moa.classifiers.imbalanced.SamplingClassifier.java Source code

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Here is the source code for moa.classifiers.imbalanced.SamplingClassifier.java

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/*
 *    SamplingClassifier.java
 *    Copyright (C) 2013, Drexel University 
 *    @author Gregory Ditzler (gregory.ditzler@gmail.com)
 *
 *    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.imbalanced;

import weka.core.Instance;
import moa.classifiers.AbstractClassifier;
import moa.classifiers.Classifier;
import moa.core.Measurement;
import moa.core.MiscUtils;
import moa.options.ClassOption;
import moa.options.FlagOption;

public class SamplingClassifier extends AbstractClassifier {
    private static final long serialVersionUID = 1L;
    public ClassOption baseLearnerOption = new ClassOption("baseLearner", 'l', "Classifier to train.",
            Classifier.class, "trees.HoeffdingTree");
    public FlagOption overSampleOption = new FlagOption("overSample", 'o', "Oversample class 0.");
    public FlagOption underSampleOption = new FlagOption("underSample", 'm', "Undersample class 0.");
    public FlagOption logTransformOption = new FlagOption("logTransform", 'z', "Log(1/p)");
    public double rareCount;
    public double count;
    protected Classifier classifier;

    @Override
    public boolean isRandomizable() {
        return true;
    }

    @Override
    public double[] getVotesForInstance(Instance inst) {
        return this.classifier.getVotesForInstance(inst);
    }

    @Override
    public void resetLearningImpl() {
        Classifier baseLearner = (Classifier) getPreparedClassOption(this.baseLearnerOption);
        baseLearner.resetLearning();
        this.classifier = baseLearner.copy();
        this.rareCount = 0.0;
        this.count = 0.0;
    }

    @Override
    public void trainOnInstanceImpl(Instance inst) {
        if (inst.classIndex() == 0) {
            this.rareCount += 1.0;
        }
        this.count += 1.0;
        double w;

        if (this.overSampleOption.isSet() && inst.classIndex() == 0) {
            w = 1.0 / (this.rareCount / this.count);
            if (this.logTransformOption.isSet()) {
                w = Math.log(w);
            }
        } else if (this.underSampleOption.isSet() && inst.classIndex() != 0) {
            w = 1.0 - this.rareCount / this.count;
        } else {
            w = 1.0;
        }
        int k = MiscUtils.poisson(w, this.classifierRandom);
        Instance weightedInst = (Instance) inst.copy();
        weightedInst.setWeight(inst.weight() * k);
        this.classifier.trainOnInstance(weightedInst);
    }

    @Override
    protected Measurement[] getModelMeasurementsImpl() {
        // TODO Auto-generated method stub
        Measurement[] m = new Measurement[0];
        return m;
    }

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

    }

}