meka.classifiers.multilabel.meta.EnsembleML.java Source code

Introduction

Here is the source code for meka.classifiers.multilabel.meta.EnsembleML.java

Source

/*
 *   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 3 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, see <http://www.gnu.org/licenses/>.
 */

package meka.classifiers.multilabel.meta;

import java.util.Random;

import meka.classifiers.multilabel.ProblemTransformationMethod;
import weka.core.Instances;
import weka.core.Randomizable;
import weka.core.RevisionUtils;

/**
 * EnsembleML.java - Combines several multi-label classifiers in a simple-subset ensemble.
 * @author Jesse Read (jmr30@cs.waikato.ac.nz)
 */

public class EnsembleML extends MetaProblemTransformationMethod {

    /** for serialization. */
    private static final long serialVersionUID = 835659467275068411L;

    /**
     * Description to display in the GUI.
     * 
     * @return      the description
     */
    @Override
    public String globalInfo() {
        return "Combining several multi-label classifiers in a simple-subset ensemble.";
    }

    @Override
    public void buildClassifier(Instances train) throws Exception {
        testCapabilities(train);

        if (getDebug())
            System.out.print("-: Models: ");

        train = new Instances(train);
        m_Classifiers = ProblemTransformationMethod.makeCopies((ProblemTransformationMethod) m_Classifier,
                m_NumIterations);
        int sub_size = (train.numInstances() * m_BagSizePercent / 100);
        for (int i = 0; i < m_NumIterations; i++) {
            if (getDebug())
                System.out.print("" + i + " ");
            if (m_Classifiers[i] instanceof Randomizable)
                ((Randomizable) m_Classifiers[i]).setSeed(i);
            train.randomize(new Random(m_Seed + i));
            Instances sub_train = new Instances(train, 0, sub_size);
            m_Classifiers[i].buildClassifier(sub_train);
        }

        if (getDebug())
            System.out.println(":-");
    }

    @Override
    public String getRevision() {
        return RevisionUtils.extract("$Revision: 9117 $");
    }

    public static void main(String args[]) {
        ProblemTransformationMethod.evaluation(new EnsembleML(), args);
    }

}