/*
* Copyright (c) 2015 Villu Ruusmann
*
* This file is part of JPMML-SkLearn
*
* JPMML-SkLearn 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-SkLearn 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-SkLearn. If not, see <http://www.gnu.org/licenses/>.
*/
package sklearn.ensemble.bagging;
import java.util.List;
import org.dmg.pmml.MiningFunction;
import org.dmg.pmml.mining.MiningModel;
import org.dmg.pmml.mining.Segmentation;
import org.jpmml.converter.Schema;
import sklearn.Regressor;
import sklearn.ensemble.EnsembleRegressor;
public class BaggingRegressor extends EnsembleRegressor {
public BaggingRegressor(String module, String name){
super(module, name);
}
@Override
public MiningModel encodeModel(Schema schema){
List<? extends Regressor> estimators = getEstimators();
List<List<Integer>> estimatorsFeatures = getEstimatorsFeatures();
MiningModel miningModel = BaggingUtil.encodeBagging(estimators, estimatorsFeatures, Segmentation.MultipleModelMethod.AVERAGE, MiningFunction.REGRESSION, schema);
return miningModel;
}
public List<List<Integer>> getEstimatorsFeatures(){
List<?> estimatorsFeatures = (List)get("estimators_features_");
return BaggingUtil.transformEstimatorsFeatures(estimatorsFeatures);
}
}