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

Example 1 with Feature

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

the class LMConverter method encodeSchema.

public void encodeSchema(FormulaContext context, RExp terms, RExpEncoder encoder) {
    RIntegerVector response = (RIntegerVector) terms.getAttributeValue("response");
    Formula formula = FormulaUtil.createFormula(terms, context, encoder);
    // Dependent variable
    int responseIndex = response.asScalar();
    if (responseIndex != 0) {
        DataField dataField = (DataField) formula.getField(responseIndex - 1);
        encoder.setLabel(dataField);
    } else {
        throw new IllegalArgumentException();
    }
    String interceptName = getInterceptName();
    // Independent variables
    List<String> coefficientNames = getCoefficientNames();
    for (String coefficientName : coefficientNames) {
        if ((interceptName).equals(coefficientName)) {
            continue;
        }
        Feature feature = formula.resolveFeature(coefficientName);
        encoder.addFeature(feature);
    }
    this.formula = formula;
}

Example 2 with Feature

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

the class MVRConverter method encodeSchema.

@Override
public void encodeSchema(RExpEncoder encoder) {
    RGenericVector mvr = getObject();
    RDoubleVector coefficients = (RDoubleVector) mvr.getValue("coefficients");
    RDoubleVector scale = (RDoubleVector) mvr.getValue("scale", true);
    RExp terms = mvr.getValue("terms");
    final RGenericVector model = (RGenericVector) mvr.getValue("model");
    RStringVector rowNames = coefficients.dimnames(0);
    RStringVector columnNames = coefficients.dimnames(1);
    FormulaContext context = new ModelFrameFormulaContext(model);
    Formula formula = FormulaUtil.createFormula(terms, context, encoder);
    // Dependent variable
    {
        FieldName name = FieldName.create(columnNames.asScalar());
        DataField dataField = (DataField) encoder.getField(name);
        encoder.setLabel(dataField);
    }
    // Independent variables
    for (int i = 0; i < rowNames.size(); i++) {
        String rowName = rowNames.getValue(i);
        Feature feature = formula.resolveFeature(rowName);
        if (scale != null) {
            feature = feature.toContinuousFeature();
            Apply apply = PMMLUtil.createApply("/", feature.ref(), PMMLUtil.createConstant(scale.getValue(i)));
            DerivedField derivedField = encoder.createDerivedField(FeatureUtil.createName("scale", feature), OpType.CONTINUOUS, DataType.DOUBLE, apply);
            feature = new ContinuousFeature(encoder, derivedField);
        }
        encoder.addFeature(feature);
    }
}

Example 3 with Feature

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

the class RandomForestConverter method encodeNode.

private <P extends Number> void encodeNode(Node node, int i, ScoreEncoder<P> scoreEncoder, List<? extends Number> leftDaughter, List<? extends Number> rightDaughter, List<? extends Number> bestvar, List<Double> xbestsplit, List<P> nodepred, Schema schema) {
    Predicate leftPredicate;
    Predicate rightPredicate;
    int var = ValueUtil.asInt(bestvar.get(i));
    if (var != 0) {
        Feature feature = schema.getFeature(var - 1);
        Double split = xbestsplit.get(i);
        if (feature instanceof BooleanFeature) {
            BooleanFeature booleanFeature = (BooleanFeature) feature;
            if (split != 0.5d) {
                throw new IllegalArgumentException();
            }
            leftPredicate = createSimplePredicate(booleanFeature, SimplePredicate.Operator.EQUAL, booleanFeature.getValue(0));
            rightPredicate = createSimplePredicate(booleanFeature, SimplePredicate.Operator.EQUAL, booleanFeature.getValue(1));
        } else if (feature instanceof CategoricalFeature) {
            CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
            List<String> values = categoricalFeature.getValues();
            leftPredicate = createSimpleSetPredicate(categoricalFeature, selectValues(values, split, true));
            rightPredicate = createSimpleSetPredicate(categoricalFeature, selectValues(values, split, false));
        } else {
            ContinuousFeature continuousFeature = feature.toContinuousFeature();
            String value = ValueUtil.formatValue(split);
            leftPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.LESS_OR_EQUAL, value);
            rightPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.GREATER_THAN, value);
        }
    } else {
        P prediction = nodepred.get(i);
        node.setScore(scoreEncoder.encode(prediction));
        return;
    }
    int left = ValueUtil.asInt(leftDaughter.get(i));
    if (left != 0) {
        Node leftChild = new Node().setId(String.valueOf(left)).setPredicate(leftPredicate);
        encodeNode(leftChild, left - 1, scoreEncoder, leftDaughter, rightDaughter, bestvar, xbestsplit, nodepred, schema);
        node.addNodes(leftChild);
    }
    int right = ValueUtil.asInt(rightDaughter.get(i));
    if (right != 0) {
        Node rightChild = new Node().setId(String.valueOf(right)).setPredicate(rightPredicate);
        encodeNode(rightChild, right - 1, scoreEncoder, leftDaughter, rightDaughter, bestvar, xbestsplit, nodepred, schema);
        node.addNodes(rightChild);
    }
}

Example 4 with Feature

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

the class RangerConverter method encodeNode.

private void encodeNode(Node node, int index, ScoreEncoder scoreEncoder, RNumberVector<?> leftChildIDs, RNumberVector<?> rightChildIDs, RNumberVector<?> splitVarIDs, RNumberVector<?> splitValues, RGenericVector terminalClassCounts, Schema schema) {
    int leftIndex = ValueUtil.asInt(leftChildIDs.getValue(index));
    int rightIndex = ValueUtil.asInt(rightChildIDs.getValue(index));
    Number splitValue = splitValues.getValue(index);
    RNumberVector<?> terminalClassCount = (terminalClassCounts != null ? (RNumberVector<?>) terminalClassCounts.getValue(index) : null);
    if (leftIndex == 0 && rightIndex == 0) {
        scoreEncoder.encode(node, splitValue, terminalClassCount);
        return;
    }
    Predicate leftPredicate;
    Predicate rightPredicate;
    int splitVarIndex = ValueUtil.asInt(splitVarIDs.getValue(index));
    Feature feature = schema.getFeature(splitVarIndex - 1);
    if (feature instanceof CategoricalFeature) {
        CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
        int splitLevelIndex = ValueUtil.asInt(Math.floor(splitValue.doubleValue()));
        List<String> values = categoricalFeature.getValues();
        leftPredicate = createSimpleSetPredicate(categoricalFeature, values.subList(0, splitLevelIndex));
        rightPredicate = createSimpleSetPredicate(categoricalFeature, values.subList(splitLevelIndex, values.size()));
    } else {
        ContinuousFeature continuousFeature = feature.toContinuousFeature();
        String value = ValueUtil.formatValue(splitValue);
        leftPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.LESS_OR_EQUAL, value);
        rightPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.GREATER_THAN, value);
    }
    Node leftChild = new Node().setPredicate(leftPredicate);
    encodeNode(leftChild, leftIndex, scoreEncoder, leftChildIDs, rightChildIDs, splitVarIDs, splitValues, terminalClassCounts, schema);
    Node rightChild = new Node().setPredicate(rightPredicate);
    encodeNode(rightChild, rightIndex, scoreEncoder, leftChildIDs, rightChildIDs, splitVarIDs, splitValues, terminalClassCounts, schema);
    node.addNodes(leftChild, rightChild);
}

Example 5 with Feature

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

the class SVMConverter method scale.

private List<Feature> scale(List<Feature> features, RExpEncoder encoder) {
    RGenericVector svm = getObject();
    RDoubleVector sv = (RDoubleVector) svm.getValue("SV");
    RBooleanVector scaled = (RBooleanVector) svm.getValue("scaled");
    RGenericVector xScale = (RGenericVector) svm.getValue("x.scale");
    RStringVector rowNames = sv.dimnames(0);
    RStringVector columnNames = sv.dimnames(1);
    if ((scaled.size() != columnNames.size()) || (scaled.size() != features.size())) {
        throw new IllegalArgumentException();
    }
    RDoubleVector xScaledCenter = null;
    RDoubleVector xScaledScale = null;
    if (xScale != null) {
        xScaledCenter = (RDoubleVector) xScale.getValue("scaled:center");
        xScaledScale = (RDoubleVector) xScale.getValue("scaled:scale");
    }
    List<Feature> result = new ArrayList<>();
    for (int i = 0; i < columnNames.size(); i++) {
        String columnName = columnNames.getValue(i);
        Feature feature = features.get(i);
        if (scaled.getValue(i)) {
            feature = feature.toContinuousFeature();
            FieldName name = FeatureUtil.createName("scale", feature);
            DerivedField derivedField = encoder.getDerivedField(name);
            if (derivedField == null) {
                Double center = xScaledCenter.getValue(columnName);
                Double scale = xScaledScale.getValue(columnName);
                Apply apply = PMMLUtil.createApply("/", PMMLUtil.createApply("-", feature.ref(), PMMLUtil.createConstant(center)), PMMLUtil.createConstant(scale));
                derivedField = encoder.createDerivedField(name, OpType.CONTINUOUS, DataType.DOUBLE, apply);
            }
            feature = new ContinuousFeature(encoder, derivedField);
        }
        result.add(feature);
    }
    return result;
}