Examples with MiningModel org.dmg.pmml.mining.MiningModel used on opensource projects

Example 1 with MiningModel

use of org.dmg.pmml.mining.MiningModel 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 MiningModel

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

the class RangerConverter method encodeClassification.

private MiningModel encodeClassification(RGenericVector ranger, Schema schema) {
    RGenericVector forest = (RGenericVector) ranger.getValue("forest");
    final RStringVector levels = (RStringVector) forest.getValue("levels");
    ScoreEncoder scoreEncoder = new ScoreEncoder() {

        @Override
        public void encode(Node node, Number splitValue, RNumberVector<?> terminalClassCount) {
            int index = ValueUtil.asInt(splitValue);
            if (terminalClassCount != null) {
                throw new IllegalArgumentException();
            }
            node.setScore(levels.getValue(index - 1));
        }
    };
    List<TreeModel> treeModels = encodeForest(forest, MiningFunction.CLASSIFICATION, scoreEncoder, schema);
    MiningModel miningModel = new MiningModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(schema.getLabel())).setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.MAJORITY_VOTE, treeModels));
    return miningModel;
}

Example 3 with MiningModel

use of org.dmg.pmml.mining.MiningModel in project jpmml-sparkml by jpmml.

the class GBTRegressionModelConverter method encodeModel.

@Override
public MiningModel encodeModel(Schema schema) {
    GBTRegressionModel model = getTransformer();
    List<TreeModel> treeModels = TreeModelUtil.encodeDecisionTreeEnsemble(model, schema);
    MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel())).setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.WEIGHTED_SUM, treeModels, Doubles.asList(model.treeWeights())));
    return miningModel;
}

Example 4 with MiningModel

use of org.dmg.pmml.mining.MiningModel 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);
}

Example 5 with MiningModel

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

the class GBMConverter method encodeMultinomialClassification.

private MiningModel encodeMultinomialClassification(List<TreeModel> treeModels, Double initF, Schema schema) {
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
    List<Model> miningModels = new ArrayList<>();
    for (int i = 0, columns = categoricalLabel.size(), rows = (treeModels.size() / columns); i < columns; i++) {
        MiningModel miningModel = createMiningModel(CMatrixUtil.getColumn(treeModels, rows, columns, i), initF, segmentSchema).setOutput(ModelUtil.createPredictedOutput(FieldName.create("gbmValue(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.DOUBLE));
        miningModels.add(miningModel);
    }
    return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}