/**
 * Copyright (C) 2001-2017 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.performance;

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


/**
 * Computes either the Spearman (rho) or Kendall (tau-b) rank correlation between the actual label
 * and predicted values of an example set. Since ranking is involved, neither correlation is
 * averageable.
 * 
 * @author Paul Rubin
 */
public class RankCorrelation extends MeasuredPerformance {

	private static final long serialVersionUID = 6908369375703683363L;

	public static final String[] NAMES = { "spearman_rho", "kendall_tau" };

	public static final String[] DESCRIPTIONS = {
			"The rank correlation between the actual and predicted labels, using Spearman's rho.",
			"The rank correlation between the actual and predicted labels, using Kendall's tau-b." };

	public static final int RHO = 0;
	public static final int TAU = 1;

	private double counter = 0; // example count

	private double value = Double.NaN;

	private int type = RHO;

	/** Default constructor */
	public RankCorrelation() {
		this(RHO);
	}

	/**
	 * Constructor with user-specified choice of correlation coefficient. User specifies RHO or TAU.
	 * 
	 * @param type
	 *            coefficient type with coefficient choice
	 */
	public RankCorrelation(int type) {
		this.type = type;
	}

	public RankCorrelation(RankCorrelation rc) {
		super(rc);
		this.type = rc.type;
		this.value = rc.value;
		this.counter = rc.counter;
	}

	/** Does nothing. Everything is done in {@link #startCounting(ExampleSet, boolean)}. */
	@Override
	public void countExample(Example example) {}

	@Override
	public String getDescription() {
		return DESCRIPTIONS[type];
	}

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

	@Override
	public double getFitness() {
		return getMikroAverage();
	}

	/**
	 * Averaging across instances of RankCorrelation is unsupported (?) For now just build the usual
	 * average by summing up the values...
	 */
	@Override
	protected void buildSingleAverage(Averagable averagable) {
		RankCorrelation other = (RankCorrelation) averagable;
		this.counter += other.counter;
		this.value += other.value;
	}

	@Override
	public double getMikroAverage() {
		return value;
	}

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

	@Override
	public String getName() {
		return NAMES[type];
	}

	/** Computes whichever of rho and tau was requested. */
	@Override
	public void startCounting(ExampleSet eSet, boolean useExampleWeights) throws OperatorException {
		super.startCounting(eSet, useExampleWeights);
		this.counter = eSet.size();
		if (type == RHO) {
			this.value = RankStatistics.rho(eSet, eSet.getAttributes().getLabel(), eSet.getAttributes().getPredictedLabel());
		} else {
			this.value = RankStatistics.tau_b(eSet, eSet.getAttributes().getLabel(), eSet.getAttributes()
					.getPredictedLabel());
		}

	}
}