Examples of org.dmg.pmml.pmml_4_2.descr.TreeModel

org.dmg.pmml.pmml_4_2.descr.Characteristic
3.org/2001/XMLSchema}anyType"> <sequence> <element ref="{http://www.dmg.org/PMML-4_2}Extension" maxOccurs="unbounded" minOccurs="0"/> <element ref="{http://www.dmg.org/PMML-4_2}MiningSchema"/> <element ref="{http://www.dmg.org/PMML-4_2}Output" minOccurs="0"/> <element ref="{http://www.dmg.org/PMML-4_2}ModelStats" minOccurs="0"/> <element ref="{http://www.dmg.org/PMML-4_2}ModelExplanation" minOccurs="0"/> <element ref="{http://www.dmg.org/PMML-4_2}Targets" minOccurs="0"/> <element ref="{http://www.dmg.org/PMML-4_2}LocalTransformations" minOccurs="0"/> <element ref="{http://www.dmg.org/PMML-4_2}Node"/> <element ref="{http://www.dmg.org/PMML-4_2}ModelVerification" minOccurs="0"/> <element ref="{http://www.dmg.org/PMML-4_2}Extension" maxOccurs="unbounded" minOccurs="0"/> </sequence> <attribute name="modelName" type="{http://www.w3.org/2001/XMLSchema}string" /> <attribute name="functionName" use="required" type="{http://www.dmg.org/PMML-4_2}MINING-FUNCTION" /> <attribute name="algorithmName" type="{http://www.w3.org/2001/XMLSchema}string" /> <attribute name="missingValueStrategy" type="{http://www.dmg.org/PMML-4_2}MISSING-VALUE-STRATEGY" default="none" /> <attribute name="missingValuePenalty" type="{http://www.dmg.org/PMML-4_2}PROB-NUMBER" default="1.0" /> <attribute name="noTrueChildStrategy" type="{http://www.dmg.org/PMML-4_2}NO-TRUE-CHILD-STRATEGY" default="returnNullPrediction" /> <attribute name="splitCharacteristic" default="multiSplit"> <simpleType> <restriction base="{http://www.w3.org/2001/XMLSchema}string"> <enumeration value="binarySplit"/> <enumeration value="multiSplit"/> </restriction> </simpleType> </attribute> <attribute name="isScorable" type="{http://www.w3.org/2001/XMLSchema}boolean" default="true" /> </restriction> </complexContent> </complexType>

        PMML4Compiler compiler = new PMML4Compiler();
        PMML pmml = compiler.loadModel( PMML, ResourceFactory.newClassPathResource( source2 ).getInputStream() );


        for ( Object o : pmml.getAssociationModelsAndBaselineModelsAndClusteringModels() ) {
            if ( o instanceof TreeModel ) {
                TreeModel tree = (TreeModel) o;
                tree.setMissingValueStrategy( MISSINGVALUESTRATEGY.NONE );
            }
        }


        String theory = compiler.generateTheory( pmml );
        if ( VERBOSE ) {

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        PMML4Compiler compiler = new PMML4Compiler(); 
        PMML pmml = compiler.loadModel( PMML, ResourceFactory.newClassPathResource( source2 ).getInputStream() );


        for ( Object o : pmml.getAssociationModelsAndBaselineModelsAndClusteringModels() ) {
            if ( o instanceof TreeModel ) {
                TreeModel tree = (TreeModel) o;
                tree.setMissingValueStrategy( MISSINGVALUESTRATEGY.DEFAULT_CHILD );
            }
        }


        KieSession kSession = getSession( compiler.generateTheory( pmml ) );

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        PMML4Compiler compiler = new PMML4Compiler();
        PMML pmml = compiler.loadModel( PMML, ResourceFactory.newClassPathResource( source2 ).getInputStream() );


        for ( Object o : pmml.getAssociationModelsAndBaselineModelsAndClusteringModels() ) {
            if ( o instanceof TreeModel ) {
                TreeModel tree = (TreeModel) o;
                tree.setMissingValueStrategy( MISSINGVALUESTRATEGY.DEFAULT_CHILD );
            }
        }


        String theory = compiler.generateTheory( pmml );
        if ( VERBOSE ) {

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        PMML4Compiler compiler = new PMML4Compiler();
        PMML pmml = compiler.loadModel( PMML, ResourceFactory.newClassPathResource( source2 ).getInputStream() );


        for ( Object o : pmml.getAssociationModelsAndBaselineModelsAndClusteringModels() ) {
            if ( o instanceof TreeModel ) {
                TreeModel tree = (TreeModel) o;
                tree.setMissingValueStrategy( MISSINGVALUESTRATEGY.LAST_PREDICTION );
            }
        }


        String theory = compiler.generateTheory( pmml );
        if ( VERBOSE ) {

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        PMML4Compiler compiler = new PMML4Compiler();
        PMML pmml = compiler.loadModel( PMML, ResourceFactory.newClassPathResource( source2 ).getInputStream() );


        for ( Object o : pmml.getAssociationModelsAndBaselineModelsAndClusteringModels() ) {
            if ( o instanceof TreeModel ) {
                TreeModel tree = (TreeModel) o;
                tree.setMissingValueStrategy( MISSINGVALUESTRATEGY.NULL_PREDICTION );
            }
        }


        String theory = compiler.generateTheory( pmml );
        if ( VERBOSE ) {

View Full Code Here

        PMML4Compiler compiler = new PMML4Compiler();
        PMML pmml = compiler.loadModel( PMML, ResourceFactory.newClassPathResource( source2 ).getInputStream() );


        for ( Object o : pmml.getAssociationModelsAndBaselineModelsAndClusteringModels() ) {
            if ( o instanceof TreeModel ) {
                TreeModel tree = (TreeModel) o;
                tree.setMissingValueStrategy( MISSINGVALUESTRATEGY.AGGREGATE_NODES );
            }
        }


        String theory = compiler.generateTheory( pmml );
        if ( VERBOSE ) {

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            return "new Double[] { " + pd.getMean() + " }";
        } else if ( d instanceof UniformDistribution ) {
            UniformDistribution ud = (UniformDistribution) d;
            return "new Double[] { " + ud.getLower() + ", " + ud.getUpper() + " }";
        } else if ( d instanceof AnyDistribution ) {
            AnyDistribution ad = (AnyDistribution) d;
            return "new Double[] { " + ad.getMean() + ", " + ad.getVariance() + " }";
        }
        throw new IllegalStateException( "Unrecognized Distribution type " + d.getClass().getName() );
    }

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            extension.setName( PMMLExtensionNames.EXTERNAL_CLASS );
            extension.setValue( characteristic.getFact() );
            miningField.getExtensions().add( extension );


            for ( final org.drools.workbench.models.guided.scorecard.shared.Attribute attribute : characteristic.getAttributes() ) {
                final Attribute _attribute = new Attribute();
                _characteristic.getAttributes().add( _attribute );


                extension = new Extension();
                extension.setName( ScorecardPMMLExtensionNames.CHARACTERTISTIC_FIELD );
                extension.setValue( characteristic.getField() );
                _attribute.getExtensions().add( extension );


                if ( model.isUseReasonCodes() ) {
                    _attribute.setReasonCode( attribute.getReasonCode() );
                }
                _attribute.setPartialScore( attribute.getPartialScore() );


                final String operator = attribute.getOperator();
                final String dataType = characteristic.getDataType();
                String predicateResolver;
                if ( "boolean".equalsIgnoreCase( dataType ) ) {
                    predicateResolver = operator.toUpperCase();
                } else if ( "String".equalsIgnoreCase( dataType ) ) {
                    if ( operator.contains( "=" ) ) {
                        predicateResolver = operator + attribute.getValue();
                    } else {
                        predicateResolver = attribute.getValue() + ",";
                    }
                } else {
                    if ( NUMERIC_OPERATORS.contains( operator ) ) {
                        predicateResolver = operator + " " + attribute.getValue();
                    } else {
                        predicateResolver = attribute.getValue().replace( ",", "-" );
                    }
                }
                extension = new Extension();
                extension.setName( "predicateResolver" );
                extension.setValue( predicateResolver );
                _attribute.getExtensions().add( extension );
            }
        }


        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( miningSchema );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( output );

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            extension.setName( PMMLExtensionNames.EXTERNAL_CLASS );
            extension.setValue( characteristic.getFact() );
            miningField.getExtensions().add( extension );


            for ( final org.drools.workbench.models.guided.scorecard.shared.Attribute attribute : characteristic.getAttributes() ) {
                final Attribute _attribute = new Attribute();
                _characteristic.getAttributes().add( _attribute );


                extension = new Extension();
                extension.setName( ScorecardPMMLExtensionNames.CHARACTERTISTIC_FIELD );
                extension.setValue( characteristic.getField() );
                _attribute.getExtensions().add( extension );


                if ( model.isUseReasonCodes() ) {
                    _attribute.setReasonCode( attribute.getReasonCode() );
                }
                _attribute.setPartialScore( attribute.getPartialScore() );


                final String operator = attribute.getOperator();
                final String dataType = characteristic.getDataType();
                String predicateResolver;
                if ( "boolean".equalsIgnoreCase( dataType ) ) {
                    predicateResolver = operator.toUpperCase();
                } else if ( "String".equalsIgnoreCase( dataType ) ) {
                    if ( operator.contains( "=" ) ) {
                        predicateResolver = operator + attribute.getValue();
                    } else {
                        predicateResolver = attribute.getValue() + ",";
                    }
                } else {
                    if ( NUMERIC_OPERATORS.contains( operator ) ) {
                        predicateResolver = operator + " " + attribute.getValue();
                    } else {
                        predicateResolver = attribute.getValue().replace( ",", "-" );
                    }
                }
                extension = new Extension();
                extension.setName( "predicateResolver" );
                extension.setValue( predicateResolver );
                _attribute.getExtensions().add( extension );
            }
        }


        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( miningSchema );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( output );

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            pmmlScorecard.setBaselineScore( model.getBaselineScore() );
            pmmlScorecard.setReasonCodeAlgorithm( model.getReasonCodesAlgorithm() );
        }


        for ( final org.drools.workbench.models.guided.scorecard.shared.Characteristic characteristic : model.getCharacteristics() ) {
            final Characteristic _characteristic = new Characteristic();
            characteristics.getCharacteristics().add( _characteristic );


            extension = new Extension();
            extension.setName( PMMLExtensionNames.EXTERNAL_CLASS );
            extension.setValue( characteristic.getFact() );
            _characteristic.getExtensions().add( extension );


            extension = new Extension();
            extension.setName( ScorecardPMMLExtensionNames.CHARACTERTISTIC_DATATYPE );
            if ( "string".equalsIgnoreCase( characteristic.getDataType() ) ) {
                extension.setValue( XLSKeywords.DATATYPE_TEXT );
            } else if ( "int".equalsIgnoreCase( characteristic.getDataType() ) || "double".equalsIgnoreCase( characteristic.getDataType() ) ) {
                extension.setValue( XLSKeywords.DATATYPE_NUMBER );
            } else if ( "boolean".equalsIgnoreCase( characteristic.getDataType() ) ) {
                extension.setValue( XLSKeywords.DATATYPE_BOOLEAN );
            } else {
                System.out.println( ">>>> Found unknown data type :: " + characteristic.getDataType() );
            }
            _characteristic.getExtensions().add( extension );


            _characteristic.setBaselineScore( characteristic.getBaselineScore() );
            if ( model.isUseReasonCodes() ) {
                _characteristic.setReasonCode( characteristic.getReasonCode() );
            }
            _characteristic.setName( characteristic.getName() );


            final MiningField miningField = new MiningField();
            miningField.setName( characteristic.getField() );
            miningField.setUsageType( FIELDUSAGETYPE.ACTIVE );
            miningField.setInvalidValueTreatment( INVALIDVALUETREATMENTMETHOD.RETURN_INVALID );
            miningSchema.getMiningFields().add( miningField );


            extension = new Extension();
            extension.setName( PMMLExtensionNames.EXTERNAL_CLASS );
            extension.setValue( characteristic.getFact() );
            miningField.getExtensions().add( extension );


            for ( final org.drools.workbench.models.guided.scorecard.shared.Attribute attribute : characteristic.getAttributes() ) {
                final Attribute _attribute = new Attribute();
                _characteristic.getAttributes().add( _attribute );


                extension = new Extension();
                extension.setName( ScorecardPMMLExtensionNames.CHARACTERTISTIC_FIELD );
                extension.setValue( characteristic.getField() );
                _attribute.getExtensions().add( extension );

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Related Classes of org.dmg.pmml.pmml_4_2.descr.TreeModel

org.drools.pmml.pmml_4_1.predictive.models.DecisionTreeTest

org.drools.pmml.pmml_4_2.PMML4Compiler

org.drools.pmml.pmml_4_2.PMML4Helper

org.drools.pmml.pmml_4_2.PMMLGenerationTest

org.drools.pmml.pmml_4_2.predictive.models.DecisionTreeTest

org.drools.scorecards.DrlFromPMMLTest

org.drools.scorecards.ExternalObjectModelTest

org.drools.scorecards.parser.xls.ExcelScorecardValidator

org.drools.scorecards.ScorecardReasonCodeTest

org.drools.scorecards.ScoringStrategiesTest

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