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

Example 6 with LeafNode

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

the class KiePMMLTreeModelNodeASTFactoryTest method isFinalLeaf.

@Test
public void isFinalLeaf() {
    Node node = new LeafNode();
    DATA_TYPE targetType = DATA_TYPE.STRING;
    KiePMMLTreeModelNodeASTFactory.factory(new HashMap<>(), Collections.emptyList(), TreeModel.NoTrueChildStrategy.RETURN_NULL_PREDICTION, targetType).isFinalLeaf(node);
    assertTrue(KiePMMLTreeModelNodeASTFactory.factory(new HashMap<>(), Collections.emptyList(), TreeModel.NoTrueChildStrategy.RETURN_NULL_PREDICTION, targetType).isFinalLeaf(node));
    node = new ClassifierNode();
    assertTrue(KiePMMLTreeModelNodeASTFactory.factory(new HashMap<>(), Collections.emptyList(), TreeModel.NoTrueChildStrategy.RETURN_NULL_PREDICTION, targetType).isFinalLeaf(node));
    node.addNodes(new LeafNode());
    assertFalse(KiePMMLTreeModelNodeASTFactory.factory(new HashMap<>(), Collections.emptyList(), TreeModel.NoTrueChildStrategy.RETURN_NULL_PREDICTION, targetType).isFinalLeaf(node));
}

Example 7 with LeafNode

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

the class RandomForestConverter method encodeNode.

private <P extends Number> Node encodeNode(Predicate predicate, int i, ScoreEncoder<P> scoreEncoder, List<? extends Number> leftDaughter, List<? extends Number> rightDaughter, List<? extends Number> bestvar, List<Double> xbestsplit, List<P> nodepred, CategoryManager categoryManager, Schema schema) {
    Integer id = Integer.valueOf(i + 1);
    int var = ValueUtil.asInt(bestvar.get(i));
    if (var == 0) {
        P prediction = nodepred.get(i);
        Node result = new LeafNode(scoreEncoder.encode(prediction), predicate).setId(id);
        return result;
    }
    CategoryManager leftCategoryManager = categoryManager;
    CategoryManager rightCategoryManager = categoryManager;
    Predicate leftPredicate;
    Predicate rightPredicate;
    Feature feature = schema.getFeature(var - 1);
    Double split = xbestsplit.get(i);
    if (feature instanceof BooleanFeature) {
        BooleanFeature booleanFeature = (BooleanFeature) feature;
        if (split != 0.5d) {
            throw new IllegalArgumentException();
        }
        leftPredicate = createSimplePredicate(booleanFeature, SimplePredicate.Operator.EQUAL, booleanFeature.getValue(0));
        rightPredicate = createSimplePredicate(booleanFeature, SimplePredicate.Operator.EQUAL, booleanFeature.getValue(1));
    } else if (feature instanceof CategoricalFeature) {
        CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
        String name = categoricalFeature.getName();
        List<?> values = categoricalFeature.getValues();
        java.util.function.Predicate<Object> valueFilter = categoryManager.getValueFilter(name);
        List<Object> leftValues = selectValues(values, valueFilter, split, true);
        List<Object> rightValues = selectValues(values, valueFilter, split, false);
        leftCategoryManager = categoryManager.fork(name, leftValues);
        rightCategoryManager = categoryManager.fork(name, rightValues);
        leftPredicate = createPredicate(categoricalFeature, leftValues);
        rightPredicate = createPredicate(categoricalFeature, rightValues);
    } else {
        ContinuousFeature continuousFeature = feature.toContinuousFeature();
        leftPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.LESS_OR_EQUAL, split);
        rightPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.GREATER_THAN, split);
    }
    Node result = new BranchNode(null, predicate).setId(id);
    List<Node> nodes = result.getNodes();
    int left = ValueUtil.asInt(leftDaughter.get(i));
    if (left != 0) {
        Node leftChild = encodeNode(leftPredicate, left - 1, scoreEncoder, leftDaughter, rightDaughter, bestvar, xbestsplit, nodepred, leftCategoryManager, schema);
        nodes.add(leftChild);
    }
    int right = ValueUtil.asInt(rightDaughter.get(i));
    if (right != 0) {
        Node rightChild = encodeNode(rightPredicate, right - 1, scoreEncoder, leftDaughter, rightDaughter, bestvar, xbestsplit, nodepred, rightCategoryManager, schema);
        nodes.add(rightChild);
    }
    return result;
}

Example 8 with LeafNode

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

the class BinaryTreeConverter method encodeNode.

private Node encodeNode(RGenericVector tree, Predicate predicate, Schema schema) {
    RIntegerVector nodeId = tree.getIntegerElement("nodeID");
    RBooleanVector terminal = tree.getBooleanElement("terminal");
    RGenericVector psplit = tree.getGenericElement("psplit");
    RGenericVector ssplits = tree.getGenericElement("ssplits");
    RDoubleVector prediction = tree.getDoubleElement("prediction");
    RGenericVector left = tree.getGenericElement("left");
    RGenericVector right = tree.getGenericElement("right");
    Integer id = nodeId.asScalar();
    if ((Boolean.TRUE).equals(terminal.asScalar())) {
        Node result = new LeafNode(null, predicate).setId(id);
        return encodeScore(result, prediction, schema);
    }
    RNumberVector<?> splitpoint = psplit.getNumericElement("splitpoint");
    RStringVector variableName = psplit.getStringElement("variableName");
    if (ssplits.size() > 0) {
        throw new IllegalArgumentException();
    }
    Predicate leftPredicate;
    Predicate rightPredicate;
    String name = variableName.asScalar();
    Integer index = this.featureIndexes.get(name);
    if (index == null) {
        throw new IllegalArgumentException();
    }
    Feature feature = schema.getFeature(index);
    if (feature instanceof CategoricalFeature) {
        CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
        List<?> values = categoricalFeature.getValues();
        List<Integer> splitValues = (List<Integer>) splitpoint.getValues();
        leftPredicate = createPredicate(categoricalFeature, selectValues(values, splitValues, true));
        rightPredicate = createPredicate(categoricalFeature, selectValues(values, splitValues, false));
    } else {
        ContinuousFeature continuousFeature = feature.toContinuousFeature();
        Number value = splitpoint.asScalar();
        leftPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.LESS_OR_EQUAL, value);
        rightPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.GREATER_THAN, value);
    }
    Node leftChild = encodeNode(left, leftPredicate, schema);
    Node rightChild = encodeNode(right, rightPredicate, schema);
    Node result = new BranchNode(null, predicate).setId(id).addNodes(leftChild, rightChild);
    return result;
}

Example 9 with LeafNode

use of org.dmg.pmml.tree.LeafNode in project jpmml-sparkml by jpmml.

the class TreeModelUtil method encodeDecisionTree.

private static <M extends Model<M> & DecisionTreeModel> TreeModel encodeDecisionTree(ModelConverter<?> converter, M model, PredicateManager predicateManager, ScoreDistributionManager scoreDistributionManager, Schema schema) {
    TreeModel treeModel;
    if (model instanceof DecisionTreeRegressionModel) {
        ScoreEncoder scoreEncoder = new ScoreEncoder() {

            @Override
            public Node encode(Node node, org.apache.spark.ml.tree.LeafNode leafNode) {
                node.setScore(leafNode.prediction());
                return node;
            }
        };
        treeModel = encodeTreeModel(MiningFunction.REGRESSION, scoreEncoder, model, predicateManager, schema);
    } else if (model instanceof DecisionTreeClassificationModel) {
        ScoreEncoder scoreEncoder = new ScoreEncoder() {

            private CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();

            @Override
            public Node encode(Node node, org.apache.spark.ml.tree.LeafNode leafNode) {
                node = new ClassifierNode(null, node.getPredicate());
                int index = ValueUtil.asInt(leafNode.prediction());
                node.setScore(this.categoricalLabel.getValue(index));
                ImpurityCalculator impurityCalculator = leafNode.impurityStats();
                node.setRecordCount(ValueUtil.narrow(impurityCalculator.count()));
                scoreDistributionManager.addScoreDistributions(node, this.categoricalLabel.getValues(), impurityCalculator.stats());
                return node;
            }
        };
        treeModel = encodeTreeModel(MiningFunction.CLASSIFICATION, scoreEncoder, model, predicateManager, schema);
    } else {
        throw new IllegalArgumentException();
    }
    Boolean compact = (Boolean) converter.getOption(HasTreeOptions.OPTION_COMPACT, Boolean.TRUE);
    if (compact != null && compact) {
        Visitor visitor = new TreeModelCompactor();
        visitor.applyTo(treeModel);
    }
    return treeModel;
}