Examples with TreeModel org.dmg.pmml.tree.TreeModel used on opensource projects

Example 36 with TreeModel

use of org.dmg.pmml.tree.TreeModel in project drools by kiegroup.

the class KiePMMLTreeModelFactoryTest method getKiePMMLTreeModel.

@Test
public void getKiePMMLTreeModel() throws InstantiationException, IllegalAccessException {
    final Map<String, KiePMMLOriginalTypeGeneratedType> fieldTypeMap = getFieldTypeMap(pmml.getDataDictionary(), pmml.getTransformationDictionary(), treeModel.getLocalTransformations());
    KnowledgeBuilderImpl knowledgeBuilder = new KnowledgeBuilderImpl();
    final CommonCompilationDTO<TreeModel> compilationDTO = CommonCompilationDTO.fromGeneratedPackageNameAndFields(PACKAGE_NAME, pmml, treeModel, new HasKnowledgeBuilderMock(knowledgeBuilder));
    final DroolsCompilationDTO<TreeModel> droolsCompilationDTO = DroolsCompilationDTO.fromCompilationDTO(compilationDTO, fieldTypeMap);
    KiePMMLTreeModel retrieved = KiePMMLTreeModelFactory.getKiePMMLTreeModel(droolsCompilationDTO);
    assertNotNull(retrieved);
    assertEquals(treeModel.getModelName(), retrieved.getName());
    assertEquals(TARGET_FIELD, retrieved.getTargetField());
}

Example 37 with TreeModel

use of org.dmg.pmml.tree.TreeModel in project drools by kiegroup.

the class KiePMMLTreeModelFactoryTest method getKiePMMLScorecardModelSourcesMap.

@Test
public void getKiePMMLScorecardModelSourcesMap() {
    final Map<String, KiePMMLOriginalTypeGeneratedType> fieldTypeMap = getFieldTypeMap(pmml.getDataDictionary(), pmml.getTransformationDictionary(), treeModel.getLocalTransformations());
    KnowledgeBuilderImpl knowledgeBuilder = new KnowledgeBuilderImpl();
    final CommonCompilationDTO<TreeModel> compilationDTO = CommonCompilationDTO.fromGeneratedPackageNameAndFields(PACKAGE_NAME, pmml, treeModel, new HasKnowledgeBuilderMock(knowledgeBuilder));
    final DroolsCompilationDTO<TreeModel> droolsCompilationDTO = DroolsCompilationDTO.fromCompilationDTO(compilationDTO, fieldTypeMap);
    Map<String, String> retrieved = KiePMMLTreeModelFactory.getKiePMMLTreeModelSourcesMap(droolsCompilationDTO);
    assertNotNull(retrieved);
    assertEquals(1, retrieved.size());
}

Example 38 with TreeModel

use of org.dmg.pmml.tree.TreeModel in project jpmml-r by jpmml.

the class RandomForestConverter method encodeTreeModel.

private <P extends Number> TreeModel encodeTreeModel(MiningFunction miningFunction, ScoreEncoder<P> scoreEncoder, List<? extends Number> leftDaughter, List<? extends Number> rightDaughter, List<P> nodepred, List<? extends Number> bestvar, List<Double> xbestsplit, Schema schema) {
    RGenericVector randomForest = getObject();
    Node root = encodeNode(True.INSTANCE, 0, scoreEncoder, leftDaughter, rightDaughter, bestvar, xbestsplit, nodepred, new CategoryManager(), schema);
    TreeModel treeModel = new TreeModel(miningFunction, ModelUtil.createMiningSchema(schema.getLabel()), root).setMissingValueStrategy(TreeModel.MissingValueStrategy.NULL_PREDICTION).setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT);
    if (this.compact) {
        Visitor visitor = new RandomForestCompactor();
        visitor.applyTo(treeModel);
    }
    return treeModel;
}

Example 39 with TreeModel

use of org.dmg.pmml.tree.TreeModel in project jpmml-r by jpmml.

the class RandomForestConverter method encodeRegression.

private MiningModel encodeRegression(RGenericVector forest, Schema schema) {
    RNumberVector<?> leftDaughter = forest.getNumericElement("leftDaughter");
    RNumberVector<?> rightDaughter = forest.getNumericElement("rightDaughter");
    RDoubleVector nodepred = forest.getDoubleElement("nodepred");
    RNumberVector<?> bestvar = forest.getNumericElement("bestvar");
    RDoubleVector xbestsplit = forest.getDoubleElement("xbestsplit");
    RIntegerVector nrnodes = forest.getIntegerElement("nrnodes");
    RNumberVector<?> ntree = forest.getNumericElement("ntree");
    ScoreEncoder<Double> scoreEncoder = new ScoreEncoder<Double>() {

        @Override
        public Double encode(Double value) {
            return value;
        }
    };
    int rows = nrnodes.asScalar();
    int columns = ValueUtil.asInt(ntree.asScalar());
    Schema segmentSchema = schema.toAnonymousSchema();
    List<TreeModel> treeModels = new ArrayList<>();
    for (int i = 0; i < columns; i++) {
        TreeModel treeModel = encodeTreeModel(MiningFunction.REGRESSION, scoreEncoder, FortranMatrixUtil.getColumn(leftDaughter.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(rightDaughter.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(nodepred.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(bestvar.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(xbestsplit.getValues(), rows, columns, i), segmentSchema);
        treeModels.add(treeModel);
    }
    MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel())).setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, treeModels));
    return miningModel;
}

Example 40 with TreeModel

use of org.dmg.pmml.tree.TreeModel in project jpmml-r by jpmml.

the class RandomForestConverter method encodeClassification.

private MiningModel encodeClassification(RGenericVector forest, Schema schema) {
    RNumberVector<?> bestvar = forest.getNumericElement("bestvar");
    RNumberVector<?> treemap = forest.getNumericElement("treemap");
    RIntegerVector nodepred = forest.getIntegerElement("nodepred");
    RDoubleVector xbestsplit = forest.getDoubleElement("xbestsplit");
    RIntegerVector nrnodes = forest.getIntegerElement("nrnodes");
    RDoubleVector ntree = forest.getDoubleElement("ntree");
    int rows = nrnodes.asScalar();
    int columns = ValueUtil.asInt(ntree.asScalar());
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    ScoreEncoder<Integer> scoreEncoder = new ScoreEncoder<Integer>() {

        @Override
        public Object encode(Integer value) {
            return categoricalLabel.getValue(value - 1);
        }
    };
    Schema segmentSchema = schema.toAnonymousSchema();
    List<TreeModel> treeModels = new ArrayList<>();
    for (int i = 0; i < columns; i++) {
        List<? extends Number> daughters = FortranMatrixUtil.getColumn(treemap.getValues(), 2 * rows, columns, i);
        TreeModel treeModel = encodeTreeModel(MiningFunction.CLASSIFICATION, scoreEncoder, FortranMatrixUtil.getColumn(daughters, rows, 2, 0), FortranMatrixUtil.getColumn(daughters, rows, 2, 1), FortranMatrixUtil.getColumn(nodepred.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(bestvar.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(xbestsplit.getValues(), rows, columns, i), segmentSchema);
        treeModels.add(treeModel);
    }
    MiningModel miningModel = new MiningModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel)).setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.MAJORITY_VOTE, treeModels)).setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));
    return miningModel;
}