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

Example 31 with Feature

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

the class Formula method resolveFeature.

public Feature resolveFeature(String name) {
    RExpEncoder encoder = getEncoder();
    List<String> variables = split(name);
    if (variables.size() == 1) {
        return resolveFeature(FieldName.create(name));
    } else {
        List<Feature> variableFeatures = new ArrayList<>();
        for (String variable : variables) {
            Feature variableFeature = resolveFeature(FieldName.create(variable));
            variableFeatures.add(variableFeature);
        }
        return new InteractionFeature(encoder, FieldName.create(name), DataType.DOUBLE, variableFeatures);
    }
}

Example 32 with Feature

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

the class SVMConverter method encodeNonFormula.

private void encodeNonFormula(RExpEncoder encoder) {
    RGenericVector svm = getObject();
    RDoubleVector type = (RDoubleVector) svm.getValue("type");
    RDoubleVector sv = (RDoubleVector) svm.getValue("SV");
    RVector<?> levels = (RVector<?>) svm.getValue("levels");
    Type svmType = Type.values()[ValueUtil.asInt(type.asScalar())];
    RStringVector rowNames = sv.dimnames(0);
    RStringVector columnNames = sv.dimnames(1);
    // Dependent variable
    {
        FieldName name = FieldName.create("_target");
        switch(svmType) {
            case C_CLASSIFICATION:
            case NU_CLASSIFICATION:
                {
                    RStringVector stringLevels = (RStringVector) levels;
                    DataField dataField = encoder.createDataField(name, OpType.CATEGORICAL, DataType.STRING, stringLevels.getValues());
                    encoder.setLabel(dataField);
                }
                break;
            case ONE_CLASSIFICATION:
                {
                    encoder.setLabel(new ContinuousLabel(null, DataType.DOUBLE));
                }
                break;
            case EPS_REGRESSION:
            case NU_REGRESSION:
                {
                    DataField dataField = encoder.createDataField(name, OpType.CONTINUOUS, DataType.DOUBLE);
                    encoder.setLabel(dataField);
                }
                break;
        }
    }
    List<Feature> features = new ArrayList<>();
    // Independent variables
    for (int i = 0; i < columnNames.size(); i++) {
        String columnName = columnNames.getValue(i);
        DataField dataField = encoder.createDataField(FieldName.create(columnName), OpType.CONTINUOUS, DataType.DOUBLE);
        features.add(new ContinuousFeature(encoder, dataField));
    }
    features = scale(features, encoder);
    for (Feature feature : features) {
        encoder.addFeature(feature);
    }
}

Example 33 with Feature

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

the class SVMConverter method encodeFormula.

private void encodeFormula(RExpEncoder encoder) {
    RGenericVector svm = getObject();
    RDoubleVector type = (RDoubleVector) svm.getValue("type");
    RDoubleVector sv = (RDoubleVector) svm.getValue("SV");
    RVector<?> levels = (RVector<?>) svm.getValue("levels");
    RExp terms = svm.getValue("terms");
    final RGenericVector xlevels;
    try {
        xlevels = (RGenericVector) svm.getValue("xlevels");
    } catch (IllegalArgumentException iae) {
        throw new IllegalArgumentException("No variable levels information. Please initialize the \'xlevels\' element", iae);
    }
    Type svmType = Type.values()[ValueUtil.asInt(type.asScalar())];
    RStringVector rowNames = sv.dimnames(0);
    RStringVector columnNames = sv.dimnames(1);
    RIntegerVector response = (RIntegerVector) terms.getAttributeValue("response");
    FormulaContext context = new FormulaContext() {

        @Override
        public List<String> getCategories(String variable) {
            if (xlevels.hasValue(variable)) {
                RStringVector levels = (RStringVector) xlevels.getValue(variable);
                return levels.getValues();
            }
            return null;
        }

        @Override
        public RGenericVector getData() {
            return null;
        }
    };
    Formula formula = FormulaUtil.createFormula(terms, context, encoder);
    // Dependent variable
    int responseIndex = response.asScalar();
    if (responseIndex != 0) {
        DataField dataField = (DataField) formula.getField(responseIndex - 1);
        switch(svmType) {
            case C_CLASSIFICATION:
            case NU_CLASSIFICATION:
                {
                    RStringVector stringLevels = (RStringVector) levels;
                    dataField = (DataField) encoder.toCategorical(dataField.getName(), stringLevels.getValues());
                }
                break;
            case ONE_CLASSIFICATION:
                {
                    OpType opType = dataField.getOpType();
                    if (!(OpType.CONTINUOUS).equals(opType)) {
                        throw new IllegalArgumentException();
                    }
                }
                break;
            default:
                break;
        }
        encoder.setLabel(dataField);
    } else {
        switch(svmType) {
            case ONE_CLASSIFICATION:
                break;
            default:
                throw new IllegalArgumentException();
        }
        encoder.setLabel(new ContinuousLabel(null, DataType.DOUBLE));
    }
    List<Feature> features = new ArrayList<>();
    // Independent variables
    for (int i = 0; i < columnNames.size(); i++) {
        String columnName = columnNames.getValue(i);
        Feature feature = formula.resolveFeature(columnName);
        features.add(feature);
    }
    features = scale(features, encoder);
    for (Feature feature : features) {
        encoder.addFeature(feature);
    }
}

Example 34 with Feature

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

the class ScorecardConverter method encodeModel.

@Override
public Scorecard encodeModel(Schema schema) {
    RGenericVector glm = getObject();
    RDoubleVector coefficients = (RDoubleVector) glm.getValue("coefficients");
    RGenericVector family = (RGenericVector) glm.getValue("family");
    RGenericVector scConf;
    try {
        scConf = (RGenericVector) glm.getValue("sc.conf");
    } catch (IllegalArgumentException iae) {
        throw new IllegalArgumentException("No scorecard configuration information. Please initialize the \'sc.conf\' element", iae);
    }
    Double intercept = coefficients.getValue(LMConverter.INTERCEPT, true);
    List<? extends Feature> features = schema.getFeatures();
    if (coefficients.size() != (features.size() + (intercept != null ? 1 : 0))) {
        throw new IllegalArgumentException();
    }
    RNumberVector<?> odds = (RNumberVector<?>) scConf.getValue("odds");
    RNumberVector<?> basePoints = (RNumberVector<?>) scConf.getValue("base_points");
    RNumberVector<?> pdo = (RNumberVector<?>) scConf.getValue("pdo");
    double factor = (pdo.asScalar()).doubleValue() / Math.log(2);
    Map<FieldName, Characteristic> fieldCharacteristics = new LinkedHashMap<>();
    for (Feature feature : features) {
        FieldName name = feature.getName();
        if (!(feature instanceof BinaryFeature)) {
            throw new IllegalArgumentException();
        }
        Double coefficient = getFeatureCoefficient(feature, coefficients);
        Characteristic characteristic = fieldCharacteristics.get(name);
        if (characteristic == null) {
            characteristic = new Characteristic().setName(FeatureUtil.createName("score", feature));
            fieldCharacteristics.put(name, characteristic);
        }
        BinaryFeature binaryFeature = (BinaryFeature) feature;
        SimplePredicate simplePredicate = new SimplePredicate().setField(binaryFeature.getName()).setOperator(SimplePredicate.Operator.EQUAL).setValue(binaryFeature.getValue());
        Attribute attribute = new Attribute().setPartialScore(formatScore(-1d * coefficient * factor)).setPredicate(simplePredicate);
        characteristic.addAttributes(attribute);
    }
    Characteristics characteristics = new Characteristics();
    Collection<Map.Entry<FieldName, Characteristic>> entries = fieldCharacteristics.entrySet();
    for (Map.Entry<FieldName, Characteristic> entry : entries) {
        Characteristic characteristic = entry.getValue();
        Attribute attribute = new Attribute().setPartialScore(0d).setPredicate(new True());
        characteristic.addAttributes(attribute);
        characteristics.addCharacteristics(characteristic);
    }
    Scorecard scorecard = new Scorecard(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()), characteristics).setInitialScore(formatScore((basePoints.asScalar()).doubleValue() - Math.log((odds.asScalar()).doubleValue()) * factor - (intercept != null ? intercept * factor : 0))).setUseReasonCodes(false);
    return scorecard;
}

Example 35 with Feature

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

the class XGBoostConverter method encodeSchema.

@Override
public void encodeSchema(RExpEncoder encoder) {
    RGenericVector booster = getObject();
    RGenericVector schema = (RGenericVector) booster.getValue("schema", true);
    RVector<?> fmap;
    try {
        fmap = (RVector<?>) booster.getValue("fmap");
    } catch (IllegalArgumentException iae) {
        throw new IllegalArgumentException("No feature map information. Please initialize the \'fmap\' element");
    }
    FeatureMap featureMap;
    try {
        featureMap = loadFeatureMap(fmap);
    } catch (IOException ioe) {
        throw new IllegalArgumentException(ioe);
    }
    if (schema != null) {
        RVector<?> missing = (RVector<?>) schema.getValue("missing", true);
        if (missing != null) {
            featureMap.addMissingValue(ValueUtil.formatValue(missing.asScalar()));
        }
    }
    Learner learner = ensureLearner();
    // Dependent variable
    {
        ObjFunction obj = learner.getObj();
        FieldName targetField = FieldName.create("_target");
        List<String> targetCategories = null;
        if (schema != null) {
            RStringVector responseName = (RStringVector) schema.getValue("response_name", true);
            RStringVector responseLevels = (RStringVector) schema.getValue("response_levels", true);
            if (responseName != null) {
                targetField = FieldName.create(responseName.asScalar());
            }
            if (responseLevels != null) {
                targetCategories = responseLevels.getValues();
            }
        }
        Label label = obj.encodeLabel(targetField, targetCategories, encoder);
        encoder.setLabel(label);
    }
    // Independent variables
    {
        List<Feature> features = featureMap.encodeFeatures(encoder);
        for (Feature feature : features) {
            encoder.addFeature(feature);
        }
    }
}