meka.classifiers.multilabel.MajorityLabelset.java Source code

Introduction

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

import java.util.HashMap;

import weka.core.Instance;
import weka.core.Instances;
import meka.core.MLUtils;
import meka.classifiers.multitarget.MultiTargetClassifier;
import weka.core.RevisionUtils;

/**
 * MajorityLabelset.java - The most simplest multi-label classifier. 
 * <p>Predicts the most common labelset from the training data for <i>all</i> test instances.</p>
 * 
 * @author    Jesse Read
 * @version September 2015
 */
public class MajorityLabelset extends AbstractMultiLabelClassifier implements MultiTargetClassifier {

    /** for serialization. */
    private static final long serialVersionUID = -5932291001079843869L;

    protected double prediction[] = null;
    protected HashMap<String, Double> classFreqs = new HashMap<String, Double>();

    protected double maxValue = 0.0;

    /**
     * Description to display in the GUI.
     * 
     * @return      the description
     */
    @Override
    public String globalInfo() {
        return "Majority Labelset Classifier: Always predict the combination of labels which occurs most frequently in the training set.";
    }

    protected void updateCount(Instance x, int L) {
        String y = MLUtils.toBitString(x, L);

        if (classFreqs.containsKey(y)) {
            double freq = classFreqs.get(y) + x.weight();
            classFreqs.put(y, freq);
            if (maxValue < freq) {
                maxValue = freq;
                this.prediction = MLUtils.fromBitString(y);
            }
        } else {
            classFreqs.put(y, x.weight());
        }
    }

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

        int L = D.classIndex();
        this.prediction = new double[L];

        for (int i = 0; i < D.numInstances(); i++) {
            updateCount(D.instance(i), L);
        }

    }

    @Override
    public double[] distributionForInstance(Instance test) throws Exception {
        return prediction;
    }

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

}