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

Example 11 with CategoricalLabel

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

the class RandomForestConverter method encodeClassification.

private MiningModel encodeClassification(RGenericVector forest, final Schema schema) {
    RNumberVector<?> bestvar = (RNumberVector<?>) forest.getValue("bestvar");
    RNumberVector<?> treemap = (RNumberVector<?>) forest.getValue("treemap");
    RIntegerVector nodepred = (RIntegerVector) forest.getValue("nodepred");
    RDoubleVector xbestsplit = (RDoubleVector) forest.getValue("xbestsplit");
    RIntegerVector nrnodes = (RIntegerVector) forest.getValue("nrnodes");
    RDoubleVector ntree = (RDoubleVector) forest.getValue("ntree");
    int rows = nrnodes.asScalar();
    int columns = ValueUtil.asInt(ntree.asScalar());
    final CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    ScoreEncoder<Integer> scoreEncoder = new ScoreEncoder<Integer>() {

        @Override
        public String encode(Integer value) {
            return categoricalLabel.getValue(value - 1);
        }
    };
    Schema segmentSchema = schema.toAnonymousSchema();
    List<TreeModel> treeModels = new ArrayList<>();
    for (int i = 0; i < columns; i++) {
        List<? extends Number> daughters = FortranMatrixUtil.getColumn(treemap.getValues(), 2 * rows, columns, i);
        TreeModel treeModel = encodeTreeModel(MiningFunction.CLASSIFICATION, scoreEncoder, FortranMatrixUtil.getColumn(daughters, rows, 2, 0), FortranMatrixUtil.getColumn(daughters, rows, 2, 1), FortranMatrixUtil.getColumn(nodepred.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(bestvar.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(xbestsplit.getValues(), rows, columns, i), segmentSchema);
        treeModels.add(treeModel);
    }
    MiningModel miningModel = new MiningModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel)).setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.MAJORITY_VOTE, treeModels)).setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));
    return miningModel;
}

Example 12 with CategoricalLabel

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

the class RExpEncoder method setLabel.

public void setLabel(DataField dataField) {
    Label label;
    OpType opType = dataField.getOpType();
    switch(opType) {
        case CATEGORICAL:
            label = new CategoricalLabel(dataField);
            break;
        case CONTINUOUS:
            label = new ContinuousLabel(dataField);
            break;
        default:
            throw new IllegalArgumentException();
    }
    setLabel(label);
}

Example 13 with CategoricalLabel

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

the class GeneralizedLinearRegressionModelConverter method registerOutputFields.

@Override
public List<OutputField> registerOutputFields(Label label, SparkMLEncoder encoder) {
    List<OutputField> result = super.registerOutputFields(label, encoder);
    MiningFunction miningFunction = getMiningFunction();
    switch(miningFunction) {
        case CLASSIFICATION:
            CategoricalLabel categoricalLabel = (CategoricalLabel) label;
            result = new ArrayList<>(result);
            result.addAll(ModelUtil.createProbabilityFields(DataType.DOUBLE, categoricalLabel.getValues()));
            break;
        default:
            break;
    }
    return result;
}

Example 14 with CategoricalLabel

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

the class GeneralizedLinearRegressionModelConverter method encodeModel.

@Override
public GeneralRegressionModel encodeModel(Schema schema) {
    GeneralizedLinearRegressionModel model = getTransformer();
    String targetCategory = null;
    MiningFunction miningFunction = getMiningFunction();
    switch(miningFunction) {
        case CLASSIFICATION:
            CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
            if (categoricalLabel.size() != 2) {
                throw new IllegalArgumentException();
            }
            targetCategory = categoricalLabel.getValue(1);
            break;
        default:
            break;
    }
    GeneralRegressionModel generalRegressionModel = new GeneralRegressionModel(GeneralRegressionModel.ModelType.GENERALIZED_LINEAR, miningFunction, ModelUtil.createMiningSchema(schema.getLabel()), null, null, null).setDistribution(parseFamily(model.getFamily())).setLinkFunction(parseLinkFunction(model.getLink())).setLinkParameter(parseLinkParameter(model.getLink()));
    GeneralRegressionModelUtil.encodeRegressionTable(generalRegressionModel, schema.getFeatures(), model.intercept(), VectorUtil.toList(model.coefficients()), targetCategory);
    return generalRegressionModel;
}

Example 15 with CategoricalLabel

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

the class MultilayerPerceptronClassificationModelConverter method encodeModel.

@Override
public NeuralNetwork encodeModel(Schema schema) {
    MultilayerPerceptronClassificationModel model = getTransformer();
    int[] layers = model.layers();
    Vector weights = model.weights();
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    if (categoricalLabel.size() != layers[layers.length - 1]) {
        throw new IllegalArgumentException();
    }
    List<? extends Feature> features = schema.getFeatures();
    if (features.size() != layers[0]) {
        throw new IllegalArgumentException();
    }
    NeuralInputs neuralInputs = NeuralNetworkUtil.createNeuralInputs(features, DataType.DOUBLE);
    List<? extends Entity> entities = neuralInputs.getNeuralInputs();
    List<NeuralLayer> neuralLayers = new ArrayList<>();
    int weightPos = 0;
    for (int layer = 1; layer < layers.length; layer++) {
        NeuralLayer neuralLayer = new NeuralLayer();
        int rows = entities.size();
        int columns = layers[layer];
        List<List<Double>> weightMatrix = new ArrayList<>();
        for (int column = 0; column < columns; column++) {
            List<Double> weightVector = new ArrayList<>();
            for (int row = 0; row < rows; row++) {
                weightVector.add(weights.apply(weightPos + (row * columns) + column));
            }
            weightMatrix.add(weightVector);
        }
        weightPos += (rows * columns);
        for (int column = 0; column < columns; column++) {
            List<Double> weightVector = weightMatrix.get(column);
            Double bias = weights.apply(weightPos);
            Neuron neuron = NeuralNetworkUtil.createNeuron(entities, weightVector, bias).setId(String.valueOf(layer) + "/" + String.valueOf(column + 1));
            neuralLayer.addNeurons(neuron);
            weightPos++;
        }
        if (layer == (layers.length - 1)) {
            neuralLayer.setActivationFunction(NeuralNetwork.ActivationFunction.IDENTITY).setNormalizationMethod(NeuralNetwork.NormalizationMethod.SOFTMAX);
        }
        neuralLayers.add(neuralLayer);
        entities = neuralLayer.getNeurons();
    }
    if (weightPos != weights.size()) {
        throw new IllegalArgumentException();
    }
    NeuralNetwork neuralNetwork = new NeuralNetwork(MiningFunction.CLASSIFICATION, NeuralNetwork.ActivationFunction.LOGISTIC, ModelUtil.createMiningSchema(categoricalLabel), neuralInputs, neuralLayers).setNeuralOutputs(NeuralNetworkUtil.createClassificationNeuralOutputs(entities, categoricalLabel));
    return neuralNetwork;
}