/*
 *  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.performance.cost;

import java.util.HashMap;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.performance.MeasuredPerformance;
import com.rapidminer.tools.math.Averagable;

/**
 * This performance Criterion works with given ranking costs.
 * If the real class is on the x-th rank of confidences, the 
 * costs given for that rank are added.
 * 
 * @author Sebastian Land
 */
public class RankingCriterion extends MeasuredPerformance {

	private static final long serialVersionUID = -7466139591781285005L;

	private double costs;
	private Attribute label;
	private Attribute[] confidenceAttributes;
	private double exampleCount;
	private int[] rankIntervallStarts;
	private double[] rankIntervallCost; 
	private HashMap<String, Integer> confidenceAttributesMap = new HashMap<String, Integer>();
	
	public RankingCriterion(int[] rankIntervallStarts, double[] rankIntervallCost, ExampleSet exampleSet) {
		label = exampleSet.getAttributes().getLabel();
		this.rankIntervallStarts = rankIntervallStarts;
		this.rankIntervallCost = rankIntervallCost;
		confidenceAttributes = new Attribute[label.getMapping().size()];
		int i = 0;
		for (String labelValue: label.getMapping().getValues()) {
			confidenceAttributes[i] = exampleSet.getAttributes().getSpecial(Attributes.CONFIDENCE_NAME + "_" + labelValue);
			confidenceAttributesMap.put(labelValue, i);
			i++;
		}
		costs = 0;
	}
	
	@Override
	public String getDescription() {
		return "This Criterion delievers the ranking costs";
	}

	@Override
	public String getName() {
		return "RankingCosts";
	}

	@Override
	public void countExample(Example example) {
		// finding current rank
		int indexOfCorrect = confidenceAttributesMap.get(example.getNominalValue(label));
		double confidenceOfCorrect = example.getValue(confidenceAttributes[indexOfCorrect]);
		int rank = 0;
		for (int i = 0; i < confidenceAttributes.length; i++) {
			double currentConfidence = example.getValue(confidenceAttributes[i]);
			if (currentConfidence > confidenceOfCorrect) {
				rank++;
			}
		}
		
		// getting costs for rank
		int intervallIndex = 0;
		while (intervallIndex < rankIntervallStarts.length - 1 && rankIntervallStarts[intervallIndex + 1] <= rank) {
			intervallIndex++;
		}
		if (rank >= rankIntervallStarts[0]) {
			// otherwise not defined costs: Assume 0
			costs += rankIntervallCost[intervallIndex];
		}
		exampleCount ++;
	}

	@Override
	public double getExampleCount() {
		return exampleCount;
		}

	@Override
	public double getFitness() {
		return -costs;
	}

	@Override
	protected void buildSingleAverage(Averagable averagable) {
	}

	@Override
	public double getMikroAverage() {
		return costs / exampleCount;
	}

	@Override
	public double getMikroVariance() {
		return 0;
	}


}