From 2ca1b4dcea9a002ad378edd3dcbde80636c8897a Mon Sep 17 00:00:00 2001 From: i317814 Date: Thu, 2 Mar 2023 10:22:43 +0000 Subject: [PATCH] samples & runtimes for apl 4.2307.0 --- dataForHANACloud.tar.gz | Bin 5008711 -> 5008705 bytes runtimes/cpp/DateUtilities.h | 2 +- runtimes/java/KxJRT.jar | Bin 29915 -> 29721 bytes runtimes/java/src/DateUtils.java | 12 ++-- runtimes/java/src/IKxJModel.java | 2 +- runtimes/java/src/KxFileReader.java | 27 +------- runtimes/java/src/KxFileReaderWithNames.java | 2 +- runtimes/java/src/KxInputReader.java | 11 --- runtimes/java/src/KxJApplyOnFile.java | 2 +- runtimes/java/src/KxJApplyOnInput.java | 2 +- runtimes/java/src/KxJApplyOnJDBC.java | 12 ++-- runtimes/java/src/KxJApplyWithMapWrapper.java | 8 ++- runtimes/java/src/KxJDBCInput.java | 2 - .../segmented_apply_hce.sql | 64 ++++++++++++++++++ 14 files changed, 90 insertions(+), 56 deletions(-) create mode 100644 sql/any/apl_samples/binary-classification/segmented_apply_hce.sql diff --git 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zLV)!!94uk1jGTNfLQ*Hdn~_O`8NLA^tk!Owz;<9DyaWzNO2d^iFf3vG9yd8KPImI! zB1qi-DUxRTo;W!$QE{?WF+|9-Sej{9%H*_Sd9dB(T(w!hfks&aQ>YB8QE8I{)8sJh e{= 1) && (lFirstDay <=3)) + if ((31 == lNbDays) && (lFirstDay >= 1) && (lFirstDay <=3)) return true; - else if ((30 == lMonthDay) && ((2 == lFirstDay) || (3 == lFirstDay))) + else if ((30 == lNbDays) && ((2 == lFirstDay) || (3 == lFirstDay))) return true; - else if ((29 == lMonthDay) && (3 == lMonthDay)) + else if ((29 == lNbDays) && (3 == lFirstDay)) return true; else return false; @@ -209,7 +209,7 @@ public static int getWeekOfMonth(Date iDate) { lWMonth1 = 4 + (l5Weeks ? 1 : 0); } - int lNbDaysInMonth = getDayInMonth(iDate); + int lNbDaysInMonth = getDaysInMonth(iDate); Calendar lNewDate = Calendar.getInstance(); lNewDate.set(lCalendar.get(Calendar.YEAR), diff --git a/runtimes/java/src/IKxJModel.java b/runtimes/java/src/IKxJModel.java index af4d195..d2bd619 100644 --- a/runtimes/java/src/IKxJModel.java +++ b/runtimes/java/src/IKxJModel.java @@ -11,7 +11,7 @@ package KxJRT; /** - * The Automatic Analytics Java model interface. + * The Automatic Analytics Java model interface. * Generated Java code for K2R and K2S implements this interface. * @version 1.0 */ diff --git a/runtimes/java/src/KxFileReader.java b/runtimes/java/src/KxFileReader.java index 471dbc1..6bd43c0 100644 --- a/runtimes/java/src/KxFileReader.java +++ b/runtimes/java/src/KxFileReader.java @@ -146,6 +146,9 @@ public boolean hasMoreLines() { while (lCurrentLine.equals("")) { try { lCurrentLine = mBuffer.readLine(); + if (lCurrentLine == null) { + return false; + } } catch ( IOException e ) { return false; @@ -174,30 +177,6 @@ private void initFields( String iLine ) { mFields = new String[lFieldsCount]; } - private void printLineError() { - KxLog.getInstance().print("More fields are found\n"); - System.err.println( "error : found more fields than exprected line "+ - mRowCounter +"."); - } - - private void printFieldError( int iFieldIndex ) { - KxLog.getInstance().print( "warning : invalid type for field "+ - iFieldIndex+ " line " + mRowCounter + "." ); - } - - private void printEmptyField( int iFieldIndex ) { - if (null != mNameFields) { - KxLog.getInstance().print( "warning : empty value for " + - mNameFields[iFieldIndex] +" at line " + - mRowCounter + "." ); - } - else { - KxLog.getInstance().print( "warning : empty value for fields " + - iFieldIndex +" at line " + - mRowCounter + "." ); - } - } - public boolean isEmpty( int iVariableIndex, String iMissingString ) { String lValue = mFields[ iVariableIndex ]; if( lValue == null diff --git a/runtimes/java/src/KxFileReaderWithNames.java b/runtimes/java/src/KxFileReaderWithNames.java index d0271c5..8170be2 100644 --- a/runtimes/java/src/KxFileReaderWithNames.java +++ b/runtimes/java/src/KxFileReaderWithNames.java @@ -58,7 +58,7 @@ private void init() { } } - public String[] getVariables() { + final public String[] getVariables() { return mVariables; } diff --git a/runtimes/java/src/KxInputReader.java b/runtimes/java/src/KxInputReader.java index fb1c814..52f93fd 100644 --- a/runtimes/java/src/KxInputReader.java +++ b/runtimes/java/src/KxInputReader.java @@ -59,17 +59,6 @@ public void initFields( String iLine ) { } } - private void printLineError() { - KxLog.getInstance().print("More fields are found\n"); - System.err.println( "error : found more fields than exprected line "+ - mRowCounter +"."); - } - - private void printFieldError( int iFieldIndex ) { - KxLog.getInstance().print( "warning : invalid type for field "+ - iFieldIndex+ " line " + mRowCounter + "." ); - } - private void printEmptyField( int iFieldIndex ) { KxLog.getInstance().print("warning : empty value for fields " + iFieldIndex +" at line " + diff --git a/runtimes/java/src/KxJApplyOnFile.java b/runtimes/java/src/KxJApplyOnFile.java index 610cae1..266b40d 100644 --- a/runtimes/java/src/KxJApplyOnFile.java +++ b/runtimes/java/src/KxJApplyOnFile.java @@ -14,7 +14,7 @@ import java.lang.*; /** - * Applies a Automatic Analytics Java model on a flat file + * Applies a Automatic Analytics Java model on a flat file * with variable names on the first line. * @see KxJRT.KxFileReaderWithNames KxFileReaderWithNames * @see KxJRT.KxFileReader KxFileReader diff --git a/runtimes/java/src/KxJApplyOnInput.java b/runtimes/java/src/KxJApplyOnInput.java index 50d55d2..55adc74 100644 --- a/runtimes/java/src/KxJApplyOnInput.java +++ b/runtimes/java/src/KxJApplyOnInput.java @@ -15,7 +15,7 @@ import java.util.TreeMap; /** - * Applies a Automatic Analytics Java model on a line + * Applies a Automatic Analytics Java model on a line * @see KxJRT.KxInputReader KxInputReader */ public class KxJApplyOnInput { diff --git a/runtimes/java/src/KxJApplyOnJDBC.java b/runtimes/java/src/KxJApplyOnJDBC.java index f854073..acf0c25 100644 --- a/runtimes/java/src/KxJApplyOnJDBC.java +++ b/runtimes/java/src/KxJApplyOnJDBC.java @@ -176,13 +176,15 @@ else if( iArgs[i].equalsIgnoreCase("-usage") || lApplyScore = new KxJApplyOnJDBC(lModel); loadDriver(lDriver); - - Connection lConnection = newConnection(lDatabase, lUser, lPwd); - Statement lStatement = lConnection.createStatement(); - ResultSet lResult = lStatement.executeQuery(lQuery); + try(Connection lConnection = newConnection(lDatabase, lUser, lPwd); + Statement lStatement = lConnection.createStatement()) + { - lApplyScore.apply(lResult, lOut); + ResultSet lResult = lStatement.executeQuery(lQuery); + + lApplyScore.apply(lResult, lOut); + } } catch(Exception e ) { diff --git a/runtimes/java/src/KxJApplyWithMapWrapper.java b/runtimes/java/src/KxJApplyWithMapWrapper.java index 4046ebd..8d54ed1 100644 --- a/runtimes/java/src/KxJApplyWithMapWrapper.java +++ b/runtimes/java/src/KxJApplyWithMapWrapper.java @@ -7,8 +7,6 @@ import java.text.ParseException; public class KxJApplyWithMapWrapper { - public static String mDesc = "Automatic Analytics command line score calculator.\n Expected input:\n \n KxJApplyWithMapWrapper ScorerClass var1=value1 var2=value2 ...\n \n where \"ScorerClass\" is the name of the class generated by Automatic Analytics (which should\n be in the classpath)"; - public String mScorerClassName; private IKxJModel mScorerModel; @@ -120,7 +118,11 @@ else if( iArgs[i].equalsIgnoreCase("-usage") ) { BufferedReader lBuffer = new BufferedReader(lStreamReader); // skip the first row which contains the header file - String lCurrentLine = lBuffer.readLine(); + String lCurrentLine = lBuffer.readLine(); + if (lCurrentLine == null) { + System.out.println("Empty input file."); + return; + } String[] lVariables = lCurrentLine.split(lSeparator); for (String lOutput:lMapWrapper.mScorerModel.getModelOutputVariables()) diff --git a/runtimes/java/src/KxJDBCInput.java b/runtimes/java/src/KxJDBCInput.java index e35bf19..5573a9c 100644 --- a/runtimes/java/src/KxJDBCInput.java +++ b/runtimes/java/src/KxJDBCInput.java @@ -99,7 +99,6 @@ public float floatValue( int iIndex ) { mMissing = false; } catch( SQLException e ) { - lValue = (float)0.0; mMissing = true; } return lValue; @@ -112,7 +111,6 @@ public double doubleValue( int iIndex ) { mMissing = false; } catch( SQLException e ) { - lValue = 0.0; mMissing = true; } return lValue; diff --git a/sql/any/apl_samples/binary-classification/segmented_apply_hce.sql b/sql/any/apl_samples/binary-classification/segmented_apply_hce.sql new file mode 100644 index 0000000..48e6869 --- /dev/null +++ b/sql/any/apl_samples/binary-classification/segmented_apply_hce.sql @@ -0,0 +1,64 @@ +-- @required(hanaMinimumVersion,4.00.000) +-- ================================================================ +-- APL_AREA, APPLY_MODEL, using a binary format for the model +-- This script demonstrates the application of the binary classification model +-- to predict the target and to get the individual contributions per input +-- +-- Assumption 1: The users & privileges have been created & granted (see apl_admin_ex.sql). +-- Assumption 2: There's a valid trained model (created by APL) in the MODEL_TRAIN_BIN table. +-- @depend(segmented_create_train_hce.sql) +connect USER_APL password Password1; +SET SESSION 'APL_CACHE_SCHEMA' = 'APL_CACHE'; + +drop table APPLY_OPERATION_LOG; +create table APPLY_OPERATION_LOG like "SAP_PA_APL"."sap.pa.apl.base::BASE.T.OPERATION_LOG"; + +drop table APPLY_SUMMARY; +create table APPLY_SUMMARY like "SAP_PA_APL"."sap.pa.apl.base::BASE.T.SUMMARY"; + +-- USER_APL.APPLY_OUT definition +DROP TYPE APPLY_OUT_T; +CREATE TYPE APPLY_OUT_T AS TABLE ("KxIndex" BIGINT,"class" INTEGER,"gb_score_class" DOUBLE, "Seg" INTEGER); + +DROP TABLE APPLY_OUT; +CREATE TABLE APPLY_OUT LIKE APPLY_OUT_T; + + +DO BEGIN + declare header "SAP_PA_APL"."sap.pa.apl.base::BASE.T.FUNCTION_HEADER"; + declare config "SAP_PA_APL"."sap.pa.apl.base::BASE.T.OPERATION_CONFIG_EXTENDED"; + declare model "SAP_PA_APL"."sap.pa.apl.base::BASE.T.MODEL_BIN_OID"; + declare out_log "SAP_PA_APL"."sap.pa.apl.base::BASE.T.OPERATION_LOG"; + declare out_sum "SAP_PA_APL"."sap.pa.apl.base::BASE.T.SUMMARY"; + declare apply_out APPLY_OUT_T; + + insert into :model select * from MODEL_TRAIN_BIN; + datatset = SELECT * from "USER_APL"."ADULT01_SORTED"; + + :header.insert(('Oid', '#42')); + :header.insert(('LogLevel', '2')); + :header.insert(('ModelFormat', 'bin')); + :header.insert(('MaxTasks', '2')); -- define nb parallel tasks to use for train + + :config.insert(('APL/SegmentColumnName', 'Seg',null)); -- define the column used as the segmentation colum + + CALL _SYS_AFL.APL_APPLY_MODEL__OVERLOAD_4_3(:header, :model, :config, :datatset,apply_out,out_log,out_sum); + + insert into APPLY_OUT select * from :apply_out; + insert into APPLY_OPERATION_LOG select * from :out_log; + insert into APPLY_SUMMARY select * from :out_sum; +END; + +SELECT "Seg", count(*) from APPLY_OUT group by "Seg"; + +-- Nb trained models +select count(*) from APPLY_SUMMARY where "KEY" = 'AplTaskElapsedTime'; + +-- Average time to train a segment +select AVG(to_double("VALUE")) from APPLY_SUMMARY where "KEY" = 'AplTaskElapsedTime'; + +-- Total time +select * from APPLY_SUMMARY where "KEY" = 'AplTotalElapsedTime'; + +-- Get models in error +select * from APPLY_OPERATION_LOG where "LEVEL" = 0;

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