Examples with DataField org.dmg.pmml.DataField used on opensource projects

Example 6 with DataField

use of org.dmg.pmml.DataField in project jpmml-r by jpmml.

the class RangerConverter method encodeSchema.

@Override
public void encodeSchema(RExpEncoder encoder) {
    RGenericVector ranger = getObject();
    RGenericVector forest;
    try {
        forest = (RGenericVector) ranger.getValue("forest");
    } catch (IllegalArgumentException iae) {
        throw new IllegalArgumentException("No forest information. Please initialize the \'forest\' element", iae);
    }
    RGenericVector variableLevels;
    try {
        variableLevels = (RGenericVector) ranger.getValue("variable.levels");
    } catch (IllegalArgumentException iae) {
        throw new IllegalArgumentException("No variable levels information. Please initialize the \'variable.levels\' element", iae);
    }
    RStringVector treeType = (RStringVector) ranger.getValue("treetype");
    // Dependent variable
    {
        FieldName name = FieldName.create("_target");
        DataField dataField;
        switch(treeType.asScalar()) {
            case "Regression":
                {
                    dataField = encoder.createDataField(name, OpType.CONTINUOUS, DataType.DOUBLE);
                }
                break;
            case "Classification":
            case "Probability estimation":
                {
                    RStringVector levels = (RStringVector) forest.getValue("levels");
                    dataField = encoder.createDataField(name, OpType.CATEGORICAL, null, levels.getValues());
                }
                break;
            default:
                throw new IllegalArgumentException();
        }
        encoder.setLabel(dataField);
    }
    RBooleanVector isOrdered = (RBooleanVector) forest.getValue("is.ordered");
    RStringVector independentVariableNames = (RStringVector) forest.getValue("independent.variable.names");
    // Independent variables
    for (int i = 0; i < independentVariableNames.size(); i++) {
        if (!isOrdered.getValue(i + 1)) {
            throw new IllegalArgumentException();
        }
        String independentVariableName = independentVariableNames.getValue(i);
        FieldName name = FieldName.create(independentVariableName);
        DataField dataField;
        if (variableLevels.hasValue(independentVariableName)) {
            RStringVector levels = (RStringVector) variableLevels.getValue(independentVariableName);
            dataField = encoder.createDataField(name, OpType.CATEGORICAL, DataType.STRING, levels.getValues());
        } else {
            dataField = encoder.createDataField(name, OpType.CONTINUOUS, DataType.DOUBLE);
        }
        encoder.addFeature(dataField);
    }
}

Example 7 with DataField

use of org.dmg.pmml.DataField in project jpmml-r by jpmml.

the class BinaryTreeConverter method encodeResponse.

private void encodeResponse(S4Object responses, RExpEncoder encoder) {
    RGenericVector variables = (RGenericVector) responses.getAttributeValue("variables");
    RBooleanVector is_nominal = (RBooleanVector) responses.getAttributeValue("is_nominal");
    RGenericVector levels = (RGenericVector) responses.getAttributeValue("levels");
    RStringVector variableNames = variables.names();
    String variableName = variableNames.asScalar();
    DataField dataField;
    Boolean categorical = is_nominal.getValue(variableName);
    if ((Boolean.TRUE).equals(categorical)) {
        this.miningFunction = MiningFunction.CLASSIFICATION;
        RExp targetVariable = variables.getValue(variableName);
        RStringVector targetVariableClass = (RStringVector) targetVariable.getAttributeValue("class");
        RStringVector targetCategories = (RStringVector) levels.getValue(variableName);
        dataField = encoder.createDataField(FieldName.create(variableName), OpType.CATEGORICAL, RExpUtil.getDataType(targetVariableClass.asScalar()), targetCategories.getValues());
    } else if ((Boolean.FALSE).equals(categorical)) {
        this.miningFunction = MiningFunction.REGRESSION;
        dataField = encoder.createDataField(FieldName.create(variableName), OpType.CONTINUOUS, DataType.DOUBLE);
    } else {
        throw new IllegalArgumentException();
    }
    encoder.setLabel(dataField);
}

Example 8 with DataField

use of org.dmg.pmml.DataField in project jpmml-r by jpmml.

the class ElmNNConverter method encodeSchema.

@Override
public void encodeSchema(RExpEncoder encoder) {
    RGenericVector elmNN = getObject();
    final RGenericVector model;
    try {
        model = (RGenericVector) elmNN.getValue("model");
    } catch (IllegalArgumentException iae) {
        throw new IllegalArgumentException("No model frame information. Please initialize the \'model\' element", iae);
    }
    RExp terms = model.getAttributeValue("terms");
    RIntegerVector response = (RIntegerVector) terms.getAttributeValue("response");
    RStringVector columns = (RStringVector) terms.getAttributeValue("columns");
    FormulaContext context = new ModelFrameFormulaContext(model);
    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);
    }
    // Independent variables
    for (int i = 0; i < columns.size(); i++) {
        String column = columns.getValue(i);
        if (i == 0 && "(Intercept)".equals(column)) {
            continue;
        }
        Feature feature = formula.resolveFeature(column);
        encoder.addFeature(feature);
    }
}

Example 9 with DataField

use of org.dmg.pmml.DataField in project jpmml-r by jpmml.

the class RandomForestConverter method encodeNonFormula.

private void encodeNonFormula(RExpEncoder encoder) {
    RGenericVector randomForest = getObject();
    RGenericVector forest = (RGenericVector) randomForest.getValue("forest");
    RNumberVector<?> y = (RNumberVector<?>) randomForest.getValue("y", true);
    RStringVector xNames = (RStringVector) randomForest.getValue("xNames", true);
    RNumberVector<?> ncat = (RNumberVector<?>) forest.getValue("ncat");
    RGenericVector xlevels = (RGenericVector) forest.getValue("xlevels");
    if (xNames == null) {
        xNames = xlevels.names();
    }
    // Dependent variable
    {
        FieldName name = FieldName.create("_target");
        DataField dataField;
        if (y instanceof RIntegerVector) {
            dataField = encoder.createDataField(name, OpType.CATEGORICAL, null, RExpUtil.getFactorLevels(y));
        } else {
            dataField = encoder.createDataField(name, OpType.CONTINUOUS, DataType.DOUBLE);
        }
        encoder.setLabel(dataField);
    }
    // Independernt variables
    for (int i = 0; i < ncat.size(); i++) {
        FieldName name = FieldName.create(xNames.getValue(i));
        DataField dataField;
        boolean categorical = ((ncat.getValue(i)).doubleValue() > 1d);
        if (categorical) {
            RStringVector levels = (RStringVector) xlevels.getValue(i);
            dataField = encoder.createDataField(name, OpType.CATEGORICAL, null, levels.getValues());
        } else {
            dataField = encoder.createDataField(name, OpType.CONTINUOUS, DataType.DOUBLE);
        }
        encoder.addFeature(dataField);
    }
}

Example 10 with DataField

use of org.dmg.pmml.DataField 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;
}