Package org.dmg.pmml.pmml_4_2.descr

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

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        final Scorecard pmmlScorecard = ScorecardPMMLUtils.createScorecard();
        final Output output = new Output();
        final Characteristics characteristics = new Characteristics();
        final MiningSchema miningSchema = new MiningSchema();

        Extension extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_RESULTANT_SCORE_CLASS );
        extension.setValue( model.getFactName() );

        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_IMPORTS );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );
        List<String> imports = new ArrayList<String>();
        StringBuilder importBuilder = new StringBuilder();
        for ( Import imp : model.getImports().getImports() ) {
            if ( !imports.contains( imp.getType() ) ) {
                imports.add( imp.getType() );
                importBuilder.append( imp.getType() ).append( "," );
            }
        }
        extension.setValue( importBuilder.toString() );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_RESULTANT_SCORE_FIELD );
        extension.setValue( model.getFieldName() );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_PACKAGE );
        String pkgName = model.getPackageName();
        extension.setValue( !( pkgName == null || pkgName.isEmpty() ) ? pkgName : null );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        final String modelName = convertToJavaIdentifier( model.getName() );
        pmmlScorecard.setModelName( modelName );
        pmmlScorecard.setInitialScore( model.getInitialScore() );
        pmmlScorecard.setUseReasonCodes( model.isUseReasonCodes() );

        if ( model.isUseReasonCodes() ) {
            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.CHARACTERTISTIC_EXTERNAL_CLASS );
            extension.setValue( characteristic.getFact() );
            _characteristic.getExtensions().add( extension );

            extension = new Extension();
            extension.setName( PMMLExtensionNames.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 );

            if ( model.isUseReasonCodes() ) {
                _characteristic.setBaselineScore( characteristic.getBaselineScore() );
                _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.CHARACTERTISTIC_EXTERNAL_CLASS );
            extension.setValue( characteristic.getFact() );
            miningField.getExtensions().add( extension );

            final String[] numericOperators = new String[]{ "=", ">", "<", ">=", "<=" };
            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( PMMLExtensionNames.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 ( ArrayUtils.contains( numericOperators, 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 );
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        final Scorecard pmmlScorecard = ScorecardPMMLUtils.createScorecard();
        final Output output = new Output();
        final Characteristics characteristics = new Characteristics();
        final MiningSchema miningSchema = new MiningSchema();

        Extension extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_RESULTANT_SCORE_CLASS );
        extension.setValue( model.getFactName() );

        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_IMPORTS );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );
        List<String> imports = new ArrayList<String>();
        imports.add( model.getFactName() );
        StringBuilder importBuilder = new StringBuilder();
        importBuilder.append( model.getFactName() );

        for ( final org.drools.guvnor.models.guided.scorecard.shared.Characteristic characteristic : model.getCharacteristics() ) {
            if ( !imports.contains( characteristic.getFact() ) ) {
                imports.add( characteristic.getFact() );
                importBuilder.append( "," ).append( characteristic.getFact() );
            }
        }
        imports.clear();
        extension.setValue( importBuilder.toString() );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_RESULTANT_SCORE_FIELD );
        extension.setValue( model.getFieldName() );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_PACKAGE );
        extension.setValue( model.getPackageName() );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        final String modelName = convertToJavaIdentifier( model.getName() );
        pmmlScorecard.setModelName( modelName );
        pmmlScorecard.setInitialScore( model.getInitialScore() );
        pmmlScorecard.setUseReasonCodes( model.isUseReasonCodes() );

        if ( model.isUseReasonCodes() ) {
            pmmlScorecard.setBaselineScore( model.getBaselineScore() );
            pmmlScorecard.setReasonCodeAlgorithm( model.getReasonCodesAlgorithm() );
        }

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

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

            extension = new Extension();
            extension.setName( PMMLExtensionNames.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 );

            if ( model.isUseReasonCodes() ) {
                _characteristic.setBaselineScore( characteristic.getBaselineScore() );
                _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.CHARACTERTISTIC_EXTERNAL_CLASS );
            extension.setValue( characteristic.getFact() );
            miningField.getExtensions().add( extension );

            final String[] numericOperators = new String[]{ "=", ">", "<", ">=", "<=" };
            for ( final org.drools.guvnor.models.guided.scorecard.shared.Attribute attribute : characteristic.getAttributes() ) {
                final Attribute _attribute = new Attribute();
                _characteristic.getAttributes().add( _attribute );

                extension = new Extension();
                extension.setName( PMMLExtensionNames.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 ( ArrayUtils.contains( numericOperators, 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 );
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            assertNotNull(drl);
            for (Object serializable : pmmlDocument.getAssociationModelsAndBaselineModelsAndClusteringModels()){
                if (serializable instanceof Scorecard){
                    Scorecard scorecard = (Scorecard)serializable;
                    assertEquals("Sample Score",scorecard.getModelName());
                    Extension extension = ScorecardPMMLUtils.getExtension(scorecard.getExtensionsAndCharacteristicsAndMiningSchemas(), ScorecardPMMLExtensionNames.SCORECARD_SCORING_STRATEGY);
                    assertNotNull(extension);
                    assertEquals( extension.getValue(), AggregationStrategy.AGGREGATE_SCORE.toString() );
                    return;
                }
            }
        }
        fail();
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                        assertNotNull(outputField);
                        assertEquals("totalScore", outputField.getName());
                        assertEquals("Final Score", outputField.getDisplayName());
                        assertEquals("double", outputField.getDataType().value());
                        assertEquals("predictedValue", outputField.getFeature().value());
                        final Extension extension = ScorecardPMMLUtils.getExtension(outputField.getExtensions(), PMMLExtensionNames.EXTERNAL_CLASS );
                        assertNotNull(extension);
                        assertEquals("org.drools.scorecards.example.Applicant",extension.getValue());
                        return;
                    }
                }
            }
        }
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        final Scorecard pmmlScorecard = ScorecardPMMLUtils.createScorecard();
        final Output output = new Output();
        final Characteristics characteristics = new Characteristics();
        final MiningSchema miningSchema = new MiningSchema();

        Extension extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_RESULTANT_SCORE_CLASS );
        extension.setValue( model.getFactName() );

        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_IMPORTS );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );
        List<String> imports = new ArrayList<String>();
        StringBuilder importBuilder = new StringBuilder();
        for ( Import imp : model.getImports().getImports() ) {
            if ( !imports.contains( imp.getType() ) ) {
                imports.add( imp.getType() );
                importBuilder.append( imp.getType() ).append( "," );
            }
        }
        extension.setValue( importBuilder.toString() );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_RESULTANT_SCORE_FIELD );
        extension.setValue( model.getFieldName() );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_PACKAGE );
        String pkgName = model.getPackageName();
        extension.setValue( !( pkgName == null || pkgName.isEmpty() ) ? pkgName : null );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        final String modelName = convertToJavaIdentifier( model.getName() );
        pmmlScorecard.setModelName( modelName );
        pmmlScorecard.setInitialScore( model.getInitialScore() );
        pmmlScorecard.setUseReasonCodes( model.isUseReasonCodes() );

        if ( model.isUseReasonCodes() ) {
            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.CHARACTERTISTIC_EXTERNAL_CLASS );
            extension.setValue( characteristic.getFact() );
            _characteristic.getExtensions().add( extension );

            extension = new Extension();
            extension.setName( PMMLExtensionNames.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 );

            if ( model.isUseReasonCodes() ) {
                _characteristic.setBaselineScore( characteristic.getBaselineScore() );
                _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.CHARACTERTISTIC_EXTERNAL_CLASS );
            extension.setValue( characteristic.getFact() );
            miningField.getExtensions().add( extension );

            final String[] numericOperators = new String[]{ "=", ">", "<", ">=", "<=" };
            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( PMMLExtensionNames.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 ( ArrayUtils.contains( numericOperators, 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 );
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        final Scorecard pmmlScorecard = ScorecardPMMLUtils.createScorecard();
        final Output output = new Output();
        final Characteristics characteristics = new Characteristics();
        final MiningSchema miningSchema = new MiningSchema();

        Extension extension = new Extension();
        extension.setName( PMMLExtensionNames.EXTERNAL_CLASS );
        extension.setValue( model.getFactName() );

        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.MODEL_IMPORTS );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );
        List<String> imports = new ArrayList<String>();
        StringBuilder importBuilder = new StringBuilder();
        for ( Import imp : model.getImports().getImports() ) {
            if ( !imports.contains( imp.getType() ) ) {
                imports.add( imp.getType() );
                importBuilder.append( imp.getType() ).append( "," );
            }
        }
        extension.setValue( importBuilder.toString() );

        extension = new Extension();
        extension.setName( ScorecardPMMLExtensionNames.SCORECARD_RESULTANT_SCORE_FIELD );
        extension.setValue( model.getFieldName() );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.MODEL_PACKAGE );
        String pkgName = model.getPackageName();
        extension.setValue( !( pkgName == null || pkgName.isEmpty() ) ? pkgName : null );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        final String modelName = convertToJavaIdentifier( model.getName() );
        pmmlScorecard.setModelName( modelName );
        pmmlScorecard.setInitialScore( model.getInitialScore() );
        pmmlScorecard.setUseReasonCodes( model.isUseReasonCodes() );

        if ( model.isUseReasonCodes() ) {
            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 );

            final String[] numericOperators = new String[]{ "=", ">", "<", ">=", "<=" };
            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 ( ArrayUtils.contains( numericOperators, 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 );
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                        assertNotNull(outputField);
                        assertEquals("totalScore", outputField.getName());
                        assertEquals("Final Score", outputField.getDisplayName());
                        assertEquals("double", outputField.getDataType().value());
                        assertEquals("predictedValue", outputField.getFeature().value());
                        final Extension extension = ScorecardPMMLUtils.getExtension(outputField.getExtensions(), PMMLExtensionNames.SCORECARD_RESULTANT_SCORE_CLASS);
                        assertNotNull(extension);
                        assertEquals("org.drools.scorecards.example.Applicant",extension.getValue());
                        return;
                    }
                }
            }
        }
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        final Scorecard pmmlScorecard = ScorecardPMMLUtils.createScorecard();
        final Output output = new Output();
        final Characteristics characteristics = new Characteristics();
        final MiningSchema miningSchema = new MiningSchema();

        Extension extension = new Extension();
        extension.setName( PMMLExtensionNames.EXTERNAL_CLASS );
        extension.setValue( model.getFactName() );

        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.MODEL_IMPORTS );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );
        List<String> imports = new ArrayList<String>();
        StringBuilder importBuilder = new StringBuilder();
        for ( Import imp : model.getImports().getImports() ) {
            if ( !imports.contains( imp.getType() ) ) {
                imports.add( imp.getType() );
                importBuilder.append( imp.getType() ).append( "," );
            }
        }
        extension.setValue( importBuilder.toString() );

        extension = new Extension();
        extension.setName( ScorecardPMMLExtensionNames.SCORECARD_RESULTANT_SCORE_FIELD );
        extension.setValue( model.getFieldName() );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.MODEL_PACKAGE );
        String pkgName = model.getPackageName();
        extension.setValue( !( pkgName == null || pkgName.isEmpty() ) ? pkgName : null );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        final String modelName = convertToJavaIdentifier( model.getName() );
        pmmlScorecard.setModelName( modelName );
        pmmlScorecard.setInitialScore( model.getInitialScore() );
        pmmlScorecard.setUseReasonCodes( model.isUseReasonCodes() );

        if ( model.isUseReasonCodes() ) {
            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 );

                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 );
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        final Scorecard pmmlScorecard = ScorecardPMMLUtils.createScorecard();
        final Output output = new Output();
        final Characteristics characteristics = new Characteristics();
        final MiningSchema miningSchema = new MiningSchema();

        Extension extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_RESULTANT_SCORE_CLASS );
        extension.setValue( model.getFactName() );

        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_IMPORTS );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );
        List<String> imports = new ArrayList<String>();
        imports.add( model.getFactName() );
        StringBuilder importBuilder = new StringBuilder();
        importBuilder.append( model.getFactName() );

        for ( final org.drools.workbench.models.guided.scorecard.shared.Characteristic characteristic : model.getCharacteristics() ) {
            if ( !imports.contains( characteristic.getFact() ) ) {
                imports.add( characteristic.getFact() );
                importBuilder.append( "," ).append( characteristic.getFact() );
            }
        }
        imports.clear();
        extension.setValue( importBuilder.toString() );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_RESULTANT_SCORE_FIELD );
        extension.setValue( model.getFieldName() );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        extension = new Extension();
        extension.setName( PMMLExtensionNames.SCORECARD_PACKAGE );
        extension.setValue( model.getPackageName() );
        pmmlScorecard.getExtensionsAndCharacteristicsAndMiningSchemas().add( extension );

        final String modelName = convertToJavaIdentifier( model.getName() );
        pmmlScorecard.setModelName( modelName );
        pmmlScorecard.setInitialScore( model.getInitialScore() );
        pmmlScorecard.setUseReasonCodes( model.isUseReasonCodes() );

        if ( model.isUseReasonCodes() ) {
            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.CHARACTERTISTIC_EXTERNAL_CLASS );
            extension.setValue( characteristic.getFact() );
            _characteristic.getExtensions().add( extension );

            extension = new Extension();
            extension.setName( PMMLExtensionNames.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 );

            if ( model.isUseReasonCodes() ) {
                _characteristic.setBaselineScore( characteristic.getBaselineScore() );
                _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.CHARACTERTISTIC_EXTERNAL_CLASS );
            extension.setValue( characteristic.getFact() );
            miningField.getExtensions().add( extension );

            final String[] numericOperators = new String[]{ "=", ">", "<", ">=", "<=" };
            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( PMMLExtensionNames.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 ( ArrayUtils.contains( numericOperators, 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 );
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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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TOP

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  • org.drools.pmml.pmml_4_2.PMMLGenerationTest
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