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

Example 41 with TreeModel

use of org.dmg.pmml.tree.TreeModel 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 42 with TreeModel

use of org.dmg.pmml.tree.TreeModel 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 43 with TreeModel

use of org.dmg.pmml.tree.TreeModel 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;
}

Example 44 with TreeModel

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

the class RangerConverter method encodeRegression.

private MiningModel encodeRegression(RGenericVector forest, Schema schema) {
    ScoreEncoder scoreEncoder = new ScoreEncoder() {

        @Override
        public Node encode(Node node, Number splitValue, RNumberVector<?> terminalClassCount) {
            node.setScore(splitValue);
            return node;
        }
    };
    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 45 with TreeModel

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

the class GBMConverter method encodeMultinomialClassification.

private MiningModel encodeMultinomialClassification(List<TreeModel> treeModels, Double initF, Schema schema) {
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.DOUBLE);
    List<Model> miningModels = new ArrayList<>();
    for (int i = 0, columns = categoricalLabel.size(), rows = (treeModels.size() / columns); i < columns; i++) {
        MiningModel miningModel = createMiningModel(CMatrixUtil.getColumn(treeModels, rows, columns, i), initF, segmentSchema).setOutput(ModelUtil.createPredictedOutput(FieldNameUtil.create("gbmValue", categoricalLabel.getValue(i)), OpType.CONTINUOUS, DataType.DOUBLE));
        miningModels.add(miningModel);
    }
    return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}