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

Example 11 with Feature

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

the class ModelConverter method encodeSchema.

public Schema encodeSchema(SparkMLEncoder encoder) {
    T model = getTransformer();
    Label label = null;
    if (model instanceof HasLabelCol) {
        HasLabelCol hasLabelCol = (HasLabelCol) model;
        String labelCol = hasLabelCol.getLabelCol();
        Feature feature = encoder.getOnlyFeature(labelCol);
        MiningFunction miningFunction = getMiningFunction();
        switch(miningFunction) {
            case CLASSIFICATION:
                {
                    if (feature instanceof CategoricalFeature) {
                        CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
                        DataField dataField = encoder.getDataField(categoricalFeature.getName());
                        label = new CategoricalLabel(dataField);
                    } else if (feature instanceof ContinuousFeature) {
                        ContinuousFeature continuousFeature = (ContinuousFeature) feature;
                        int numClasses = 2;
                        if (model instanceof ClassificationModel) {
                            ClassificationModel<?, ?> classificationModel = (ClassificationModel<?, ?>) model;
                            numClasses = classificationModel.numClasses();
                        }
                        List<String> categories = new ArrayList<>();
                        for (int i = 0; i < numClasses; i++) {
                            categories.add(String.valueOf(i));
                        }
                        Field<?> field = encoder.toCategorical(continuousFeature.getName(), categories);
                        encoder.putOnlyFeature(labelCol, new CategoricalFeature(encoder, field, categories));
                        label = new CategoricalLabel(field.getName(), field.getDataType(), categories);
                    } else {
                        throw new IllegalArgumentException("Expected a categorical or categorical-like continuous feature, got " + feature);
                    }
                }
                break;
            case REGRESSION:
                {
                    Field<?> field = encoder.toContinuous(feature.getName());
                    field.setDataType(DataType.DOUBLE);
                    label = new ContinuousLabel(field.getName(), field.getDataType());
                }
                break;
            default:
                throw new IllegalArgumentException("Mining function " + miningFunction + " is not supported");
        }
    }
    if (model instanceof ClassificationModel) {
        ClassificationModel<?, ?> classificationModel = (ClassificationModel<?, ?>) model;
        CategoricalLabel categoricalLabel = (CategoricalLabel) label;
        int numClasses = classificationModel.numClasses();
        if (numClasses != categoricalLabel.size()) {
            throw new IllegalArgumentException("Expected " + numClasses + " target categories, got " + categoricalLabel.size() + " target categories");
        }
    }
    String featuresCol = model.getFeaturesCol();
    List<Feature> features = encoder.getFeatures(featuresCol);
    if (model instanceof PredictionModel) {
        PredictionModel<?, ?> predictionModel = (PredictionModel<?, ?>) model;
        int numFeatures = predictionModel.numFeatures();
        if (numFeatures != -1 && features.size() != numFeatures) {
            throw new IllegalArgumentException("Expected " + numFeatures + " features, got " + features.size() + " features");
        }
    }
    Schema result = new Schema(label, features);
    return result;
}

Example 12 with Feature

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

the class SparkMLEncoder method getFeatures.

public List<Feature> getFeatures(String column) {
    List<Feature> features = this.columnFeatures.get(column);
    if (features == null) {
        FieldName name = FieldName.create(column);
        DataField dataField = getDataField(name);
        if (dataField == null) {
            dataField = createDataField(name);
        }
        Feature feature;
        DataType dataType = dataField.getDataType();
        switch(dataType) {
            case STRING:
                feature = new WildcardFeature(this, dataField);
                break;
            case INTEGER:
            case DOUBLE:
                feature = new ContinuousFeature(this, dataField);
                break;
            case BOOLEAN:
                feature = new BooleanFeature(this, dataField);
                break;
            default:
                throw new IllegalArgumentException("Data type " + dataType + " is not supported");
        }
        return Collections.singletonList(feature);
    }
    return features;
}

Example 13 with Feature

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

the class SparkMLEncoder method getFeatures.

public List<Feature> getFeatures(String column, int[] indices) {
    List<Feature> features = getFeatures(column);
    List<Feature> result = new ArrayList<>();
    for (int i = 0; i < indices.length; i++) {
        int index = indices[i];
        Feature feature = features.get(index);
        result.add(feature);
    }
    return result;
}

Example 14 with Feature

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

the class TermFeature method createApply.

public Apply createApply() {
    DefineFunction defineFunction = getDefineFunction();
    Feature feature = getFeature();
    String value = getValue();
    Constant constant = PMMLUtil.createConstant(value, DataType.STRING);
    return PMMLUtil.createApply(defineFunction.getName(), feature.ref(), constant);
}

Example 15 with Feature

use of org.jpmml.converter.Feature 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;
}