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

Example 1 with ContinuousLabel

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

the class GBMConverter method encodeModel.

@Override
public MiningModel encodeModel(Schema schema) {
    RGenericVector gbm = getObject();
    RDoubleVector initF = (RDoubleVector) gbm.getValue("initF");
    RGenericVector trees = (RGenericVector) gbm.getValue("trees");
    RGenericVector c_splits = (RGenericVector) gbm.getValue("c.splits");
    RGenericVector distribution = (RGenericVector) gbm.getValue("distribution");
    RStringVector distributionName = (RStringVector) distribution.getValue("name");
    Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
    List<TreeModel> treeModels = new ArrayList<>();
    for (int i = 0; i < trees.size(); i++) {
        RGenericVector tree = (RGenericVector) trees.getValue(i);
        TreeModel treeModel = encodeTreeModel(MiningFunction.REGRESSION, tree, c_splits, segmentSchema);
        treeModels.add(treeModel);
    }
    MiningModel miningModel = encodeMiningModel(distributionName, treeModels, initF.asScalar(), schema);
    return miningModel;
}

Example 2 with ContinuousLabel

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

the class ElmNNConverter method encodeModel.

@Override
public NeuralNetwork encodeModel(Schema schema) {
    RGenericVector elmNN = getObject();
    RDoubleVector inpweight = (RDoubleVector) elmNN.getValue("inpweight");
    RDoubleVector biashid = (RDoubleVector) elmNN.getValue("biashid");
    RDoubleVector outweight = (RDoubleVector) elmNN.getValue("outweight");
    RStringVector actfun = (RStringVector) elmNN.getValue("actfun");
    RDoubleVector nhid = (RDoubleVector) elmNN.getValue("nhid");
    Label label = schema.getLabel();
    List<? extends Feature> features = schema.getFeatures();
    switch(actfun.asScalar()) {
        case "purelin":
            break;
        default:
            throw new IllegalArgumentException();
    }
    NeuralInputs neuralInputs = NeuralNetworkUtil.createNeuralInputs(features, DataType.DOUBLE);
    List<? extends Entity> entities = neuralInputs.getNeuralInputs();
    List<NeuralLayer> neuralLayers = new ArrayList<>(2);
    NeuralLayer hiddenNeuralLayer = new NeuralLayer();
    int rows = ValueUtil.asInt(nhid.asScalar());
    int columns = 1 + features.size();
    for (int row = 0; row < rows; row++) {
        List<Double> weights = FortranMatrixUtil.getRow(inpweight.getValues(), rows, columns, row);
        Double bias = biashid.getValue(row);
        bias += weights.remove(0);
        Neuron neuron = NeuralNetworkUtil.createNeuron(entities, weights, bias).setId("hidden/" + String.valueOf(row + 1));
        hiddenNeuralLayer.addNeurons(neuron);
    }
    neuralLayers.add(hiddenNeuralLayer);
    entities = hiddenNeuralLayer.getNeurons();
    NeuralLayer outputNeuralLayer = new NeuralLayer();
    // XXX
    columns = 1;
    for (int column = 0; column < columns; column++) {
        List<Double> weights = FortranMatrixUtil.getColumn(outweight.getValues(), rows, columns, column);
        Double bias = Double.NaN;
        Neuron neuron = NeuralNetworkUtil.createNeuron(entities, weights, bias).setId("output/" + String.valueOf(column + 1));
        outputNeuralLayer.addNeurons(neuron);
    }
    neuralLayers.add(outputNeuralLayer);
    entities = outputNeuralLayer.getNeurons();
    NeuralOutputs neuralOutputs = NeuralNetworkUtil.createRegressionNeuralOutputs(entities, (ContinuousLabel) label);
    NeuralNetwork neuralNetwork = new NeuralNetwork(MiningFunction.REGRESSION, NeuralNetwork.ActivationFunction.IDENTITY, ModelUtil.createMiningSchema(label), neuralInputs, neuralLayers).setNeuralOutputs(neuralOutputs);
    return neuralNetwork;
}

Example 3 with ContinuousLabel

use of org.jpmml.converter.ContinuousLabel 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 4 with ContinuousLabel

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

the class GBMConverter method encodeBinaryClassification.

private MiningModel encodeBinaryClassification(List<TreeModel> treeModels, Double initF, double coefficient, Schema schema) {
    Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
    MiningModel miningModel = createMiningModel(treeModels, initF, segmentSchema).setOutput(ModelUtil.createPredictedOutput(FieldName.create("gbmValue"), OpType.CONTINUOUS, DataType.DOUBLE));
    return MiningModelUtil.createBinaryLogisticClassification(miningModel, -coefficient, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}

Example 5 with ContinuousLabel

use of org.jpmml.converter.ContinuousLabel 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);
}