F1.java

package com.yahoo.labs.samoa.evaluation.measures;

/*
 * #%L
 * SAMOA
 * %%
 * Copyright (C) 2010 RWTH Aachen University, Germany
 * %%
 * 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.
 * #L%
 */

import com.yahoo.labs.samoa.moa.cluster.Clustering;
import com.yahoo.labs.samoa.moa.evaluation.MeasureCollection;
import com.yahoo.labs.samoa.moa.evaluation.MembershipMatrix;
import com.yahoo.labs.samoa.moa.core.DataPoint;
import java.util.ArrayList;


public class F1 extends MeasureCollection{

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

    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);
    }

}