SAP HANA Predictive Analysis Library (PAL)

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BPNN performs three steps as follows: 1. Build the network structure and initialize the network. 2. Feed the training data repeatedly to train the network. 3. Make prediction with the trained network. Training During the training, BPNN adjusts the connection weights by comparing its output and expected target to make the output more accurate. The following training styles are available: ● ● Batch training: weights updating is based on the error of the entire package of training patterns. Thus, in one round the weights are updated once. Stochastic training: weights updating is based on the error of a single training pattern. Thus, in one round the weights are updated for each pattern. Support for Categorical Attributes If an attribute is of category type, it will be converted to a binary vector and then be used as numerical attributes. For example, in the below table, "Gender" is of category type. Table 87: Customer ID Age Income Gender T1 31 10,000 Female T2 27 8,000 Male Because "Gender" has two distinct values, it will be converted into a binary vector with two dimensions: Table 88: Customer ID Age Income Gender_1 Gender_2 T1 31 10,000 0 1 T2 27 8,000 1 0 Prerequisites For training data: ● ● ● The data are of integer, varchar, nvarchar, or double data type and do not contain null value. Otherwise the algorithm will issue errors. If it is for classification, then the last column is considered as the label column and is of integer, varchar, or nvarchar type. If it is for regression, then you should specify how many last columns are considered as target values, and they are of integer or double type. For predicted data: ● ● ● The data are of integer, varchar, nvarchar, or double data type and does not contain null value. Otherwise the algorithm will issue errors. The first column is ID column and should be of integer type. The column order and column number of the predicted data are the same as the order and number used in model training. 126 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
CREATEBPNN This function trains a BPNN model from input data. Procedure Generation CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL', 'CREATEBPNN', '', '', ); The signature table should contain the following records: Table 89: Position Schema Name Table Type Name Parameter Type 1 IN 2 IN 3 4 OUT OUT Procedure Calling CALL .(, , , ) WITH OVERVIEW; The procedure name is the same as specified in the procedure generation. The input, parameter, and output tables must be of the types specified in the signature table. Signature Input Table Table 90: Table Column Column Data Type Description Training data Columns Integer, double, varchar, or nvarchar Attribute data Last column(s) Integer, varchar, or nvarchar for classification; Integer or double for regression. Label column for classification; Target value for regression. Parameter Table Mandatory Parameters The following parameters are mandatory and must be given a value. Table 91: Name Data Type Description HIDDEN_LAYER_ACTIVE_FUNC Integer Active function code for the hidden layer. SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 127
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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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Page 77 and 78: Expected Result PAL_SILHOUETTE_RESU
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Page 81 and 82: Example Assume that: ● ● ● DM
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Page 91 and 92: The input, parameter, and output ta
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Page 99 and 100: Procedure Calling CALL .(, , ) with
Page 103 and 104: Expected Result DOCTOPICDIST_TBL: 3
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Page 111 and 112: CREATE TYPE PAL_SOM_MODEL_T AS TABL
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Page 117 and 118: INSERT INTO PAL_SLIGHT_SIL_PDATA_TB
Page 119 and 120: AUC Procedure Generation CALL SYS.A
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Page 123 and 124: Example 2 SET SCHEMA DM_PAL; DROP T
Page 125: 3.2.2 Back Propagation Neural Netwo
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Page 133 and 134: PAL_TRAIN_NN_RESULT_TBL: PAL_REGRES
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Page 137 and 138: "NAME" VARCHAR(100), "MODEL" CLOB )
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Page 157 and 158: PAL_CHAID_TREEMODEL_TBL: PAL_CHAID_
Page 161 and 162: INSERT INTO PAL_CM_DATA_TBL VALUES(
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Page 165 and 166: PAL_KNN_RESULTS_TBL: 3.2.8 Logistic
Page 167 and 168: Procedure Calling CALL .(, , , , )
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Page 175 and 176: 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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Table Column Column Data Type Descr
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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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INSERT INTO #PAL_CONTROL_TBL VALUES
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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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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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SAP HANA Predictive Analysis Library (PAL)

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