tr.gov.ulakbim.jDenetX.classifiers.OCBoost.java Source code

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Here is the source code for tr.gov.ulakbim.jDenetX.classifiers.OCBoost.java

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/*
 *    OCBoost.java
 *    Copyright (C) 2008 University of Waikato, Hamilton, New Zealand
 *    @author Albert Bifet
 *
 *    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.classifiers;

import tr.gov.ulakbim.jDenetX.core.Measurement;
import tr.gov.ulakbim.jDenetX.core.SizeOf;
import tr.gov.ulakbim.jDenetX.options.ClassOption;
import tr.gov.ulakbim.jDenetX.options.FloatOption;
import tr.gov.ulakbim.jDenetX.options.IntOption;
import weka.core.Instance;
import weka.core.Utils;

public class OCBoost extends AbstractClassifier {

    private static final long serialVersionUID = 1L;

    public ClassOption baseLearnerOption = new ClassOption("baseLearner", 'l', "Classifier to train.",
            Classifier.class, "HoeffdingTree");

    public IntOption ensembleSizeOption = new IntOption("ensembleSize", 's', "The number of models to boost.", 10,
            1, Integer.MAX_VALUE);

    public FloatOption smoothingOption = new FloatOption("smoothingParameter", 'e', "Smoothing parameter.", 0.5,
            0.0, 100.0);

    protected Classifier[] ensemble;

    protected double[] alpha;

    protected double[] alphainc;

    protected double[] pipos;

    protected double[] pineg;

    protected double[][] wpos;

    protected double[][] wneg;

    @Override
    public int measureByteSize() {
        int size = (int) SizeOf.sizeOf(this);
        for (Classifier classifier : this.ensemble) {
            size += classifier.measureByteSize();
        }
        return size;
    }

    @Override
    public void resetLearningImpl() {
        this.ensemble = new Classifier[this.ensembleSizeOption.getValue()];
        this.alpha = new double[this.ensemble.length];
        this.alphainc = new double[this.ensemble.length];
        this.pipos = new double[this.ensemble.length];
        this.pineg = new double[this.ensemble.length];
        this.wpos = new double[this.ensemble.length][this.ensemble.length];
        this.wneg = new double[this.ensemble.length][this.ensemble.length];

        Classifier baseLearner = (Classifier) getPreparedClassOption(this.baseLearnerOption);
        baseLearner.resetLearning();
        for (int i = 0; i < this.ensemble.length; i++) {
            this.ensemble[i] = baseLearner.copy();
            alpha[i] = 0.0;
            alphainc[i] = 0.0;
            for (int j = 0; j < this.ensemble.length; j++) {
                wpos[i][j] = this.smoothingOption.getValue();
                wneg[i][j] = this.smoothingOption.getValue();
            }
        }
    }

    @Override
    public void trainOnInstanceImpl(Instance inst) {
        double d = 1.0;
        int[] m = new int[this.ensemble.length];
        for (int j = 0; j < this.ensemble.length; j++) {
            int j0 = 0; //max(0,j-K)
            pipos[j] = 1.0;
            pineg[j] = 1.0;
            m[j] = -1;
            if (this.ensemble[j].correctlyClassifies(inst)) {
                m[j] = 1;
            }
            for (int k = j0; k <= j - 1; k++) {
                pipos[j] *= wpos[j][k] / wpos[j][j] * Math.exp(-alphainc[k])
                        + (1.0 - wpos[j][k] / wpos[j][j]) * Math.exp(alphainc[k]);
                pineg[j] *= wneg[j][k] / wneg[j][j] * Math.exp(-alphainc[k])
                        + (1.0 - wneg[j][k] / wneg[j][j]) * Math.exp(alphainc[k]);
            }
            for (int k = 0; k <= j; k++) {
                wpos[j][k] = wpos[j][k] * pipos[j] + d * (m[k] == 1 ? 1 : 0) * (m[j] == 1 ? 1 : 0);
                wneg[j][k] = wneg[j][k] * pineg[j] + d * (m[k] == -1 ? 1 : 0) * (m[j] == -1 ? 1 : 0);
            }
            alphainc[j] = -alpha[j];
            alpha[j] = 0.5 * Math.log(wpos[j][j] / wneg[j][j]);
            alphainc[j] += alpha[j];

            d = d * Math.exp(-alpha[j] * m[j]);

            if (d > 0.0) {
                Instance weightedInst = (Instance) inst.copy();
                weightedInst.setWeight(inst.weight() * d);
                this.ensemble[j].trainOnInstance(weightedInst);
            }
        }
    }

    protected double getEnsembleMemberWeight(int i) {
        return alpha[i];
    }

    public double[] getVotesForInstance(Instance inst) {
        double[] output = new double[2];
        int vote;
        double combinedVote = 0.0;
        for (int i = 0; i < this.ensemble.length; i++) {
            vote = Utils.maxIndex(this.ensemble[i].getVotesForInstance(inst));
            if (vote == 0)
                vote = -1;
            combinedVote += (double) vote * getEnsembleMemberWeight(i);
        }
        output[0] = 0;
        output[1] = 0;
        output[combinedVote > 0 ? 1 : 0] = 1;
        return output;
    }

    public boolean isRandomizable() {
        return true;
    }

    @Override
    public void getModelDescription(StringBuilder out, int indent) {
        // TODO Auto-generated method stub

    }

    @Override
    protected Measurement[] getModelMeasurementsImpl() {
        return new Measurement[] {
                new Measurement("ensemble size", this.ensemble != null ? this.ensemble.length : 0) };
    }

    @Override
    public Classifier[] getSubClassifiers() {
        return this.ensemble.clone();
    }

}