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

Example 6 with Schema

use of org.jpmml.converter.Schema 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);
}

Example 7 with Schema

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

the class IForestConverter method encodeModel.

@Override
public Model encodeModel(Schema schema) {
    RGenericVector iForest = getObject();
    RGenericVector trees = (RGenericVector) iForest.getValue("trees");
    RDoubleVector ntree = (RDoubleVector) iForest.getValue("ntree");
    if (trees == null) {
        throw new IllegalArgumentException();
    }
    final RIntegerVector xrow = (RIntegerVector) trees.getValue("xrow");
    Schema segmentSchema = schema.toAnonymousSchema();
    List<TreeModel> treeModels = new ArrayList<>();
    for (int i = 0; i < ValueUtil.asInt(ntree.asScalar()); i++) {
        TreeModel treeModel = encodeTreeModel(trees, i, segmentSchema);
        treeModels.add(treeModel);
    }
    // "rawPathLength / avgPathLength(xrow)"
    Transformation normalizedPathLength = new AbstractTransformation() {

        @Override
        public FieldName getName(FieldName name) {
            return FieldName.create("normalizedPathLength");
        }

        @Override
        public Expression createExpression(FieldRef fieldRef) {
            return PMMLUtil.createApply("/", fieldRef, PMMLUtil.createConstant(avgPathLength(xrow.asScalar())));
        }
    };
    // "2 ^ (-1 * normalizedPathLength)"
    Transformation anomalyScore = new AbstractTransformation() {

        @Override
        public FieldName getName(FieldName name) {
            return FieldName.create("anomalyScore");
        }

        @Override
        public boolean isFinalResult() {
            return true;
        }

        @Override
        public Expression createExpression(FieldRef fieldRef) {
            return PMMLUtil.createApply("pow", PMMLUtil.createConstant(2d), PMMLUtil.createApply("*", PMMLUtil.createConstant(-1d), fieldRef));
        }
    };
    MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel())).setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, treeModels)).setOutput(ModelUtil.createPredictedOutput(FieldName.create("rawPathLength"), OpType.CONTINUOUS, DataType.DOUBLE, normalizedPathLength, anomalyScore));
    return miningModel;
}

Example 8 with Schema

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

the class XGBoostConverter method encodeModel.

@Override
public MiningModel encodeModel(Schema schema) {
    RGenericVector booster = getObject();
    RNumberVector<?> ntreeLimit = (RNumberVector<?>) booster.getValue("ntreelimit", true);
    RBooleanVector compact = (RBooleanVector) booster.getValue("compact", true);
    Learner learner = ensureLearner();
    Schema xgbSchema = XGBoostUtil.toXGBoostSchema(schema);
    MiningModel miningModel = learner.encodeMiningModel((ntreeLimit != null ? ValueUtil.asInteger(ntreeLimit.asScalar()) : null), (compact != null ? compact.asScalar() : false), xgbSchema);
    return miningModel;
}

Example 9 with Schema

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

the class RandomForestConverter method encodeClassification.

private MiningModel encodeClassification(RGenericVector forest, final Schema schema) {
    RNumberVector<?> bestvar = (RNumberVector<?>) forest.getValue("bestvar");
    RNumberVector<?> treemap = (RNumberVector<?>) forest.getValue("treemap");
    RIntegerVector nodepred = (RIntegerVector) forest.getValue("nodepred");
    RDoubleVector xbestsplit = (RDoubleVector) forest.getValue("xbestsplit");
    RIntegerVector nrnodes = (RIntegerVector) forest.getValue("nrnodes");
    RDoubleVector ntree = (RDoubleVector) forest.getValue("ntree");
    int rows = nrnodes.asScalar();
    int columns = ValueUtil.asInt(ntree.asScalar());
    final CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    ScoreEncoder<Integer> scoreEncoder = new ScoreEncoder<Integer>() {

        @Override
        public String encode(Integer value) {
            return categoricalLabel.getValue(value - 1);
        }
    };
    Schema segmentSchema = schema.toAnonymousSchema();
    List<TreeModel> treeModels = new ArrayList<>();
    for (int i = 0; i < columns; i++) {
        List<? extends Number> daughters = FortranMatrixUtil.getColumn(treemap.getValues(), 2 * rows, columns, i);
        TreeModel treeModel = encodeTreeModel(MiningFunction.CLASSIFICATION, scoreEncoder, FortranMatrixUtil.getColumn(daughters, rows, 2, 0), FortranMatrixUtil.getColumn(daughters, rows, 2, 1), FortranMatrixUtil.getColumn(nodepred.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(bestvar.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(xbestsplit.getValues(), rows, columns, i), segmentSchema);
        treeModels.add(treeModel);
    }
    MiningModel miningModel = new MiningModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel)).setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.MAJORITY_VOTE, treeModels)).setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));
    return miningModel;
}

Example 10 with Schema

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

the class RandomForestConverter method encodeRegression.

private MiningModel encodeRegression(RGenericVector forest, final Schema schema) {
    RNumberVector<?> leftDaughter = (RNumberVector<?>) forest.getValue("leftDaughter");
    RNumberVector<?> rightDaughter = (RNumberVector<?>) forest.getValue("rightDaughter");
    RDoubleVector nodepred = (RDoubleVector) forest.getValue("nodepred");
    RNumberVector<?> bestvar = (RNumberVector<?>) forest.getValue("bestvar");
    RDoubleVector xbestsplit = (RDoubleVector) forest.getValue("xbestsplit");
    RIntegerVector nrnodes = (RIntegerVector) forest.getValue("nrnodes");
    RDoubleVector ntree = (RDoubleVector) forest.getValue("ntree");
    ScoreEncoder<Double> scoreEncoder = new ScoreEncoder<Double>() {

        @Override
        public String encode(Double value) {
            return ValueUtil.formatValue(value);
        }
    };
    int rows = nrnodes.asScalar();
    int columns = ValueUtil.asInt(ntree.asScalar());
    Schema segmentSchema = schema.toAnonymousSchema();
    List<TreeModel> treeModels = new ArrayList<>();
    for (int i = 0; i < columns; i++) {
        TreeModel treeModel = encodeTreeModel(MiningFunction.REGRESSION, scoreEncoder, FortranMatrixUtil.getColumn(leftDaughter.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(rightDaughter.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(nodepred.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(bestvar.getValues(), rows, columns, i), FortranMatrixUtil.getColumn(xbestsplit.getValues(), rows, columns, i), segmentSchema);
        treeModels.add(treeModel);
    }
    MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel())).setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, treeModels));
    return miningModel;
}