SAP HANA Predictive Analysis Library (PAL)

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Table 237: Table Column Column Data Type Description Dataset / Historical Data 1st column Integer, varchar, or nvarchar Transaction ID 2nd column Integer, varchar, or nvarchar Item ID Parameter Table Mandatory Parameters None. Optional Parameters The following parameters are optional. If a parameter is not specified, PAL will use its default value. Table 238: Name Data Type Default Value Description MIN_SUPPORT Double 0 Minimum support. Value range: between 0 and 1 MIN_CONFIDENCE Double 0 Minimum confidence. Value range: between 0 and 1 MIN_LIFT Double 0 Minimum lift. MAXITEMLENGTH Integer 10 Maximum length of leading items and dependent items in the output. MAX_CONSEQUENT Integer 10 Maximum length of righthand side. UBIQUITOUS Double 1.0 Ignores items whose support values are greater than the UBIQUITOUS value during the frequent items mining phase. LHS_RESTRICT Varchar No default value Specifies which items are allowed in the left-hand side of the association rule. RHS_RESTRICT Varchar No default value Specifies which items are allowed in the right-hand side of the association rule. 310 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
Name Data Type Default Value Description LHS_IS_COMPLEMEN TARY_RHS Integer 0 If you use RHS_RESTRICT to restrict some items to the right-hand side of the association rules, you can set this parameter to 1 to restrict the consequent items to the left-hand side. For example, if you have 1000 items (i1, i2, ..., i1000) and want to restrict i1 and i2 to the right-hand side, and i3, i4, ..., i1000 to the left-hand side, you can set the parameters similar to the following: INSERT INTO PAL_CONTROL_TBL VALUES (‘RHS_RESTRICT’, NULL, NULL, ‘i1’); INSERT INTO PAL_CONTROL_TBL VALUES (‘RHS_RESTRICT’, NULL, NULL, ‘i2’); INSERT INTO PAL_CONTROL_TBL VALUES (‘LHS_IS_COMPLEMENT ARY_RHS’, 1, NULL, NULL); RHS_IS_COMPLEMEN TARY_LHS Integer 0 If you use LHS_RESTRICT to restrict some items to the left-hand side of the association rules, you can set this parameter to 1 to restrict the consequent items to the right-hand side. THREAD_NUMBER Integer 1 Number of threads. SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 311
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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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Page 261 and 262: CREATE LOCAL TEMPORARY COLUMN TABLE
Page 263 and 264: Table Column Column Data Type Descr
Page 265 and 266: Assume we have m observation pairs
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Page 271 and 272: Example 1: Fitting multiple linear
Page 273 and 274: CREATE COLUMN TABLE PAL_ENET_MLR_PD
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Page 277 and 278: 3.3.5 Polynomial Regression Polynom
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Page 283 and 284: The input, parameter, and output ta
Page 285 and 286: 3.4 Association Algorithms This sec
Page 291 and 292: "STRINGARGS" VARCHAR (100) ); INSER
Page 293 and 294: Optional Parameters The following p
Page 297 and 298: INSERT INTO PAL_APRIORI_DATA_TBL VA
Page 299 and 300: PAL_APRIORI_CONS_ITEMS_TBL: LITEAPR
Page 303 and 304: INSERT INTO PAL_LITEAPRIORI_DATA_TB
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Page 309: FPGROWTH (Relational Output) FP-Gro
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Page 315 and 316: PAL_FPGROWTH_VALUES_TBL: 3.4.3 K-Op
Page 319 and 320: INSERT INTO PAL_KORD_DATA_TBL VALUE
Page 321 and 322: Prerequisite No missing or null dat
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Page 327 and 328: DROP TYPE PAL_ARIMAX_DATA_T; CREATE
Page 329 and 330: CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
Page 331 and 332: We get And variance of the forecast
Page 333 and 334: CREATE COLUMN TABLE PAL_ARIMA_PDATA
Page 335 and 336: Table Column Column Data Type Descr
Page 337 and 338: INSERT INTO PAL_ARIMAX_DATA_TBL VAL
Page 339 and 340: 3.5.2 Auto ARIMA This function auto
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Page 347 and 348: CREATE LOCAL TEMPORARY COLUMN TABLE
Page 349 and 350: Example 3: Auto Seasonal ARIMA SET
Page 351 and 352: INSERT INTO PAL_ARIMA_DATA_TBL VALU
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Page 355 and 356: F 1 = a 0 + b 0 2. Calculation: S t
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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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Prerequisite Each column must conta
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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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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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