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

Example 11 with Node

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

the class GBMConverter method encodeNode.

private void encodeNode(Node node, int i, RGenericVector tree, RGenericVector c_splits, Schema schema) {
    RIntegerVector splitVar = (RIntegerVector) tree.getValue(0);
    RDoubleVector splitCodePred = (RDoubleVector) tree.getValue(1);
    RIntegerVector leftNode = (RIntegerVector) tree.getValue(2);
    RIntegerVector rightNode = (RIntegerVector) tree.getValue(3);
    RIntegerVector missingNode = (RIntegerVector) tree.getValue(4);
    RDoubleVector prediction = (RDoubleVector) tree.getValue(7);
    Predicate missingPredicate;
    Predicate leftPredicate;
    Predicate rightPredicate;
    Integer var = splitVar.getValue(i);
    if (var != -1) {
        Feature feature = schema.getFeature(var);
        missingPredicate = createSimplePredicate(feature, SimplePredicate.Operator.IS_MISSING, null);
        Double split = splitCodePred.getValue(i);
        if (feature instanceof CategoricalFeature) {
            CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
            List<String> values = categoricalFeature.getValues();
            int index = ValueUtil.asInt(split);
            RIntegerVector c_split = (RIntegerVector) c_splits.getValue(index);
            List<Integer> splitValues = c_split.getValues();
            leftPredicate = createSimpleSetPredicate(categoricalFeature, selectValues(values, splitValues, true));
            rightPredicate = createSimpleSetPredicate(categoricalFeature, selectValues(values, splitValues, false));
        } else {
            ContinuousFeature continuousFeature = feature.toContinuousFeature();
            String value = ValueUtil.formatValue(split);
            leftPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.LESS_THAN, value);
            rightPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.GREATER_OR_EQUAL, value);
        }
    } else {
        Double value = prediction.getValue(i);
        node.setScore(ValueUtil.formatValue(value));
        return;
    }
    Integer missing = missingNode.getValue(i);
    if (missing != -1) {
        Node missingChild = new Node().setId(String.valueOf(missing + 1)).setPredicate(missingPredicate);
        encodeNode(missingChild, missing, tree, c_splits, schema);
        node.addNodes(missingChild);
    }
    Integer left = leftNode.getValue(i);
    if (left != -1) {
        Node leftChild = new Node().setId(String.valueOf(left + 1)).setPredicate(leftPredicate);
        encodeNode(leftChild, left, tree, c_splits, schema);
        node.addNodes(leftChild);
    }
    Integer right = rightNode.getValue(i);
    if (right != -1) {
        Node rightChild = new Node().setId(String.valueOf(right + 1)).setPredicate(rightPredicate);
        encodeNode(rightChild, right, tree, c_splits, schema);
        node.addNodes(rightChild);
    }
}

Example 12 with Node

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

the class BinaryTreeConverter method encodeTreeModel.

private TreeModel encodeTreeModel(RGenericVector tree, Schema schema) {
    Node root = new Node().setPredicate(new True());
    encodeNode(root, tree, schema);
    TreeModel treeModel = new TreeModel(this.miningFunction, ModelUtil.createMiningSchema(schema.getLabel()), root).setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT);
    return treeModel;
}

Example 13 with Node

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

the class BinaryTreeConverter method encodeNode.

private void encodeNode(Node node, RGenericVector tree, Schema schema) {
    RIntegerVector nodeId = (RIntegerVector) tree.getValue("nodeID");
    RBooleanVector terminal = (RBooleanVector) tree.getValue("terminal");
    RGenericVector psplit = (RGenericVector) tree.getValue("psplit");
    RGenericVector ssplits = (RGenericVector) tree.getValue("ssplits");
    RDoubleVector prediction = (RDoubleVector) tree.getValue("prediction");
    RGenericVector left = (RGenericVector) tree.getValue("left");
    RGenericVector right = (RGenericVector) tree.getValue("right");
    node.setId(String.valueOf(nodeId.asScalar()));
    if ((Boolean.TRUE).equals(terminal.asScalar())) {
        node = encodeScore(node, prediction, schema);
        return;
    }
    RNumberVector<?> splitpoint = (RNumberVector<?>) psplit.getValue("splitpoint");
    RStringVector variableName = (RStringVector) psplit.getValue("variableName");
    if (ssplits.size() > 0) {
        throw new IllegalArgumentException();
    }
    Predicate leftPredicate;
    Predicate rightPredicate;
    FieldName name = FieldName.create(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<String> values = categoricalFeature.getValues();
        List<Integer> splitValues = (List<Integer>) splitpoint.getValues();
        leftPredicate = createSimpleSetPredicate(categoricalFeature, selectValues(values, splitValues, true));
        rightPredicate = createSimpleSetPredicate(categoricalFeature, selectValues(values, splitValues, false));
    } else {
        ContinuousFeature continuousFeature = feature.toContinuousFeature();
        String value = ValueUtil.formatValue((Double) splitpoint.asScalar());
        leftPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.LESS_OR_EQUAL, value);
        rightPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.GREATER_THAN, value);
    }
    Node leftChild = new Node().setPredicate(leftPredicate);
    encodeNode(leftChild, left, schema);
    Node rightChild = new Node().setPredicate(rightPredicate);
    encodeNode(rightChild, right, schema);
    node.addNodes(leftChild, rightChild);
}

Example 14 with Node

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

the class RangerConverter method encodeRegression.

private MiningModel encodeRegression(RGenericVector ranger, Schema schema) {
    RGenericVector forest = (RGenericVector) ranger.getValue("forest");
    ScoreEncoder scoreEncoder = new ScoreEncoder() {

        @Override
        public void encode(Node node, Number splitValue, RNumberVector<?> terminalClassCount) {
            node.setScore(ValueUtil.formatValue(splitValue));
        }
    };
    List<TreeModel> treeModels = encodeForest(forest, MiningFunction.REGRESSION, scoreEncoder, schema);
    MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel())).setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, treeModels));
    return miningModel;
}

Example 15 with Node

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

the class RangerConverter method encodeProbabilityForest.

private MiningModel encodeProbabilityForest(RGenericVector ranger, Schema schema) {
    RGenericVector forest = (RGenericVector) ranger.getValue("forest");
    final RStringVector levels = (RStringVector) forest.getValue("levels");
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    ScoreEncoder scoreEncoder = new ScoreEncoder() {

        @Override
        public void encode(Node node, Number splitValue, RNumberVector<?> terminalClassCount) {
            if (splitValue.doubleValue() != 0d || (terminalClassCount == null || terminalClassCount.size() != levels.size())) {
                throw new IllegalArgumentException();
            }
            Double maxProbability = null;
            for (int i = 0; i < terminalClassCount.size(); i++) {
                String value = levels.getValue(i);
                Double probability = ValueUtil.asDouble(terminalClassCount.getValue(i));
                if (maxProbability == null || (maxProbability).compareTo(probability) < 0) {
                    node.setScore(value);
                    maxProbability = probability;
                }
                ScoreDistribution scoreDisctibution = new ScoreDistribution(value, probability);
                node.addScoreDistributions(scoreDisctibution);
            }
        }
    };
    List<TreeModel> treeModels = encodeForest(forest, MiningFunction.CLASSIFICATION, scoreEncoder, schema);
    MiningModel miningModel = new MiningModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel)).setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, treeModels)).setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));
    return miningModel;
}