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

Example 6 with CategoricalLabel

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

the class ModelConverter method encodeSchema.

public Schema encodeSchema(SparkMLEncoder encoder) {
    T model = getTransformer();
    Label label = null;
    if (model instanceof HasLabelCol) {
        HasLabelCol hasLabelCol = (HasLabelCol) model;
        String labelCol = hasLabelCol.getLabelCol();
        Feature feature = encoder.getOnlyFeature(labelCol);
        MiningFunction miningFunction = getMiningFunction();
        switch(miningFunction) {
            case CLASSIFICATION:
                {
                    if (feature instanceof CategoricalFeature) {
                        CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
                        DataField dataField = encoder.getDataField(categoricalFeature.getName());
                        label = new CategoricalLabel(dataField);
                    } else if (feature instanceof ContinuousFeature) {
                        ContinuousFeature continuousFeature = (ContinuousFeature) feature;
                        int numClasses = 2;
                        if (model instanceof ClassificationModel) {
                            ClassificationModel<?, ?> classificationModel = (ClassificationModel<?, ?>) model;
                            numClasses = classificationModel.numClasses();
                        }
                        List<String> categories = new ArrayList<>();
                        for (int i = 0; i < numClasses; i++) {
                            categories.add(String.valueOf(i));
                        }
                        Field<?> field = encoder.toCategorical(continuousFeature.getName(), categories);
                        encoder.putOnlyFeature(labelCol, new CategoricalFeature(encoder, field, categories));
                        label = new CategoricalLabel(field.getName(), field.getDataType(), categories);
                    } else {
                        throw new IllegalArgumentException("Expected a categorical or categorical-like continuous feature, got " + feature);
                    }
                }
                break;
            case REGRESSION:
                {
                    Field<?> field = encoder.toContinuous(feature.getName());
                    field.setDataType(DataType.DOUBLE);
                    label = new ContinuousLabel(field.getName(), field.getDataType());
                }
                break;
            default:
                throw new IllegalArgumentException("Mining function " + miningFunction + " is not supported");
        }
    }
    if (model instanceof ClassificationModel) {
        ClassificationModel<?, ?> classificationModel = (ClassificationModel<?, ?>) model;
        CategoricalLabel categoricalLabel = (CategoricalLabel) label;
        int numClasses = classificationModel.numClasses();
        if (numClasses != categoricalLabel.size()) {
            throw new IllegalArgumentException("Expected " + numClasses + " target categories, got " + categoricalLabel.size() + " target categories");
        }
    }
    String featuresCol = model.getFeaturesCol();
    List<Feature> features = encoder.getFeatures(featuresCol);
    if (model instanceof PredictionModel) {
        PredictionModel<?, ?> predictionModel = (PredictionModel<?, ?>) model;
        int numFeatures = predictionModel.numFeatures();
        if (numFeatures != -1 && features.size() != numFeatures) {
            throw new IllegalArgumentException("Expected " + numFeatures + " features, got " + features.size() + " features");
        }
    }
    Schema result = new Schema(label, features);
    return result;
}

Example 7 with CategoricalLabel

use of org.jpmml.converter.CategoricalLabel 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 = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
    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(FieldName.create("gbmValue(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.DOUBLE));
        miningModels.add(miningModel);
    }
    return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}

Example 8 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 = (RDoubleVector) lrm.getValue("coefficients");
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    if (categoricalLabel.size() != 2) {
        throw new IllegalArgumentException();
    }
    String targetCategory = categoricalLabel.getValue(1);
    Double intercept = coefficients.getValue(getInterceptName(), true);
    List<? extends Feature> features = schema.getFeatures();
    if (coefficients.size() != (features.size() + (intercept != null ? 1 : 0))) {
        throw new IllegalArgumentException();
    }
    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, intercept, featureCoefficients, targetCategory);
    return generalRegressionModel;
}

Example 9 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();
    if (categoricalLabel.size() != probabilities.size()) {
        throw new IllegalArgumentException();
    }
    Double maxProbability = null;
    for (int i = 0; i < categoricalLabel.size(); i++) {
        String 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);
        node.addScoreDistributions(scoreDistribution);
    }
    return node;
}

Example 10 with CategoricalLabel

use of org.jpmml.converter.CategoricalLabel 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;
}