WindowClassificationPerformanceEvaluator.java

/*
 *    WindowClassificationPerformanceEvaluator.java
 *    Copyright (C) 2007 University of Waikato, Hamilton, New Zealand
 *    @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 *
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 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 General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */
package tr.gov.ulakbim.jDenetX.evaluation;

import tr.gov.ulakbim.jDenetX.AbstractMOAObject;
import tr.gov.ulakbim.jDenetX.core.Measurement;
import weka.core.Utils;

public class WindowClassificationPerformanceEvaluator extends AbstractMOAObject
        implements ClassificationPerformanceEvaluator {

    private static final long serialVersionUID = 1L;

//	public IntOption widthOption = new IntOption("width",
//			'w', "Size of Window", 1000);

    protected double TotalweightObserved = 0;

    protected int width;

    protected Estimator weightObserved;

    protected Estimator weightCorrect;

    protected Estimator[] columnKappa;

    protected Estimator[] rowKappa;

    protected int numClasses;

    public class Estimator {
        protected double[] window;
        protected int posWindow;
        protected int lenWindow;
        protected int SizeWindow;
        protected double sum;

        public Estimator(int sizeWindow) {
            window = new double[sizeWindow];
            SizeWindow = sizeWindow;
            posWindow = 0;
        }

        public void add(double value) {
            sum -= window[posWindow];
            sum += value;
            window[posWindow] = value;
            posWindow++;
            if (posWindow == SizeWindow)
                posWindow = 0;
        }

        public double total() {
            return sum;
        }
    }

    public void setWindowWidth(int w) {
        this.width = w;
        reset();
    }

    public void reset() {
        reset(this.numClasses);
    }

    public void reset(int numClasses) {
        this.numClasses = numClasses;
        this.rowKappa = new Estimator[numClasses];
        this.columnKappa = new Estimator[numClasses];
        for (int i = 0; i < this.numClasses; i++) {
            this.rowKappa[i] = new Estimator(this.width);
            this.columnKappa[i] = new Estimator(this.width);
        }
        this.weightCorrect = new Estimator(this.width);
        this.weightObserved = new Estimator(this.width);
        this.TotalweightObserved = 0;
    }

    public void addClassificationAttempt(int trueClass, double[] classVotes,
                                         double weight) {
        if (weight > 0.0) {
            if (TotalweightObserved == 0) {
                reset(classVotes.length > 1 ? classVotes.length : 2);
            }
            this.TotalweightObserved += weight;
            this.weightObserved.add(weight);
            int predictedClass = Utils.maxIndex(classVotes);
            if (predictedClass == trueClass) {
                this.weightCorrect.add(weight);
            } else {
                this.weightCorrect.add(0);
            }
            //Add Kappa statistic information
            for (int i = 0; i < this.numClasses; i++) {
                this.rowKappa[i].add(i == predictedClass ? weight : 0);
                this.columnKappa[i].add(i == trueClass ? weight : 0);
            }

        }
    }

    public Measurement[] getPerformanceMeasurements() {
        return new Measurement[]{
                new Measurement("classified instances",
                        this.TotalweightObserved),
                new Measurement("classifications correct (percent)",
                        getFractionCorrectlyClassified() * 100.0),
                new Measurement("Kappa Statistic (percent)",
                        getKappaStatistic() * 100.0)};

    }

    public double getTotalWeightObserved() {
        return this.weightObserved.total();
    }

    public double getFractionCorrectlyClassified() {
        return this.weightObserved.total() > 0.0 ? (double) this.weightCorrect.total()
                / this.weightObserved.total() : 0.0;
    }

    public double getKappaStatistic() {
        if (this.weightObserved.total() > 0.0) {
            double p0 = this.weightCorrect.total() / this.weightObserved.total();
            double pc = 0;
            for (int i = 0; i < this.numClasses; i++) {
                pc += (this.rowKappa[i].total() / this.weightObserved.total()) *
                        (this.columnKappa[i].total() / this.weightObserved.total());
            }
            return (p0 - pc) / (1 - pc);
        } else {
            return 0;
        }
    }

    public double getFractionIncorrectlyClassified() {
        return 1.0 - getFractionCorrectlyClassified();
    }

    public void getDescription(StringBuilder sb, int indent) {
        Measurement.getMeasurementsDescription(getPerformanceMeasurements(),
                sb, indent);
    }

}