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

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

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/*
 *   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 meka.classifiers.multilabel.BR;
import meka.classifiers.multilabel.ProblemTransformationMethod;
import weka.classifiers.AbstractClassifier;
import weka.core.*;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;

/**
 * MBR.java - Meta BR: BR stacked with feature outputs into another BR.
 * Described in: Godbole and Sarawagi, <i>Discriminative Methods for Multi-labeled Classification</i>. 
 * 
 * @version   June 2009
 * @author    Jesse Read (jmr30@cs.waikato.ac.nz)
 */
public class MBR extends ProblemTransformationMethod implements TechnicalInformationHandler {

    /** for serialization. */
    private static final long serialVersionUID = 865889198021748917L;

    protected BR m_BASE = null;
    protected BR m_META = null;

    public MBR() {
        // default classifier for GUI
        this.m_Classifier = new BR();
    }

    /**
     * Description to display in the GUI.
     * 
     * @return      the description
     */
    @Override
    public String globalInfo() {
        return "BR stacked with feature outputs.\nFor more information see:\n"
                + getTechnicalInformation().toString();
    }

    @Override
    protected String defaultClassifierString() {
        return BR.class.getName();
    }

    @Override
    public TechnicalInformation getTechnicalInformation() {
        TechnicalInformation result;

        result = new TechnicalInformation(Type.INPROCEEDINGS);
        result.setValue(Field.AUTHOR, "Shantanu Godbole, Sunita Sarawagi");
        result.setValue(Field.TITLE, "Discriminative Methods for Multi-labeled Classification");
        result.setValue(Field.BOOKTITLE, "Advances in Knowledge Discovery and Data Mining");
        result.setValue(Field.YEAR, "2004");
        result.setValue(Field.PAGES, "22-30");
        result.setValue(Field.SERIES, "LNCS");

        return result;
    }

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

        int c = data.classIndex();

        // Base BR

        if (getDebug())
            System.out.println("Build BR Base (" + c + " models)");
        m_BASE = (BR) AbstractClassifier.forName(getClassifier().getClass().getName(),
                ((AbstractClassifier) getClassifier()).getOptions());
        m_BASE.buildClassifier(data);

        // Meta BR

        if (getDebug())
            System.out.println("Prepare Meta data           ");
        Instances meta_data = new Instances(data);

        FastVector BinaryClass = new FastVector(c);
        BinaryClass.addElement("0");
        BinaryClass.addElement("1");

        for (int i = 0; i < c; i++) {
            meta_data.insertAttributeAt(new Attribute("metaclass" + i, BinaryClass), c);
        }

        for (int i = 0; i < data.numInstances(); i++) {
            double cfn[] = m_BASE.distributionForInstance(data.instance(i));
            for (int a = 0; a < cfn.length; a++) {
                meta_data.instance(i).setValue(a + c, cfn[a]);
            }
        }

        meta_data.setClassIndex(c);
        m_InstancesTemplate = new Instances(meta_data, 0);

        if (getDebug())
            System.out.println("Build BR Meta (" + c + " models)");

        m_META = (BR) AbstractClassifier.forName(getClassifier().getClass().getName(),
                ((AbstractClassifier) getClassifier()).getOptions());
        m_META.buildClassifier(meta_data);
    }

    @Override
    public double[] distributionForInstance(Instance instance) throws Exception {

        int c = instance.classIndex();

        double result[] = m_BASE.distributionForInstance(instance);

        instance.setDataset(null);

        for (int i = 0; i < c; i++) {
            instance.insertAttributeAt(c);
        }

        instance.setDataset(m_InstancesTemplate);

        for (int i = 0; i < c; i++) {
            instance.setValue(c + i, result[i]);
        }

        return m_META.distributionForInstance(instance);
    }

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

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