTAT_RESULT_TBL LIKE PAL_UNIVARSTAT_RESULT_T; CALL DM_PAL.PAL_UNIVARIATESTAT_PROC(PAL_UNIVARSTAT_DATA_TBL, #PAL_CONTROL_TBL, PAL_UNIVARSTAT_RESULT_TBL) WITH OVERVIEW; SELECT * FROM PAL_UNIVARSTAT_RESULT_TBL; Expected Result 3.7.10 Variance Equal Test This function is used to test the equality of two random variances using F-test. The null hypothesis is that two independent normal variances are equal. The observed sums of some selected squares are then examined to see whether their ratio is significantly incompatible with this null hypothesis. Let x 1 , x 2 , ..., x n and y 1 , y 2 , ..., y n be independent and identically distributed samples from two populations. 536 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
Let the sample mean of x and y be: Therefore, the sample variance of x and y are: Then the test statistics is: The F value will be used to calculate a p-value by comparing the value of F to an F-distribution. The degree of freedom is set to (n-1) and (m-1). Prerequisites ● ● The input data has two tables and each table has only one column with the type of integer or double. The input data does not contain null value. The algorithm will issue errors when encountering null values. VAREQUALTEST This function tests the equality of variances between two input data. Procedure Generation CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘VAREQUALTEST’, ‘’, '', ); The signature table should contain the following records: Table 410: Position Schema Name Table Type Name Parameter Type 1 2 IN IN 3 IN 4 OUT SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 537
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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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3.2.13 Random Forest The ransom for
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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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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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Page 485 and 486: Prerequisite Each column must conta
Page 487 and 488: Assume that: ● ● ● DM_PAL is
Page 489 and 490: PAL_MISSING_VALUES_RESULT_TBL: PAL_
Page 491 and 492: The procedure name is the same as s
Page 493 and 494: INSERT INTO PAL_VT_DATA_TBL VALUES
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Page 497 and 498: 3.7.2 Chi-Squared Test for Independ
Page 499 and 500: ● USER1 has been assigned the AFL
Page 501 and 502: DISTRPROB This function calculates
Page 503 and 504: ● USER1 has been assigned the AFL
Page 505 and 506: The signature table should contain
Page 507 and 508: "VALUE" DOUBLE ); DROP TABLE PDATA_
Page 509 and 510: Table Column Column Data Type Descr
Page 511 and 512: "DOUBLEARGS" DOUBLE, "STRINGARGS" V
Page 513 and 514: "TYPE_NAME" NVARCHAR(256), "PARAMET
Page 515 and 516: 2. Calculate PAL also supports the
Page 517 and 518: Assume that: ● ● ● DM_PAL is
Page 519 and 520: The Kaplan-Meier estimator can be r
Page 525 and 526: PAL_KMSURV_LOGRANK_STAT2_TBL: 3.7.7
Page 527 and 528: Table 398: Table Column Column Data
Page 531 and 532: CREATE TYPE PAL_DISTRQUANTILE_RESUL
Page 533 and 534: where x' = x - Table 404: Definitio
Page 535: Table 408: Name Data Type Default V
Page 539 and 540: CREATE TYPE PAL_VAREQUALTEST_DATA1_
Page 541 and 542: The quantity is computed as a weigh
Page 545 and 546: Prerequisites ● ● Input data ca
Page 547 and 548: "PARAMETER_TYPE" VARCHAR(7) ); INSE
Page 549 and 550: Signature Input Tables Table 425: T
Page 551 and 552: CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
Page 553 and 554: "DISTANCE" DOUBLE ); DROP TYPE PAL_
Page 555 and 556: "STRINGARGS" VARCHAR(100) ); --crea
Page 557 and 558: The projected estimates are single
Page 559 and 560: INSERT INTO PDATA VALUES (2, 'DM_PA
Page 561 and 562: ----product revenue---- DELETE FROM
Page 563 and 564: ---- calculate net present value of
Page 565 and 566: UPDATE PAL_DISTRPROB_DISTRPARAM_TBL
Page 567 and 568: Then we get the survival estimates
Page 569 and 570: INSERT INTO PAL_TRIAL_DATA_TBL VALU
Page 571 and 572: INSERT INTO PAL_DISTRFITCENSORED_DA
Page 573 and 574: The expected results are as follows
Page 575 and 576: ); INSERT INTO PAL_DISTRPROB_PDATA_
Page 577 and 578: Important Disclaimers and Legal Inf
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SAP HANA Predictive Analysis Library (PAL)

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