/* * Copyright (c) 2020 Villu Ruusmann * * This file is part of JPMML-XGBoost * * JPMML-XGBoost 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-XGBoost 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-XGBoost. If not, see <http://www.gnu.org/licenses/>. */ package org.jpmml.xgboost; import java.util.List; import org.dmg.pmml.Apply; import org.dmg.pmml.DataType; import org.dmg.pmml.Expression; import org.dmg.pmml.FieldName; import org.dmg.pmml.FieldRef; 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.FunctionTransformation; 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; public class HingeClassification extends Classification { public HingeClassification(){ super(2); } @Override public MiningModel encodeMiningModel(List<RegTree> trees, List<Float> weights, float base_score, Integer ntreeLimit, Schema schema){ Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.FLOAT); Transformation transformation = new FunctionTransformation(PMMLFunctions.THRESHOLD){ @Override public FieldName getName(FieldName name){ return FieldName.create("hinge(" + name + ")"); } @Override public Expression createExpression(FieldRef fieldRef){ Apply apply = (Apply)super.createExpression(fieldRef); apply.addExpressions(PMMLUtil.createConstant(0f)); return apply; } }; MiningModel miningModel = createMiningModel(trees, weights, base_score, ntreeLimit, segmentSchema) .setOutput(ModelUtil.createPredictedOutput(FieldName.create("xgbValue"), OpType.CONTINUOUS, DataType.FLOAT, transformation)); return MiningModelUtil.createBinaryLogisticClassification(miningModel, 1d, 0d, RegressionModel.NormalizationMethod.NONE, true, schema); } }