/* * Copyright (c) 2019 Villu Ruusmann * * This file is part of JPMML-SparkML * * JPMML-SparkML is free software: you can redistribute it and/or modify * it under the terms of the GNU Affero General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * JPMML-SparkML is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU Affero General Public License for more details. * * You should have received a copy of the GNU Affero General Public License * along with JPMML-SparkML. If not, see <http://www.gnu.org/licenses/>. */ package org.jpmml.sparkml.model; import org.apache.spark.ml.classification.LinearSVCModel; import org.dmg.pmml.DataType; import org.dmg.pmml.Expression; import org.dmg.pmml.FieldName; import org.dmg.pmml.FieldRef; import org.dmg.pmml.Model; import org.dmg.pmml.OpType; import org.dmg.pmml.PMMLFunctions; import org.dmg.pmml.mining.MiningModel; import org.dmg.pmml.regression.RegressionModel; import org.jpmml.converter.AbstractTransformation; import org.jpmml.converter.ModelUtil; import org.jpmml.converter.PMMLUtil; import org.jpmml.converter.Schema; import org.jpmml.converter.Transformation; import org.jpmml.converter.mining.MiningModelUtil; import org.jpmml.sparkml.ClassificationModelConverter; public class LinearSVCModelConverter extends ClassificationModelConverter<LinearSVCModel> implements HasRegressionTableOptions { public LinearSVCModelConverter(LinearSVCModel model){ super(model); } @Override public MiningModel encodeModel(Schema schema){ LinearSVCModel model = getTransformer(); Transformation transformation = new AbstractTransformation(){ @Override public Expression createExpression(FieldRef fieldRef){ return PMMLUtil.createApply(PMMLFunctions.THRESHOLD) .addExpressions(fieldRef, PMMLUtil.createConstant(model.getThreshold())); } }; Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.DOUBLE); Model linearModel = LinearModelUtil.createRegression(this, model.coefficients(), model.intercept(), segmentSchema) .setOutput(ModelUtil.createPredictedOutput(FieldName.create("margin"), OpType.CONTINUOUS, DataType.DOUBLE, transformation)); return MiningModelUtil.createBinaryLogisticClassification(linearModel, 1d, 0d, RegressionModel.NormalizationMethod.NONE, false, schema); } }