/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2008 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;

import java.util.Iterator;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.tools.math.Averagable;


/**
 * Normalized absolute error is the total absolute error normalized by the error
 * simply predicting the average of the actual values.
 * 
 * @author Ingo Mierswa
 * @version $Id: NormalizedAbsoluteError.java,v 2.1 2006/04/14 07:47:17
 *          ingomierswa Exp $
 */
public class NormalizedAbsoluteError extends MeasuredPerformance {

	private static final long serialVersionUID = -3899005486051589953L;

	private Attribute predictedAttribute;

	private Attribute labelAttribute;
	
	private Attribute weightAttribute;

	private double deviationSum = 0.0d;

	private double relativeSum = 0.0d;

	private double trueLabelSum = 0.0d;

	private double exampleCounter = 0.0d;

	public NormalizedAbsoluteError() {
	}

	public NormalizedAbsoluteError(NormalizedAbsoluteError nae) {
		super(nae);
		this.deviationSum = nae.deviationSum;
		this.relativeSum = nae.relativeSum;
		this.trueLabelSum = nae.trueLabelSum;
		this.exampleCounter = nae.exampleCounter;
        this.labelAttribute = (Attribute)nae.labelAttribute.clone();
        this.predictedAttribute = (Attribute)nae.predictedAttribute.clone();
        if (nae.weightAttribute != null)
        	this.weightAttribute = (Attribute)nae.weightAttribute.clone();
	}

	public String getName() {
		return "normalized_absolute_error";
	}

	public String getDescription() {
		return "The absolute error divided by the error made if the average would have been predicted.";
	}

	public double getExampleCount() {
		return exampleCounter;
	}

	public void startCounting(ExampleSet exampleSet, boolean useExampleWeights) throws OperatorException {
		super.startCounting(exampleSet, useExampleWeights);
		if (exampleSet.size() <= 1)
			throw new UserError(null, 919, getName(), "normalized absolute error can only be calculated for test sets with more than 2 examples.");
		this.predictedAttribute = exampleSet.getAttributes().getPredictedLabel();
		this.labelAttribute = exampleSet.getAttributes().getLabel();
		if (useExampleWeights)
			this.weightAttribute = exampleSet.getAttributes().getWeight();
		this.trueLabelSum = 0.0d;
		this.deviationSum = 0.0d;
		this.relativeSum = 0.0d;
		this.exampleCounter = 0.0d;
		Iterator<Example> reader = exampleSet.iterator();
		while (reader.hasNext()) {
			Example example = reader.next();
			double label = example.getLabel();
			double weight = 1.0d;
			if (weightAttribute != null)
				weight = example.getValue(weightAttribute);
			if (!Double.isNaN(label)) {
				exampleCounter += weight;
				trueLabelSum += label * weight;
			}
		}
	}

	/** Calculates the error for the current example. */
	public void countExample(Example example) {
		double plabel;
		double label = example.getValue(labelAttribute);

		if (!predictedAttribute.isNominal()) {
			plabel = example.getValue(predictedAttribute);
		} else {
			String labelS = example.getValueAsString(labelAttribute);
			plabel = example.getConfidence(labelS);
			label = 1.0d;
		}

		double weight = 1.0d;
		if (weightAttribute != null)
			weight = example.getValue(weightAttribute);
		
		double diff = weight * Math.abs(label - plabel);
		deviationSum += diff;
		double relDiff = Math.abs(weight * label - (trueLabelSum / exampleCounter));
		relativeSum += relDiff;
	}

	public double getMikroAverage() {
		return deviationSum / relativeSum;
	}

	public double getMikroVariance() {
		return Double.NaN;
	}

	public double getFitness() {
		return -1 * getAverage();
	}

	public void buildSingleAverage(Averagable performance) {
		NormalizedAbsoluteError other = (NormalizedAbsoluteError) performance;
		this.deviationSum += other.deviationSum;
		this.relativeSum += other.relativeSum;
	}
}