AdaptiveRandomForest-master/src/main/java/moa/evaluation/EntropyCollection.java
/*
 *    EntropyCollection.java
 *    Copyright (C) 2010 RWTH Aachen University, Germany
 *    @author Jansen (moa@cs.rwth-aachen.de)
 *
 *    Licensed under the Apache License, Version 2.0 (the "License");
 *    you may not use this file except in compliance with the License.
 *    You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 *    Unless required by applicable law or agreed to in writing, software
 *    distributed under the License is distributed on an "AS IS" BASIS,
 *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *    See the License for the specific language governing permissions and
 *    limitations under the License.
 *    
 *    
 */

package moa.evaluation;

import java.util.ArrayList;
import moa.cluster.Clustering;
import moa.gui.visualization.DataPoint;


public class EntropyCollection extends MeasureCollection{
    private boolean debug = false;
    private final double beta = 1;


    @Override
    protected String[] getNames() {
        String[] names = {"GT cross entropy","FC cross entropy","Homogeneity","Completeness","V-Measure","VarInformation"};
        return names;
    }

    @Override
    protected boolean[] getDefaultEnabled() {
        boolean [] defaults = {false, false, false, false, false, false};
        return defaults;
    }

    @Override
    public void evaluateClustering(Clustering fclustering, Clustering hClustering, ArrayList<DataPoint> points) throws Exception {

        MembershipMatrix mm = new MembershipMatrix(fclustering, points);
        int numClasses = mm.getNumClasses();
        int numCluster = fclustering.size()+1;
        int n = mm.getTotalEntries();


        double FCentropy = 0;
        if(numCluster > 1){
            for (int fc = 0; fc < numCluster; fc++){
                double weight = mm.getClusterSum(fc)/(double)n;
                if(weight > 0)
                    FCentropy+= weight * Math.log10(weight);
            }
            FCentropy/=(-1*Math.log10(numCluster));
        }
        if(debug){
            System.out.println("FC entropy "+FCentropy);
        }

        double GTentropy = 0;
        if(numClasses > 1){
            for (int hc = 0; hc < numClasses; hc++){
                double weight = mm.getClassSum(hc)/(double)n;
                if(weight > 0)
                    GTentropy+= weight * Math.log10(weight);
            }
            GTentropy/=(-1*Math.log10(numClasses));
        }
        if(debug){
            System.out.println("GT entropy "+GTentropy);
        }


        //cluster based entropy
        double FCcrossEntropy = 0;
        
        for (int fc = 0; fc < numCluster; fc++){
            double e = 0;
            int clusterWeight = mm.getClusterSum(fc);
            if(clusterWeight>0){
                for (int hc = 0; hc < numClasses; hc++) {
                    double p = mm.getClusterClassWeight(fc, hc)/(double)clusterWeight;
                    if(p!=0){
                        e+=p * Math.log10(p);
                    }
                }
                FCcrossEntropy+=((clusterWeight/(double)n) * e);
            }
        }
        if(numCluster > 1){
            FCcrossEntropy/=-1*Math.log10(numCluster);
        }

        addValue("FC cross entropy", 1-FCcrossEntropy);
        if(debug){
            System.out.println("FC cross entropy "+(1-FCcrossEntropy));
        }


        //class based entropy
        double GTcrossEntropy = 0;
        for (int hc = 0; hc < numClasses; hc++){
            double e = 0;
            int classWeight = mm.getClassSum(hc);
            if(classWeight>0){
                for (int fc = 0; fc < numCluster; fc++) {
                    double p = mm.getClusterClassWeight(fc, hc)/(double)classWeight;
                    if(p!=0){
                        e+=p * Math.log10(p);
                    }
                }
            }
            GTcrossEntropy+=((classWeight/(double)n) * e);
        }
        if(numClasses > 1)
            GTcrossEntropy/=-1*Math.log10(numClasses);
        addValue("GT cross entropy", 1-GTcrossEntropy);
        if(debug){
            System.out.println("GT cross entropy "+(1-GTcrossEntropy));
        }

        double homogeneity;
        if(FCentropy == 0)
            homogeneity = 1;
        else
            homogeneity = 1 - FCcrossEntropy/FCentropy;

        //TODO set err values for now, needs to be debugged
        if(homogeneity > 1 || homogeneity < 0)
            addValue("Homogeneity",-1);
        else
            addValue("Homogeneity",homogeneity);

        double completeness;
        if(GTentropy == 0)
            completeness = 1;
        else
            completeness = 1 - GTcrossEntropy/GTentropy;
        addValue("Completeness",completeness);

        double vmeasure = (1+beta)*homogeneity*completeness/(beta*homogeneity+completeness);

        if(vmeasure > 1 || homogeneity < 0)
            addValue("V-Measure",-1);
        else
            addValue("V-Measure",vmeasure);



        double mutual = 0;
        for (int i = 0; i < numCluster; i++){
                for (int j = 0; j < numClasses; j++) {
                   if(mm.getClusterClassWeight(i, j)==0) continue;
                   double m = Math.log10(mm.getClusterClassWeight(i, j)/(double)mm.getClusterSum(i)/(double)mm.getClassSum(j)*(double)n);
                   m*= mm.getClusterClassWeight(i, j)/(double)n;
                   if(debug)
                        System.out.println("("+j+"/"+ j + "): "+m);
                   mutual+=m;
                }
        }
        if(numClasses > 1)
            mutual/=Math.log10(numClasses);

        double varInfo = 1;
        if(FCentropy + GTentropy > 0)
            varInfo = 2*mutual/(FCentropy + GTentropy);
        
        if(debug)
            System.out.println("mutual "+mutual+ " / VI "+varInfo);
        addValue("VarInformation", varInfo);

    }

}
