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

Example 31 with Node

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

the class RPartConverter method encodeClassification.

private TreeModel encodeClassification(RGenericVector frame, RIntegerVector rowNames, RVector<?> var, RIntegerVector n, int[][] splitInfo, RNumberVector<?> splits, RIntegerVector csplit, Schema schema) {
    RDoubleVector yval2 = frame.getDoubleElement("yval2");
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    List<?> categories = categoricalLabel.getValues();
    boolean hasScoreDistribution = hasScoreDistribution();
    ScoreEncoder scoreEncoder = new ScoreEncoder() {

        private List<Integer> classes = null;

        private List<List<? extends Number>> recordCounts = null;

        {
            int rows = rowNames.size();
            int columns = 1 + (2 * categories.size()) + 1;
            List<Integer> classes = ValueUtil.asIntegers(FortranMatrixUtil.getColumn(yval2.getValues(), rows, columns, 0));
            this.classes = new ArrayList<>(classes);
            if (hasScoreDistribution) {
                this.recordCounts = new ArrayList<>();
                for (int i = 0; i < categories.size(); i++) {
                    List<? extends Number> recordCounts = FortranMatrixUtil.getColumn(yval2.getValues(), rows, columns, 1 + i);
                    this.recordCounts.add(new ArrayList<>(recordCounts));
                }
            }
        }

        @Override
        public Node encode(Node node, int offset) {
            Object score = categories.get(this.classes.get(offset) - 1);
            Integer recordCount = n.getValue(offset);
            node.setScore(score).setRecordCount(recordCount);
            if (hasScoreDistribution) {
                node = new ClassifierNode(node);
                List<ScoreDistribution> scoreDistributions = node.getScoreDistributions();
                for (int i = 0; i < categories.size(); i++) {
                    List<? extends Number> recordCounts = this.recordCounts.get(i);
                    ScoreDistribution scoreDistribution = new ScoreDistribution().setValue(categories.get(i)).setRecordCount(recordCounts.get(offset));
                    scoreDistributions.add(scoreDistribution);
                }
            }
            return node;
        }
    };
    Node root = encodeNode(True.INSTANCE, 1, rowNames, var, n, splitInfo, splits, csplit, scoreEncoder, schema);
    TreeModel treeModel = new TreeModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(schema.getLabel()), root);
    if (hasScoreDistribution) {
        treeModel.setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));
    }
    return configureTreeModel(treeModel);
}

Example 32 with Node

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

the class RPartConverter method encodeRegression.

private TreeModel encodeRegression(RGenericVector frame, RIntegerVector rowNames, RVector<?> var, RIntegerVector n, int[][] splitInfo, RNumberVector<?> splits, RIntegerVector csplit, Schema schema) {
    RNumberVector<?> yval = frame.getNumericElement("yval");
    ScoreEncoder scoreEncoder = new ScoreEncoder() {

        @Override
        public Node encode(Node node, int offset) {
            Number score = yval.getValue(offset);
            Number recordCount = n.getValue(offset);
            node.setScore(score).setRecordCount(recordCount);
            return node;
        }
    };
    Node root = encodeNode(True.INSTANCE, 1, rowNames, var, n, splitInfo, splits, csplit, scoreEncoder, schema);
    TreeModel treeModel = new TreeModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()), root);
    return configureTreeModel(treeModel);
}

Example 33 with Node

use of org.dmg.pmml.tree.Node 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 34 with Node

use of org.dmg.pmml.tree.Node 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 35 with Node

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

the class RangerConverter method encodeProbabilityForest.

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

        @Override
        public Node encode(Node node, Number splitValue, RNumberVector<?> terminalClassCount) {
            if (splitValue.doubleValue() != 0d || (terminalClassCount == null || terminalClassCount.size() != levels.size())) {
                throw new IllegalArgumentException();
            }
            node = new ClassifierNode(node);
            List<ScoreDistribution> scoreDistributions = node.getScoreDistributions();
            Number maxProbability = null;
            for (int i = 0; i < terminalClassCount.size(); i++) {
                String value = levels.getValue(i);
                Number probability = terminalClassCount.getValue(i);
                if (maxProbability == null || ((Comparable) maxProbability).compareTo(probability) < 0) {
                    node.setScore(value);
                    maxProbability = probability;
                }
                ScoreDistribution scoreDistribution = new ScoreDistribution(value, probability);
                scoreDistributions.add(scoreDistribution);
            }
            return node;
        }
    };
    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;
}