org.openml.webapplication.fantail.dc.statistical.AttributeEntropy.java Source code

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Here is the source code for org.openml.webapplication.fantail.dc.statistical.AttributeEntropy.java

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/*
 *  Webapplication - Java library that runs on OpenML servers
 *  Copyright (C) 2014 
 *  @author Jan N. van Rijn (j.n.van.rijn@liacs.leidenuniv.nl)
 *  @author Quan Sun (quan.sun.nz@gmail.com)
 *  
 *  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 org.openml.webapplication.fantail.dc.statistical;

import java.util.HashMap;
import java.util.Map;

import org.apache.commons.math3.stat.StatUtils;
import org.openml.webapplication.fantail.dc.Characterizer;
import org.openml.webapplication.fantail.dc.DCUntils;

import weka.core.Instances;

public class AttributeEntropy extends Characterizer {

    protected static final String[] ids = new String[] { "ClassEntropy", "MeanAttributeEntropy",
            "MeanMutualInformation", "EquivalentNumberOfAtts", "NoiseToSignalRatio",

            "MinAttributeEntropy", "MinMutualInformation",

            "MaxAttributeEntropy", "MaxMutualInformation",

            "Quartile1AttributeEntropy", "Quartile1MutualInformation",

            "Quartile2AttributeEntropy", "Quartile2MutualInformation",

            "Quartile3AttributeEntropy", "Quartile3MutualInformation", };

    @Override
    public String[] getIDs() {
        return ids;
    }

    @Override
    public Map<String, Double> characterize(Instances data) {
        int nominal_count = 0;
        for (int i = 0; i < data.numAttributes(); ++i) {
            if (data.attribute(i).isNominal() && data.classIndex() != i) {
                nominal_count += 1;
            }
        }

        Map<String, Double> qualities = new HashMap<String, Double>();
        if (data.classAttribute().isNominal()) {
            double classEntropy = DCUntils.computeClassEntropy(data);
            double[] attEntropy = DCUntils.computeAttributeEntropy(data);
            double[] mutualInformation = DCUntils.computeMutualInformation(data);

            double meanMI = StatUtils.mean(mutualInformation);
            double meanAttEntropy = nominal_count > 0 ? StatUtils.mean(attEntropy) : -1;

            double noiseSignalRatio;
            double ena = 0;

            if (meanMI <= 0) {
                ena = -1;
                noiseSignalRatio = -1;
            } else {
                ena = classEntropy / meanMI;
                noiseSignalRatio = (meanAttEntropy - meanMI) / meanMI;
            }

            qualities.put(ids[0], classEntropy);
            qualities.put(ids[1], meanAttEntropy);
            qualities.put(ids[2], meanMI);
            qualities.put(ids[3], ena);
            qualities.put(ids[4], noiseSignalRatio);

            qualities.put(ids[5], StatUtils.min(attEntropy));
            qualities.put(ids[6], StatUtils.min(mutualInformation));

            qualities.put(ids[7], StatUtils.max(attEntropy));
            qualities.put(ids[8], StatUtils.max(mutualInformation));

            qualities.put(ids[9], StatUtils.percentile(attEntropy, 25));
            qualities.put(ids[10], StatUtils.percentile(mutualInformation, 25));

            qualities.put(ids[11], StatUtils.percentile(attEntropy, 50));
            qualities.put(ids[12], StatUtils.percentile(mutualInformation, 50));

            qualities.put(ids[13], StatUtils.percentile(attEntropy, 75));
            qualities.put(ids[14], StatUtils.percentile(mutualInformation, 75));
        } else { // numeric target
            for (int i = 0; i < ids.length; ++i) {
                qualities.put(ids[i], -1.0);
            }
        }
        return qualities;
    }
}