OzaBoost.java

/*
 *    OzaBoost.java
 *    Copyright (C) 2007 University of Waikato, Hamilton, New Zealand
 *    @author Richard Kirkby (rkirkby@cs.waikato.ac.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.classifiers;

import tr.gov.ulakbim.jDenetX.core.DoubleVector;
import tr.gov.ulakbim.jDenetX.core.Measurement;
import tr.gov.ulakbim.jDenetX.core.MiscUtils;
import tr.gov.ulakbim.jDenetX.core.SizeOf;
import tr.gov.ulakbim.jDenetX.options.ClassOption;
import tr.gov.ulakbim.jDenetX.options.FlagOption;
import tr.gov.ulakbim.jDenetX.options.IntOption;
import weka.core.Instance;

public class OzaBoost 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 FlagOption pureBoostOption = new FlagOption("pureBoost", 'p',
            "Boost with weights only; no poisson.");

    protected Classifier[] ensemble;

    protected double[] scms;

    protected double[] swms;

    @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()];
        Classifier baseLearner = (Classifier) getPreparedClassOption(this.baseLearnerOption);
        baseLearner.resetLearning();
        for (int i = 0; i < this.ensemble.length; i++) {
            this.ensemble[i] = baseLearner.copy();
        }
        this.scms = new double[this.ensemble.length];
        this.swms = new double[this.ensemble.length];
    }

    @Override
    public void trainOnInstanceImpl(Instance inst) {
        double lambda_d = 1.0;
        for (int i = 0; i < this.ensemble.length; i++) {
            double k = this.pureBoostOption.isSet() ? lambda_d : MiscUtils
                    .poisson(lambda_d, this.classifierRandom);
            if (k > 0.0) {
                Instance weightedInst = (Instance) inst.copy();
                weightedInst.setWeight(inst.weight() * k);
                this.ensemble[i].trainOnInstance(weightedInst);
            }
            if (this.ensemble[i].correctlyClassifies(inst)) {
                this.scms[i] += lambda_d;
                lambda_d *= this.trainingWeightSeenByModel / (2 * this.scms[i]);
            } else {
                this.swms[i] += lambda_d;
                lambda_d *= this.trainingWeightSeenByModel / (2 * this.swms[i]);
            }
        }
    }

    protected double getEnsembleMemberWeight(int i) {
        double em = this.swms[i] / (this.scms[i] + this.swms[i]);
        if ((em == 0.0) || (em > 0.5)) {
            return 0.0;
        }
        double Bm = em / (1.0 - em);
        return Math.log(1.0 / Bm);
    }

    public double[] getVotesForInstance(Instance inst) {
        DoubleVector combinedVote = new DoubleVector();
        for (int i = 0; i < this.ensemble.length; i++) {
            double memberWeight = getEnsembleMemberWeight(i);
            if (memberWeight > 0.0) {

                DoubleVector vote = new DoubleVector(this.ensemble[i]
                        .getVotesForInstance(inst));
                if (vote.sumOfValues() > 0.0) {
                    vote.normalize();
                    vote.scaleValues(memberWeight);
                    combinedVote.addValues(vote);
                }
            } else {
                break;
            }
        }
        return combinedVote.getArrayRef();
    }

    public boolean isRandomizable() {
        return true;
    }

    @Override
    public void getModelDescription(StringBuilder out, int indent) {
    }

    @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();
    }
}