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

Example 26 with ContinuousFeature

use of org.jpmml.converter.ContinuousFeature 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;
}