/* * Copyright (c) 2016 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.DataType; import org.dmg.pmml.FieldName; import org.dmg.pmml.OpType; import org.dmg.pmml.mining.MiningModel; import org.dmg.pmml.regression.RegressionModel; import org.jpmml.converter.ModelUtil; import org.jpmml.converter.Schema; import org.jpmml.converter.mining.MiningModelUtil; public class LogisticRegression extends Regression { @Override public float probToMargin(float value){ return inverseLogit(value); } @Override public MiningModel encodeMiningModel(List<RegTree> trees, List<Float> weights, float base_score, Integer ntreeLimit, Schema schema){ Schema segmentSchema = schema.toAnonymousSchema(); MiningModel miningModel = createMiningModel(trees, weights, base_score, ntreeLimit, segmentSchema) .setOutput(ModelUtil.createPredictedOutput(FieldName.create("xgbValue"), OpType.CONTINUOUS, DataType.FLOAT)); return MiningModelUtil.createRegression(miningModel, RegressionModel.NormalizationMethod.LOGIT, schema); } }