meka.classifiers.multilabel.RAkEL.java Source code

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Here is the source code for meka.classifiers.multilabel.RAkEL.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;

import meka.core.MLUtils;
import meka.core.OptionUtils;
import meka.core.PSUtils;
import meka.core.SuperLabelUtils;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.core.*;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;

import java.util.*;

/**
 * RAkEL.java - Draws M subsets of size k from the set of labels, and trains PS upon each one, then combines label votes from these PS classifiers to get a label-vector prediction. The original RAkEL by Tsoumakas et al. was a meta method, typically taking LC (aka LP) as a classifier; this implementation is more like a combination between RAkEL and PS, making it potentially very fast (recall that PS defaults to LC with 0 pruning).
 *
 * See also <i>RAkEL</i> from the <a href=http://mulan.sourceforge.net>MULAN</a> framework.
 * @author    Jesse Read
 * @version September 2015
 */

public class RAkEL extends RAkELd {

    /** for serialization. */
    private static final long serialVersionUID = -6208337124440497991L;

    /**
     * Description to display in the GUI.
     * 
     * @return      the description
     */
    @Override
    public String globalInfo() {
        return "Draws M subsets of size k from the set of labels, and trains PS upon each one, then combines label votes from the PS classifiers to get a label-vector prediction.";
    }

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

        int L = D.classIndex();
        Random r = new Random(m_S);

        if (getDebug())
            System.out.println("Building " + m_M + " models of " + m_K + " random subsets:");

        m_InstancesTemplates = new Instances[m_M];
        kMap = new int[m_M][m_K];
        m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, m_M);
        for (int i = 0; i < m_M; i++) {
            kMap[i] = SuperLabelUtils.get_k_subset(L, m_K, r);
            if (getDebug())
                System.out.println("\tmodel " + (i + 1) + "/" + m_M + ": " + Arrays.toString(kMap[i]) + ", P=" + m_P
                        + ", N=" + m_N);
            Instances D_i = SuperLabelUtils.makePartitionDataset(D, kMap[i], m_P, m_N);
            m_Classifiers[i].buildClassifier(D_i);
            m_InstancesTemplates[i] = new Instances(D_i, 0);
        }
    }

    @Override
    public String kTipText() {
        return "The number of labels in each subset (must be at least 1 and less than the number of labels) ";
    }

    /** 
     * GetM - Get the M parameter (number of subsets).
     */
    public int getM() {
        return m_M;
    }

    /** 
     * SetM - Sets the M parameter (number of subsets)
     */
    public void setM(int M) {
        m_M = M;
    }

    public String mTipText() {
        return "The number of subsets (to run in ensemble)";
    }

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

    @Override
    public TechnicalInformation getTechnicalInformation() {
        TechnicalInformation result;
        TechnicalInformation additional;

        result = new TechnicalInformation(Type.INPROCEEDINGS);
        result.setValue(Field.AUTHOR, "Grigorios Tsoumakas and Ioannis Katakis and Ioannis Vlahavas");
        result.setValue(Field.TITLE, "Random k-Labelsets for Multi-Label Classification");
        result.setValue(Field.JOURNAL, "IEEE Transactions on Knowledge and Data Engineering");
        result.setValue(Field.VOLUME, "99");
        result.setValue(Field.NUMBER, "1");
        result.setValue(Field.YEAR, "2010");

        additional = new TechnicalInformation(Type.INPROCEEDINGS);
        additional.setValue(Field.AUTHOR, "Jesse Read, Antti Puurula, Albert Bifet");
        additional.setValue(Field.TITLE, "Multi-label Classification with Meta-labels");
        additional.setValue(Field.BOOKTITLE, "International Conference on Data Mining");
        additional.setValue(Field.YEAR, "2014");

        result.add(additional);

        return result;
    }

    @Override
    public Enumeration listOptions() {
        Vector result = new Vector();
        result.addElement(new Option("\tSets M (default 10): the number of subsets", "M", 1, "-M <num>"));
        OptionUtils.add(result, super.listOptions());
        return OptionUtils.toEnumeration(result);
    }

    @Override
    public void setOptions(String[] options) throws Exception {
        setM(OptionUtils.parse(options, 'M', 10));
        super.setOptions(options);
    }

    @Override
    public String[] getOptions() {
        List<String> result = new ArrayList<>();
        OptionUtils.add(result, 'M', getM());
        OptionUtils.add(result, super.getOptions());
        return OptionUtils.toArray(result);
    }

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

}