SAP HANA Predictive Analysis Library (PAL)

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DELETE FROM PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS; CALL DM_PAL.PAL_DISTRRANDOM(PAL_DISTRRANDOM_DISTRPARAM_TAB, PAL_CONTROL_TAB, PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS) with overview; --SELECT * FROM PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS; -- calculate cash flow ---- DROP TABLE PAL_CASHFLOW_YEAR4; CREATE COLUMN TABLE PAL_CASHFLOW_YEAR4(ID INTEGER,CASH DOUBLE); INSERT INTO PAL_CASHFLOW_YEAR4 SELECT PAL_DISTRRANDOM_RESULT_TAB_REVENUE.ID, PAL_DISTRRANDOM_RESULT_TAB_REVENUE.RANDOM - PAL_DISTRRANDOM_RESULT_TAB_COSTS.RANDOM - PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS.RANDOM FROM PAL_DISTRRANDOM_RESULT_TAB_REVENUE left join PAL_DISTRRANDOM_RESULT_TAB_COSTS on PAL_DISTRRANDOM_RESULT_TAB_REVENUE.ID=PAL_DISTRRANDOM_RESULT_TAB_COSTS.ID left join PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS on PAL_DISTRRANDOM_RESULT_TAB_REVENUE.ID=PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS.ID; --SELECT * from PAL_CASHFLOW_YEAR4; ---------------------------- -------YEAR 5 -------------- --Product revenue---- DELETE FROM PAL_DISTRRANDOM_DISTRPARAM_TAB; INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('DISTRIBUTIONNAME', 'NORMAL'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('MEAN', '30000'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('VARIANCE', '9000000'); DELETE FROM PAL_DISTRRANDOM_RESULT_TAB_REVENUE; CALL DM_PAL.PAL_DISTRRANDOM(PAL_DISTRRANDOM_DISTRPARAM_TAB, PAL_CONTROL_TAB, PAL_DISTRRANDOM_RESULT_TAB_REVENUE) with overview; --SELECT * FROM PAL_DISTRRANDOM_RESULT_TAB_REVENUE; ----Product Costs--------- DELETE FROM PAL_DISTRRANDOM_DISTRPARAM_TAB; INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('DISTRIBUTIONNAME', 'NORMAL'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('MEAN', '10000'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('VARIANCE', '562500'); DELETE FROM PAL_DISTRRANDOM_RESULT_TAB_COSTS; CALL DM_PAL.PAL_DISTRRANDOM(PAL_DISTRRANDOM_DISTRPARAM_TAB, PAL_CONTROL_TAB, PAL_DISTRRANDOM_RESULT_TAB_COSTS) with overview; --SELECT * FROM PAL_DISTRRANDOM_RESULT_TAB_COSTS; ----OVERHEADS--------- DELETE FROM PAL_DISTRRANDOM_DISTRPARAM_TAB; INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('DISTRIBUTIONNAME', 'UNIFORM'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('MIN', '3000'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('MAX', '4000'); DELETE FROM PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS; CALL DM_PAL.PAL_DISTRRANDOM(PAL_DISTRRANDOM_DISTRPARAM_TAB, PAL_CONTROL_TAB, PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS) with overview; --SELECT * FROM PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS; -- calculate cash flow ---- DROP TABLE PAL_CASHFLOW_YEAR5; CREATE COLUMN TABLE PAL_CASHFLOW_YEAR5(ID INTEGER,CASH DOUBLE); INSERT INTO PAL_CASHFLOW_YEAR5 SELECT PAL_DISTRRANDOM_RESULT_TAB_REVENUE.ID, PAL_DISTRRANDOM_RESULT_TAB_REVENUE.RANDOM - PAL_DISTRRANDOM_RESULT_TAB_COSTS.RANDOM - PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS.RANDOM FROM PAL_DISTRRANDOM_RESULT_TAB_REVENUE left join PAL_DISTRRANDOM_RESULT_TAB_COSTS on PAL_DISTRRANDOM_RESULT_TAB_REVENUE.ID=PAL_DISTRRANDOM_RESULT_TAB_COSTS.ID left join PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS on PAL_DISTRRANDOM_RESULT_TAB_REVENUE.ID=PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS.ID; --SELECT * from PAL_CASHFLOW_YEAR5; Step 2 Calculate the net present value of the investment by the following equation for each sampling. 562 P U B L I C SAP HANA Predictive Analysis Library (PAL) End-to-End Scenarios
---- calculate net present value of investment ---- DROP TABLE NPV; CREATE COLUMN TABLE NPV(NPVALUE DOUBLE); INSERT INTO NPV SELECT PAL_CASHFLOW_YEAR1.CASH + PAL_CASHFLOW_YEAR2.CASH/1.05 + PAL_CASHFLOW_YEAR3.CASH/POWER(1.05,2) + PAL_CASHFLOW_YEAR4.CASH/POWER(1.05,3) + PAL_CASHFLOW_YEAR5.CASH/POWER(1.05,4) FROM PAL_CASHFLOW_YEAR1 left join PAL_CASHFLOW_YEAR2 on PAL_CASHFLOW_YEAR1.ID = PAL_CASHFLOW_YEAR2.ID left join PAL_CASHFLOW_YEAR3 on PAL_CASHFLOW_YEAR1.ID = PAL_CASHFLOW_YEAR3.ID left join PAL_CASHFLOW_YEAR4 on PAL_CASHFLOW_YEAR1.ID = PAL_CASHFLOW_YEAR4.ID left join PAL_CASHFLOW_YEAR5 on PAL_CASHFLOW_YEAR1.ID = PAL_CASHFLOW_YEAR5.ID; SELECT * FROM NPV; The expected result is as follows: Step 3 Plot the distribution of the net present value of the investment and run Distribution Fitting to fit a normal distribution to the NPV of the investment as. (The Central Limit theorem states that the output distribution will be a normal distribution.) --------------------------------------- ------- distribution fit process --- --------------------------------------- DROP TYPE PAL_DISTRFIT_DATA_T; CREATE TYPE PAL_DISTRFIT_DATA_T AS TABLE(NPVALUE DOUBLE); DROP TYPE PAL_DISTRFIT_ESTIMATION_T; CREATE TYPE PAL_DISTRFIT_ESTIMATION_T AS TABLE(NAME VARCHAR(50),VAL VARCHAR(50)); DROP TYPE PAL_DISTRFIT_STATISTICS_T; CREATE TYPE PAL_DISTRFIT_STATISTICS_T AS TABLE(NAME VARCHAR(50),VAL DOUBLE); DROP TYPE PAL_CONTROL_T; CREATE TYPE PAL_CONTROL_T AS TABLE(NAME VARCHAR (50),INTARGS INTEGER,DOUBLEARGS DOUBLE,STRINGARGS VARCHAR (100)); DROP TABLE PDATA_TBL; CREATE COLUMN TABLE PDATA_TBL("POSITION" INT, "SCHEMA_NAME" NVARCHAR(256), "TYPE_NAME" NVARCHAR(256), "PARAMETER_TYPE" VARCHAR(7)); INSERT INTO PDATA_TBL VALUES (1, 'DM_PAL','PAL_DISTRFIT_DATA_T', 'IN'); INSERT INTO PDATA_TBL VALUES (2, 'DM_PAL','PAL_CONTROL_T', 'IN'); INSERT INTO PDATA_TBL VALUES (3, 'DM_PAL','PAL_DISTRFIT_ESTIMATION_T', 'OUT'); INSERT INTO PDATA_TBL VALUES (4, 'DM_PAL','PAL_DISTRFIT_STATISTICS_T', 'OUT'); call SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('DM_PAL','PAL_DISTRFIT'); call SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL', 'DISTRFIT', 'DM_PAL','PAL_DISTRFIT', PDATA_TBL); DROP TABLE PAL_CONTROL_TBL; SAP HANA Predictive Analysis Library (PAL) End-to-End Scenarios P U B L I C 563
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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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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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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 521 and 522: The signature table should contain
Page 523 and 524: Table Column Column Data Type Descr
Page 525 and 526: PAL_KMSURV_LOGRANK_STAT2_TBL: 3.7.7
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Page 533 and 534: where x' = x - Table 404: Definitio
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Page 537 and 538: Let the sample mean of x and y be:
Page 539 and 540: CREATE TYPE PAL_VAREQUALTEST_DATA1_
Page 541 and 542: The quantity is computed as a weigh
Page 543 and 544: Table Column Column Data Type Descr
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
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Page 559 and 560: INSERT INTO PDATA VALUES (2, 'DM_PA
Page 561: ----product revenue---- DELETE FROM
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
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Page 573 and 574: The expected results are as follows
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SAP HANA Predictive Analysis Library (PAL)

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