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

Example 21 with ContinuousFeature

use of org.jpmml.converter.ContinuousFeature 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 22 with ContinuousFeature

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

the class MaxAbsScalerModelConverter method encodeFeatures.

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

Example 23 with ContinuousFeature

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

the class MinMaxScalerModelConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    MinMaxScalerModel transformer = getTransformer();
    double rescaleFactor = (transformer.getMax() - transformer.getMin());
    double rescaleConstant = transformer.getMin();
    List<Feature> features = encoder.getFeatures(transformer.getInputCol());
    Vector originalMax = transformer.originalMax();
    if (originalMax.size() != features.size()) {
        throw new IllegalArgumentException();
    }
    Vector originalMin = transformer.originalMin();
    if (originalMin.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();
        double max = originalMax.apply(i);
        double min = originalMin.apply(i);
        Expression expression = PMMLUtil.createApply("/", PMMLUtil.createApply("-", continuousFeature.ref(), PMMLUtil.createConstant(min)), PMMLUtil.createConstant(max - min));
        if (!ValueUtil.isOne(rescaleFactor)) {
            expression = PMMLUtil.createApply("*", expression, PMMLUtil.createConstant(rescaleFactor));
        }
        if (!ValueUtil.isZero(rescaleConstant)) {
            expression = PMMLUtil.createApply("+", expression, PMMLUtil.createConstant(rescaleConstant));
        }
        DerivedField derivedField = encoder.createDerivedField(formatName(transformer, i), OpType.CONTINUOUS, DataType.DOUBLE, expression);
        result.add(new ContinuousFeature(encoder, derivedField));
    }
    return result;
}

Example 24 with ContinuousFeature

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

the class PCAModelConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    PCAModel transformer = getTransformer();
    List<Feature> features = encoder.getFeatures(transformer.getInputCol());
    DenseMatrix pc = transformer.pc();
    if (pc.numRows() != features.size()) {
        throw new IllegalArgumentException();
    }
    List<Feature> result = new ArrayList<>();
    for (int i = 0; i < transformer.getK(); i++) {
        Apply apply = new Apply("sum");
        for (int j = 0; j < features.size(); j++) {
            Feature feature = features.get(j);
            ContinuousFeature continuousFeature = feature.toContinuousFeature();
            Expression expression = continuousFeature.ref();
            Double coefficient = pc.apply(j, i);
            if (!ValueUtil.isOne(coefficient)) {
                expression = PMMLUtil.createApply("*", expression, PMMLUtil.createConstant(coefficient));
            }
            apply.addExpressions(expression);
        }
        DerivedField derivedField = encoder.createDerivedField(formatName(transformer, i), OpType.CONTINUOUS, DataType.DOUBLE, apply);
        result.add(new ContinuousFeature(encoder, derivedField));
    }
    return result;
}

Example 25 with ContinuousFeature

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