/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2011 by Rapid-I and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapid-i.com
 *
 *  This program 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.
 *
 *  This program 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 this program.  If not, see http://www.gnu.org/licenses/.
 */
package com.rapidminer.operator.learner.meta;

import java.util.LinkedList;
import java.util.List;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.tools.Tools;


/**
 * This model is created by the {@link CostBasedThresholdLearner}.
 * @author Ingo Mierswa
 */
public class ThresholdModel extends PredictionModel implements DelegationModel {

	private static final long serialVersionUID = -4224958349396815500L;

	private double[] thresholds;

	private Model innerModel;

	public ThresholdModel(ExampleSet exampleSet, Model innerModel, double[] thresholds) {
		super(exampleSet);
		this.innerModel = innerModel;
		this.thresholds = thresholds;
	}

	@Override
	public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabel) throws OperatorException {
		exampleSet = innerModel.apply(exampleSet);

		for (Example example : exampleSet) {
			int predictionIndex = (int)example.getPredictedLabel();
			String className = getLabel().getMapping().mapIndex(predictionIndex);
			double confidence = example.getConfidence(className);
			if (confidence < thresholds[predictionIndex]) {
				example.setPredictedLabel(Double.NaN);
			}
		}

		return exampleSet;
	}

	@Override
	public String toString() {
		List<String> thresholdList = new LinkedList<String>();
		for (double d : thresholds) {
			thresholdList.add(Tools.formatIntegerIfPossible(d));
		}
		return "Thresholds: " + thresholdList + Tools.getLineSeparator() + innerModel.toString(); 
	}

	@Override
	public Model getBaseModel() {
		return innerModel;
	}

	@Override
	public String getShortInfo() {
		List<String> thresholdList = new LinkedList<String>();
		for (double d : thresholds) {
			thresholdList.add(Tools.formatIntegerIfPossible(d));
		}
		return "Thresholds: " + thresholdList;
	}
}