Examples with CategoricalLabel org.jpmml.converter.CategoricalLabel used on opensource projects

Example 26 with CategoricalLabel

use of org.jpmml.converter.CategoricalLabel in project jpmml-r by jpmml.

the class LRMConverter method encodeModel.

@Override
public Model encodeModel(Schema schema) {
    RGenericVector lrm = getObject();
    RDoubleVector coefficients = lrm.getDoubleElement("coefficients");
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    SchemaUtil.checkSize(2, categoricalLabel);
    Object targetCategory = categoricalLabel.getValue(1);
    Double intercept = coefficients.getElement(getInterceptName(), false);
    List<? extends Feature> features = schema.getFeatures();
    SchemaUtil.checkSize(coefficients.size() - (intercept != null ? 1 : 0), features);
    List<Double> featureCoefficients = getFeatureCoefficients(features, coefficients);
    GeneralRegressionModel generalRegressionModel = new GeneralRegressionModel(GeneralRegressionModel.ModelType.GENERALIZED_LINEAR, MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), null, null, null).setLinkFunction(GeneralRegressionModel.LinkFunction.LOGIT).setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));
    GeneralRegressionModelUtil.encodeRegressionTable(generalRegressionModel, features, featureCoefficients, intercept, targetCategory);
    return generalRegressionModel;
}

Example 27 with CategoricalLabel

use of org.jpmml.converter.CategoricalLabel in project jpmml-r by jpmml.

the class BoostingConverter method encodeModel.

@Override
public Model encodeModel(Schema schema) {
    RGenericVector boosting = getObject();
    RGenericVector trees = boosting.getGenericElement("trees");
    RDoubleVector weights = boosting.getDoubleElement("weights");
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    List<TreeModel> treeModels = encodeTreeModels(trees);
    MiningModel miningModel = new MiningModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel)).setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.WEIGHTED_MAJORITY_VOTE, treeModels, weights.getValues())).setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));
    return miningModel;
}

Example 28 with CategoricalLabel

use of org.jpmml.converter.CategoricalLabel in project jpmml-r by jpmml.

the class BinaryTreeConverter method encodeClassificationScore.

private static Node encodeClassificationScore(Node node, RDoubleVector probabilities, Schema schema) {
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    SchemaUtil.checkSize(probabilities.size(), categoricalLabel);
    node = new ClassifierNode(node);
    List<ScoreDistribution> scoreDistributions = node.getScoreDistributions();
    Double maxProbability = null;
    for (int i = 0; i < categoricalLabel.size(); i++) {
        Object value = categoricalLabel.getValue(i);
        Double probability = probabilities.getValue(i);
        if (maxProbability == null || (maxProbability).compareTo(probability) < 0) {
            node.setScore(value);
            maxProbability = probability;
        }
        ScoreDistribution scoreDistribution = new ScoreDistribution(value, probability);
        scoreDistributions.add(scoreDistribution);
    }
    return node;
}