/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2014 by RapidMiner and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapidminer.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.rules;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;

/**
 * The info gain criterion for rule learning.
 *
 * @author Ingo Mierswa
 */
public class InfoGainCriterion extends AbstractCriterion {

	private static double LOG_FACTOR = 1d / Math.log(2);

	
	public double[] getBenefit(ExampleSet coveredSet, ExampleSet uncoveredSet, String labelName) {
		double coveredAccuracy = 0.0d;
		double coveredCoverage = 0.0d;
		Attribute weightAttribute = coveredSet.getAttributes().getWeight();
		Attribute labelAttribute = coveredSet.getAttributes().getLabel();
		int labelIndex = labelAttribute.getMapping().getIndex(labelName);
		for (Example e : coveredSet) {
			double weight = 1;
			if (weightAttribute != null) {
				weight = e.getValue(weightAttribute);
			}
			coveredCoverage += weight;
			if (e.getValue(labelAttribute) == labelIndex)
				coveredAccuracy += weight;
		}

		double uncoveredAccuracy = 0.0d;
		double uncoveredCoverage = 0.0d;
		weightAttribute = uncoveredSet.getAttributes().getWeight();
		labelAttribute = uncoveredSet.getAttributes().getLabel();
		labelIndex = labelAttribute.getMapping().getIndex(labelName);
		for (Example e : uncoveredSet) {
			double weight = 1;
			if (weightAttribute != null) {
				weight = e.getValue(weightAttribute);
			}
			uncoveredCoverage += weight;
			if (e.getValue(labelAttribute) == labelIndex)
				uncoveredAccuracy += weight;
		}

		double defaultAccuracy = 
			(coveredAccuracy + uncoveredAccuracy) / 
			(coveredCoverage + uncoveredCoverage);
		double infoGain = 
			coveredAccuracy * (log2((coveredAccuracy + 1.0d) / 
			(coveredCoverage + 1.0d)) - log2(defaultAccuracy));

		return new double[] { infoGain, coveredSet.size() };
	}

	public double[] getOnlineBenefit(Example example, int labelIndex) {
		double coveredAccuracy = labelWeights[labelIndex];
		double coveredWeight = weight;
		double uncoveredAccuracy = totalLabelWeights[labelIndex] - labelWeights[labelIndex];
		double uncoveredWeight = totalWeight - weight;

		double defaultAccuracy = 
			(coveredAccuracy + uncoveredAccuracy) / 
				(coveredWeight + uncoveredWeight);
		double infoGain = 
			coveredAccuracy * (log2((coveredAccuracy + 1.0d) / 
					(coveredWeight + 1.0d)) - log2(defaultAccuracy));
		double reverseInfoGain = 
			uncoveredAccuracy * (log2((uncoveredAccuracy + 1.0d) / 
					(uncoveredWeight + 1.0d)) - log2(defaultAccuracy));
		return new double[] { infoGain, coveredWeight, reverseInfoGain, uncoveredWeight};
	}
	
	private double log2(double value) {
		return Math.log(value) * LOG_FACTOR;
	}
}