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

Example 1 with Schema

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

the class GBMConverter method encodeModel.

@Override
public MiningModel encodeModel(Schema schema) {
    RGenericVector gbm = getObject();
    RDoubleVector initF = (RDoubleVector) gbm.getValue("initF");
    RGenericVector trees = (RGenericVector) gbm.getValue("trees");
    RGenericVector c_splits = (RGenericVector) gbm.getValue("c.splits");
    RGenericVector distribution = (RGenericVector) gbm.getValue("distribution");
    RStringVector distributionName = (RStringVector) distribution.getValue("name");
    Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
    List<TreeModel> treeModels = new ArrayList<>();
    for (int i = 0; i < trees.size(); i++) {
        RGenericVector tree = (RGenericVector) trees.getValue(i);
        TreeModel treeModel = encodeTreeModel(MiningFunction.REGRESSION, tree, c_splits, segmentSchema);
        treeModels.add(treeModel);
    }
    MiningModel miningModel = encodeMiningModel(distributionName, treeModels, initF.asScalar(), schema);
    return miningModel;
}

Example 2 with Schema

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

the class RangerConverter method encodeForest.

private List<TreeModel> encodeForest(RGenericVector forest, MiningFunction miningFunction, ScoreEncoder scoreEncoder, Schema schema) {
    RNumberVector<?> numTrees = (RNumberVector<?>) forest.getValue("num.trees");
    RGenericVector childNodeIDs = (RGenericVector) forest.getValue("child.nodeIDs");
    RGenericVector splitVarIDs = (RGenericVector) forest.getValue("split.varIDs");
    RGenericVector splitValues = (RGenericVector) forest.getValue("split.values");
    RGenericVector terminalClassCounts = (RGenericVector) forest.getValue("terminal.class.counts", true);
    Schema segmentSchema = schema.toAnonymousSchema();
    List<TreeModel> treeModels = new ArrayList<>();
    for (int i = 0; i < ValueUtil.asInt(numTrees.asScalar()); i++) {
        TreeModel treeModel = encodeTreeModel(miningFunction, scoreEncoder, (RGenericVector) childNodeIDs.getValue(i), (RNumberVector<?>) splitVarIDs.getValue(i), (RNumberVector<?>) splitValues.getValue(i), (terminalClassCounts != null ? (RGenericVector) terminalClassCounts.getValue(i) : null), segmentSchema);
        treeModels.add(treeModel);
    }
    return treeModels;
}

Example 3 with Schema

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

the class PreProcessEncoder method createSchema.

@Override
public Schema createSchema() {
    Schema schema = super.createSchema();
    schema = filter(schema);
    return schema;
}

Example 4 with Schema

use of org.jpmml.converter.Schema 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 5 with Schema

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

the class GBMConverter method encodeBinaryClassification.

private MiningModel encodeBinaryClassification(List<TreeModel> treeModels, Double initF, double coefficient, Schema schema) {
    Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
    MiningModel miningModel = createMiningModel(treeModels, initF, segmentSchema).setOutput(ModelUtil.createPredictedOutput(FieldName.create("gbmValue"), OpType.CONTINUOUS, DataType.DOUBLE));
    return MiningModelUtil.createBinaryLogisticClassification(miningModel, -coefficient, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}