AdaptiveRandomForest-master/src/main/java/moa/evaluation/F1.java
/*
 *    F1.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 F1 extends MeasureCollection{

    @Override
    protected String[] getNames() {
        String[] names = {"F1-P","F1-R","Purity"};
        return names;
    }

//    @Override
//    protected boolean[] getDefaultEnabled() {
//        boolean [] defaults = {false, false, false};
//        return defaults;
//    }
    
    public void evaluateClustering(Clustering clustering, Clustering trueClustering, ArrayList<DataPoint> points) {

        if (clustering.size()<0){
            addValue(0,0);
            addValue(1,0);
            return;
        }

        MembershipMatrix mm = new MembershipMatrix(clustering, points);
        //System.out.println(mm.toString());

        int numClasses = mm.getNumClasses();
        if(mm.hasNoiseClass())
            numClasses--;



        //F1 as defined in P3C, try using F1 optimization
        double F1_P = 0.0;
        double purity = 0;
        int realClusters = 0;
        for (int i = 0; i < clustering.size(); i++) {
            int max_weight = 0;
            int max_weight_index = -1;

            //find max index
            for (int j = 0; j < numClasses; j++) {
                if(mm.getClusterClassWeight(i, j) > max_weight){
                    max_weight = mm.getClusterClassWeight(i, j);
                    max_weight_index = j;
                }
            }
            if(max_weight_index!=-1){
                realClusters++;
                double precision = mm.getClusterClassWeight(i, max_weight_index)/(double)mm.getClusterSum(i);
                double recall = mm.getClusterClassWeight(i, max_weight_index)/(double) mm.getClassSum(max_weight_index);
                double f1 = 0;
                if(precision > 0 || recall > 0){
                    f1 = 2*precision*recall/(precision+recall);
                }
                F1_P += f1;
                purity += precision;

                //TODO should we move setMeasure stuff into the Cluster interface?
                clustering.get(i).setMeasureValue("F1-P", Double.toString(f1));
            }
        }
        if(realClusters > 0){
            F1_P/=realClusters;
            purity/=realClusters;
        }
        addValue("F1-P",F1_P);
        addValue("Purity",purity);



        //F1 as defined in .... mainly maximizes F1 for each class
        double F1_R = 0.0;
        for (int j = 0; j < numClasses; j++) {
            double max_f1 = 0;
            for (int i = 0; i < clustering.size(); i++) {
                double precision = mm.getClusterClassWeight(i, j)/(double)mm.getClusterSum(i);
                double recall = mm.getClusterClassWeight(i, j)/(double)mm.getClassSum(j);
                double f1 = 0;
                if(precision > 0 || recall > 0){
                    f1 = 2*precision*recall/(precision+recall);
                }
                if(max_f1 < f1){
                    max_f1 = f1;
                }
            }
            F1_R+= max_f1;
        }
        F1_R/=numClasses;

        addValue("F1-R",F1_R);
    }

}
