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

Example 11 with ContinuousFeature

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

the class TermFeature method toContinuousFeature.

@Override
public ContinuousFeature toContinuousFeature() {
    PMMLEncoder encoder = ensureEncoder();
    DerivedField derivedField = encoder.getDerivedField(getName());
    if (derivedField == null) {
        Apply apply = createApply();
        derivedField = encoder.createDerivedField(getName(), OpType.CONTINUOUS, getDataType(), apply);
    }
    return new ContinuousFeature(encoder, derivedField);
}

Example 12 with ContinuousFeature

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

the class StandardScalerModelConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    StandardScalerModel transformer = getTransformer();
    List<Feature> features = encoder.getFeatures(transformer.getInputCol());
    Vector mean = transformer.mean();
    if (transformer.getWithMean() && mean.size() != features.size()) {
        throw new IllegalArgumentException();
    }
    Vector std = transformer.std();
    if (transformer.getWithStd() && std.size() != features.size()) {
        throw new IllegalArgumentException();
    }
    List<Feature> result = new ArrayList<>();
    for (int i = 0; i < features.size(); i++) {
        Feature feature = features.get(i);
        ContinuousFeature continuousFeature = feature.toContinuousFeature();
        Expression expression = continuousFeature.ref();
        if (transformer.getWithMean()) {
            double meanValue = mean.apply(i);
            if (!ValueUtil.isZero(meanValue)) {
                expression = PMMLUtil.createApply("-", expression, PMMLUtil.createConstant(meanValue));
            }
        }
        if (transformer.getWithStd()) {
            double stdValue = std.apply(i);
            if (!ValueUtil.isOne(stdValue)) {
                expression = PMMLUtil.createApply("*", expression, PMMLUtil.createConstant(1d / stdValue));
            }
        }
        DerivedField derivedField = encoder.createDerivedField(formatName(transformer, i), OpType.CONTINUOUS, DataType.DOUBLE, expression);
        result.add(new ContinuousFeature(encoder, derivedField));
    }
    return result;
}

Example 13 with ContinuousFeature

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

the class VectorIndexerModelConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    VectorIndexerModel transformer = getTransformer();
    List<Feature> features = encoder.getFeatures(transformer.getInputCol());
    int numFeatures = transformer.numFeatures();
    if (numFeatures != features.size()) {
        throw new IllegalArgumentException("Expected " + numFeatures + " features, got " + features.size() + " features");
    }
    Map<Integer, Map<Double, Integer>> categoryMaps = transformer.javaCategoryMaps();
    List<Feature> result = new ArrayList<>();
    for (int i = 0; i < numFeatures; i++) {
        Feature feature = features.get(i);
        Map<Double, Integer> categoryMap = categoryMaps.get(i);
        if (categoryMap != null) {
            List<String> categories = new ArrayList<>();
            List<String> values = new ArrayList<>();
            DocumentBuilder documentBuilder = DOMUtil.createDocumentBuilder();
            InlineTable inlineTable = new InlineTable();
            List<String> columns = Arrays.asList("input", "output");
            List<Map.Entry<Double, Integer>> entries = new ArrayList<>(categoryMap.entrySet());
            Collections.sort(entries, VectorIndexerModelConverter.COMPARATOR);
            for (Map.Entry<Double, Integer> entry : entries) {
                String category = ValueUtil.formatValue(entry.getKey());
                categories.add(category);
                String value = ValueUtil.formatValue(entry.getValue());
                values.add(value);
                Row row = DOMUtil.createRow(documentBuilder, columns, Arrays.asList(category, value));
                inlineTable.addRows(row);
            }
            encoder.toCategorical(feature.getName(), categories);
            MapValues mapValues = new MapValues().addFieldColumnPairs(new FieldColumnPair(feature.getName(), columns.get(0))).setOutputColumn(columns.get(1)).setInlineTable(inlineTable);
            DerivedField derivedField = encoder.createDerivedField(formatName(transformer, i), OpType.CATEGORICAL, DataType.INTEGER, mapValues);
            result.add(new CategoricalFeature(encoder, derivedField, values));
        } else {
            result.add((ContinuousFeature) feature);
        }
    }
    return result;
}

Example 14 with ContinuousFeature

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

the class BinarizerConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    Binarizer transformer = getTransformer();
    Feature feature = encoder.getOnlyFeature(transformer.getInputCol());
    ContinuousFeature continuousFeature = feature.toContinuousFeature();
    Apply apply = new Apply("if").addExpressions(PMMLUtil.createApply("lessOrEqual", continuousFeature.ref(), PMMLUtil.createConstant(transformer.getThreshold()))).addExpressions(PMMLUtil.createConstant(0d), PMMLUtil.createConstant(1d));
    DerivedField derivedField = encoder.createDerivedField(formatName(transformer), OpType.CATEGORICAL, DataType.DOUBLE, apply);
    return Collections.<Feature>singletonList(new CategoricalFeature(encoder, derivedField, Arrays.asList("0", "1")));
}

Example 15 with ContinuousFeature

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

the class ClusteringModelConverter method registerOutputFields.

@Override
public List<OutputField> registerOutputFields(Label label, SparkMLEncoder encoder) {
    T model = getTransformer();
    String predictionCol = model.getPredictionCol();
    OutputField predictedField = ModelUtil.createPredictedField(FieldName.create(predictionCol), DataType.STRING, OpType.CATEGORICAL);
    Feature feature = new Feature(encoder, predictedField.getName(), predictedField.getDataType()) {

        @Override
        public ContinuousFeature toContinuousFeature() {
            throw new UnsupportedOperationException();
        }
    };
    encoder.putOnlyFeature(predictionCol, feature);
    return Collections.singletonList(predictedField);
}