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

Example 11 with CategoricalFeature

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

the class RExpEncoder method addFeature.

public void addFeature(Field<?> field) {
    Feature feature;
    OpType opType = field.getOpType();
    switch(opType) {
        case CATEGORICAL:
            feature = new CategoricalFeature(this, (DataField) field);
            break;
        case CONTINUOUS:
            feature = new ContinuousFeature(this, field);
            break;
        default:
            throw new IllegalArgumentException();
    }
    addFeature(feature);
}

Example 12 with CategoricalFeature

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

the class IndexToStringConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    IndexToString transformer = getTransformer();
    DataField dataField = encoder.createDataField(formatName(transformer), OpType.CATEGORICAL, DataType.STRING, Arrays.asList(transformer.getLabels()));
    return Collections.<Feature>singletonList(new CategoricalFeature(encoder, dataField));
}

Example 13 with CategoricalFeature

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

the class OneHotEncoderConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    OneHotEncoder transformer = getTransformer();
    boolean dropLast = true;
    Option<Object> dropLastOption = transformer.get(transformer.dropLast());
    if (dropLastOption.isDefined()) {
        dropLast = (Boolean) dropLastOption.get();
    }
    CategoricalFeature categoricalFeature = (CategoricalFeature) encoder.getOnlyFeature(transformer.getInputCol());
    List<String> values = categoricalFeature.getValues();
    if (dropLast) {
        values = values.subList(0, values.size() - 1);
    }
    List<Feature> result = new ArrayList<>();
    for (String value : values) {
        result.add(new BinaryFeature(encoder, categoricalFeature.getName(), DataType.STRING, value));
    }
    return result;
}

Example 14 with CategoricalFeature

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

the class BucketizerConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    Bucketizer transformer = getTransformer();
    Feature feature = encoder.getOnlyFeature(transformer.getInputCol());
    ContinuousFeature continuousFeature = feature.toContinuousFeature();
    Discretize discretize = new Discretize(continuousFeature.getName());
    List<String> categories = new ArrayList<>();
    double[] splits = transformer.getSplits();
    for (int i = 0; i < (splits.length - 1); i++) {
        String category = String.valueOf(i);
        categories.add(category);
        Interval interval = new Interval((i < (splits.length - 2)) ? Interval.Closure.CLOSED_OPEN : Interval.Closure.CLOSED_CLOSED).setLeftMargin(formatMargin(splits[i])).setRightMargin(formatMargin(splits[i + 1]));
        DiscretizeBin discretizeBin = new DiscretizeBin(category, interval);
        discretize.addDiscretizeBins(discretizeBin);
    }
    DerivedField derivedField = encoder.createDerivedField(formatName(transformer), OpType.CATEGORICAL, DataType.INTEGER, discretize);
    return Collections.<Feature>singletonList(new CategoricalFeature(encoder, derivedField, categories));
}

Example 15 with CategoricalFeature

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

the class ClassificationModelConverter method registerOutputFields.

@Override
public List<OutputField> registerOutputFields(Label label, SparkMLEncoder encoder) {
    T model = getTransformer();
    CategoricalLabel categoricalLabel = (CategoricalLabel) label;
    List<OutputField> result = new ArrayList<>();
    String predictionCol = model.getPredictionCol();
    OutputField pmmlPredictedField = ModelUtil.createPredictedField(FieldName.create("pmml(" + predictionCol + ")"), categoricalLabel.getDataType(), OpType.CATEGORICAL);
    result.add(pmmlPredictedField);
    List<String> categories = new ArrayList<>();
    DocumentBuilder documentBuilder = DOMUtil.createDocumentBuilder();
    InlineTable inlineTable = new InlineTable();
    List<String> columns = Arrays.asList("input", "output");
    for (int i = 0; i < categoricalLabel.size(); i++) {
        String value = categoricalLabel.getValue(i);
        String category = String.valueOf(i);
        categories.add(category);
        Row row = DOMUtil.createRow(documentBuilder, columns, Arrays.asList(value, category));
        inlineTable.addRows(row);
    }
    MapValues mapValues = new MapValues().addFieldColumnPairs(new FieldColumnPair(pmmlPredictedField.getName(), columns.get(0))).setOutputColumn(columns.get(1)).setInlineTable(inlineTable);
    final OutputField predictedField = new OutputField(FieldName.create(predictionCol), DataType.DOUBLE).setOpType(OpType.CATEGORICAL).setResultFeature(ResultFeature.TRANSFORMED_VALUE).setExpression(mapValues);
    result.add(predictedField);
    Feature feature = new CategoricalFeature(encoder, predictedField.getName(), predictedField.getDataType(), categories) {

        @Override
        public ContinuousFeature toContinuousFeature() {
            PMMLEncoder encoder = ensureEncoder();
            return new ContinuousFeature(encoder, getName(), getDataType());
        }
    };
    encoder.putOnlyFeature(predictionCol, feature);
    if (model instanceof HasProbabilityCol) {
        HasProbabilityCol hasProbabilityCol = (HasProbabilityCol) model;
        String probabilityCol = hasProbabilityCol.getProbabilityCol();
        List<Feature> features = new ArrayList<>();
        for (int i = 0; i < categoricalLabel.size(); i++) {
            String value = categoricalLabel.getValue(i);
            OutputField probabilityField = ModelUtil.createProbabilityField(FieldName.create(probabilityCol + "(" + value + ")"), DataType.DOUBLE, value);
            result.add(probabilityField);
            features.add(new ContinuousFeature(encoder, probabilityField.getName(), probabilityField.getDataType()));
        }
        encoder.putFeatures(probabilityCol, features);
    }
    return result;
}