ContinuousDomain.java

/*
 * Copyright (c) 2016 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 sklearn2pmml.decoration;

import java.util.ArrayList;
import java.util.List;

import org.dmg.pmml.Interval;
import org.dmg.pmml.OpType;
import org.jpmml.converter.ContinuousFeature;
import org.jpmml.converter.Feature;
import org.jpmml.converter.ValidValueDecorator;
import org.jpmml.converter.ValueUtil;
import org.jpmml.converter.WildcardFeature;
import org.jpmml.sklearn.ClassDictUtil;
import org.jpmml.sklearn.SkLearnEncoder;

public class ContinuousDomain extends Domain {

	public ContinuousDomain(String module, String name){
		super(module, name);
	}

	@Override
	public OpType getOpType(){
		return OpType.CONTINUOUS;
	}

	@Override
	public List<Feature> encodeFeatures(List<Feature> features, SkLearnEncoder encoder){
		List<? extends Number> dataMin = getDataMin();
		List<? extends Number> dataMax = getDataMax();

		ClassDictUtil.checkSize(features, dataMin, dataMax);

		List<Feature> result = new ArrayList<>();

		for(int i = 0; i < features.size(); i++){
			WildcardFeature wildcardFeature = (WildcardFeature)features.get(i);

			ContinuousFeature continuousFeature = wildcardFeature.toContinuousFeature();

			Interval interval = new Interval(Interval.Closure.CLOSED_CLOSED)
				.setLeftMargin(ValueUtil.asDouble(dataMin.get(i)))
				.setRightMargin(ValueUtil.asDouble(dataMax.get(i)));

			ValidValueDecorator validValueDecorator = new ValidValueDecorator()
				.addIntervals(interval);

			encoder.addDecorator(continuousFeature.getName(), validValueDecorator);

			result.add(continuousFeature);
		}

		return super.encodeFeatures(result, encoder);
	}

	public List<? extends Number> getDataMin(){
		return (List)ClassDictUtil.getArray(this, "data_min_");
	}

	public List<? extends Number> getDataMax(){
		return (List)ClassDictUtil.getArray(this, "data_max_");
	}
}