meka.classifiers.multilabel.HASEL.java Source code

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Here is the source code for meka.classifiers.multilabel.HASEL.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.SuperLabelUtils;
import weka.classifiers.AbstractClassifier;
import weka.core.Instances;
import weka.core.RevisionUtils;

import java.util.Arrays;

/**
 * HASEL - Partitions labels into subsets based on the dataset defined hierarchy.
 * Note: assuming that a <code>.</code> (fullstop/period) in the attribute names defines hierarchical branches, e.g., <code>Europe.Spain</code>.
 * @author    Jesse Read 
 * @version June 2014
 */
public class HASEL extends RAkELd {

    /** for serialization. */
    private static final long serialVersionUID = -6208388889440497988L;

    /**
     * Description to display in the GUI.
     * 
     * @return      the description
     */
    @Override
    public String globalInfo() {
        return "Partitions labels into subsets based on the dataset defined hierarchy (assuming that a '.' in the attribute names defines hierarchical branches, e.g., \"Europe.Spain\").";
    }

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

        int L = D.classIndex();
        int N = D.numInstances();

        // Get partition from dataset hierarchy
        kMap = SuperLabelUtils.getPartitionFromDatasetHierarchy(D);
        m_M = kMap.length;
        m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, m_M);
        m_InstancesTemplates = new Instances[m_M];

        for (int i = 0; i < m_M; i++) {

            if (getDebug())
                System.out.println("Building model " + (i + 1) + "/" + m_M + ": " + Arrays.toString(kMap[i]));
            Instances D_i = SuperLabelUtils.makePartitionDataset(D, kMap[i]);
            m_Classifiers[i].buildClassifier(D_i);
            m_InstancesTemplates[i] = new Instances(D_i, 0);
        }

    }

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

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

}