SAP HANA Predictive Analysis Library (PAL)

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call SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL','PARTITION','DM_PAL','PAL_PARTITION _PROC',PAL_PARTITION_PDATA_TBL); DROP TABLE PAL_PARTITION_DATA_TBL; CREATE COLUMN TABLE PAL_PARTITION_DATA_TBL LIKE PAL_PARTITION_DATA_T; INSERT INTO PAL_PARTITION_DATA_TBL VALUES (0, 'YES','Single',125,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (1, 'NO','Married',100,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (2, 'NO','Single',70,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (3, 'YES','Married',120,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (4, 'NO','Divorced',95,'YES'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (5, 'NO','Married',60,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (6, 'YES','Divorced',220,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (7, 'NO','Single',85,'YES'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (8, 'NO','Married',75,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (9, 'NO','Single',90,'YES'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (10, 'YES','Single',125,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (11, 'NO','Married',100,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (12, 'NO','Single',70,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (13, 'YES','Married',120,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (14, 'NO','Divorced',95,'YES'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (15, 'NO','Married',60,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (16, 'YES','Divorced',220,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (17, 'NO','Single',85,'YES'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (18, 'NO','Married',75,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (19, 'NO','Single',90,'YES'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (20, 'YES','Single',125,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (21, 'NO','Married',100,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (22, 'NO','Single',70,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (23, 'YES','Married',120,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (24, 'NO','Divorced',95,'YES'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (25, 'NO','Married',60,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (26, 'YES','Divorced',220,'NO'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (27, 'NO','Single',85,'YES'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (28, 'NO','Married',75,'YES'); INSERT INTO PAL_PARTITION_DATA_TBL VALUES (29, 'NO','Single',90,'YES'); DROP TABLE #PAL_CONTROL_TBL; CREATE LOCAL TEMPORARY TABLE #PAL_CONTROL_TBL LIKE PAL_CONTROL_T; INSERT INTO #PAL_CONTROL_TBL VALUES ('THREAD_NUMBER',1,null,null); INSERT INTO #PAL_CONTROL_TBL VALUES ('PARTITION_METHOD',0,null,null); INSERT INTO #PAL_CONTROL_TBL VALUES ('RANDOM_SEED',23,null,null); INSERT INTO #PAL_CONTROL_TBL VALUES ('TRAINING_PERCENT', null,0.6,null); INSERT INTO #PAL_CONTROL_TBL VALUES ('TESTING_PERCENT', null,0.2,null); INSERT INTO #PAL_CONTROL_TBL VALUES ('VALIDATION_PERCENT', null,0.2,null); DROP TABLE PAL_PARTITION_OUT_TBL; CREATE COLUMN TABLE PAL_PARTITION_OUT_TBL LIKE PAL_PARTITION_OUT_T; CALL DM_PAL.PAL_PARTITION_PROC(PAL_PARTITION_DATA_TBL, "#PAL_CONTROL_TBL", PAL_PARTITION_OUT_TBL) with overview; select * from PAL_PARTITION_OUT_TBL; Expected Result 452 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
3.6.6 Posterior Scaling Posterior scaling is used to scale data based on the previous scaling model generated by the scaling range procedure. It is assumed that new data is from similar distribution and will not update the scaling model. Prerequisites ● No missing or null data in the inputs. SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 453
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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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The following parameters are option
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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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Page 405 and 406: CREATE COLUMN TABLE PAL_DOUBLESMOOT
Page 407 and 408: 3.5.10 Triple Exponential Smoothing
Page 409 and 410: PAL calculates the prediction inter
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Page 415 and 416: PAL_TRIPLESMOOTH_RESULT_TBL: PAL_TR
Page 417 and 418: Once the trend and seasonality are
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Page 421 and 422: CREATE COLUMN TABLE PAL_TSSEASONALI
Page 423 and 424: The difference-sign test counts the
Page 425 and 426: Optional Parameters The following p
Page 427 and 428: PAL_TSTREND_TREND_TBL: PAL_TSTREND_
Page 429 and 430: Parameter Table Mandatory Parameter
Page 431 and 432: 3.6 Preprocessing Algorithms The re
Page 433 and 434: ● ● Smoothing by bin medians: e
Page 437 and 438: 3.6.2 Binning Assignment Binning as
Page 439 and 440: Table Column Column Data Type Descr
Page 441 and 442: 3.6.3 Convert Category Type to Bina
Page 443 and 444: "NAME" VARCHAR(100), "INTARGS" INTE
Page 445 and 446: Procedure Generation CALL SYS.AFLLA
Page 447 and 448: INSERT INTO PAL_IQR_TESTDT_TBL VALU
Page 449 and 450: PARTITION This function reads the i
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Page 455 and 456: Table Column Column Data Type Descr
Page 457 and 458: Expected Result PAL_NEW_SCALING_TBL
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Page 461 and 462: INSERT INTO PAL_PCA_DATA_TBL VALUES
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Page 465 and 466: INSERT INTO PAL_PCAPROJ_PDATA_TBL V
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Page 469 and 470: INSERT INTO PAL_DISTRRANDOM_PDATA_T
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Page 473 and 474: INSERT INTO PAL_SAMPLING_DATA_TBL V
Page 475 and 476: If method is 4 and SAMPLING_SIZE is
Page 477 and 478: DROP TABLE PAL_SAMPLING_PDATA_TBL;
Page 479 and 480: 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_
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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
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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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