Package org.dmg.pmml.pmml_4_2.descr

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

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    @Test
    public void testMissingTreeNull() throws Exception {
        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 );
            }
        }
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    @Test
    public void testMissingAggregate() throws Exception {
        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 );
            }
        }
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    @Test
    public void testMissingTreeNone() throws Exception {
        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 );
            }
        }
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    private static String modelName = "SmartVent";


    @Test
    public void testNNGenration() {
        PMML net = PMMLGeneratorUtils.generateSimpleNeuralNetwork(modelName,
                inputfieldNames, outputfieldNames,
                inputMeans, inputStds,
                outputMeans, outputStds,
                hiddenSize,
                weights);
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    private static final List<String> NUMERIC_OPERATORS = Arrays.asList("=", ">", "<", ">=", "<=");


    public static String marshal( final ScoreCardModel model ) {
        final PMML pmml = createPMMLDocument( model );

        final StringBuilder sb = new StringBuilder();

        //Package statement and Imports are appended by org.drools.scorecards.drl.AbstractDRLEmitter
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    @Before
    public void setUp() throws Exception {
        ScorecardCompiler scorecardCompiler = new ScorecardCompiler(INTERNAL_DECLARED_TYPES);
        if (scorecardCompiler.compileFromExcel(PMMLDocumentTest.class.getResourceAsStream("/scoremodel_c.xls")) ) {
            PMML pmmlDocument = scorecardCompiler.getPMMLDocument();
            assertNotNull(pmmlDocument);
            drl = scorecardCompiler.getDRL();
        } else {
            fail("failed to parse scoremodel Excel.");
        }
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import org.drools.scorecards.pmml.ScorecardPMMLUtils;

public class GuidedScoreCardDRLPersistence {

    public static String marshal( final ScoreCardModel model ) {
        final PMML pmml = createPMMLDocument( model );

        final StringBuilder sb = new StringBuilder();

        //Append package name and imports to DRL
        PackageNameWriter.write( sb,
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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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Related Classes of org.dmg.pmml.pmml_4_2.descr.PMML

  • org.drools.guvnor.models.guided.scorecard.backend.GuidedScoreCardDRLPersistence
  • org.drools.pmml.pmml_4_1.PMML4Compiler
  • org.drools.pmml.pmml_4_1.PMMLGenerationTest
  • 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
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