moa.classifiers.OzaBagAdwin.java Source code

Introduction

Here is the source code for moa.classifiers.OzaBagAdwin.java

Source

/*
 *    OzaBagAdwin.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 moa.classifiers;

import weka.core.Instance;

import moa.core.DoubleVector;
import moa.core.Measurement;
import moa.core.MiscUtils;
import moa.core.SizeOf;
import moa.options.ClassOption;
import moa.options.IntOption;

public class OzaBagAdwin 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 in the bag.", 10,
            1, Integer.MAX_VALUE);

    protected Classifier[] ensemble;
    protected ADWIN[] ADError;

    @Override
    public int measureByteSize() {
        int size = (int) SizeOf.sizeOf(this);
        for (Classifier classifier : this.ensemble) {
            size += classifier.measureByteSize();
        }
        for (ADWIN adwin : this.ADError) {
            size += adwin.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.ADError = new ADWIN[this.ensemble.length];
        for (int i = 0; i < this.ensemble.length; i++) {
            this.ADError[i] = new ADWIN();
        }
    }

    @Override
    public void trainOnInstanceImpl(Instance inst) {
        boolean Change = false;
        for (int i = 0; i < this.ensemble.length; i++) {
            int k = MiscUtils.poisson(1.0, this.classifierRandom);
            if (k > 0) {
                Instance weightedInst = (Instance) inst.copy();
                weightedInst.setWeight(inst.weight() * k);
                this.ensemble[i].trainOnInstance(weightedInst);
            }
            boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(inst);
            double ErrEstim = this.ADError[i].getEstimation();
            if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1))
                if (this.ADError[i].getEstimation() > ErrEstim)
                    Change = true;
        }
        if (Change) {
            double max = 0.0;
            int imax = -1;
            for (int i = 0; i < this.ensemble.length; i++) {
                if (max < this.ADError[i].getEstimation()) {
                    max = this.ADError[i].getEstimation();
                    imax = i;
                }
            }
            if (imax != -1) {
                this.ensemble[imax].resetLearning();
                //this.ensemble[imax].trainOnInstance(inst);
                this.ADError[imax] = new ADWIN();
            }
        }
    }

    public double[] getVotesForInstance(Instance inst) {
        DoubleVector combinedVote = new DoubleVector();
        for (int i = 0; i < this.ensemble.length; i++) {
            DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(inst));
            if (vote.sumOfValues() > 0.0) {
                vote.normalize();
                combinedVote.addValues(vote);
            }
        }
        return combinedVote.getArrayRef();
    }

    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();
    }

}