/*
* Copyright (c) 2017 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 GeneralizedLinearRegression extends Regression {
@Override
public float probToMargin(float value){
return inverseExp(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.EXP, schema);
}
}
