source: trunk/CrossPare/src/de/ugoe/cs/cpdp/eval/AbstractWekaEvaluation.java @ 39

package de.ugoe.cs.cpdp.eval;

import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.PrintWriter;
import java.util.ArrayList;
import java.util.LinkedList;
import java.util.List;

import de.ugoe.cs.cpdp.training.ITrainer;
import de.ugoe.cs.cpdp.training.IWekaCompatibleTrainer;
import de.ugoe.cs.util.StringTools;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.Attribute;
import weka.core.Instances;

/**
 * Base class for the evaluation of results of classifiers compatible with the {@link Classifier} interface.
 * For each classifier, the following metrics are calculated:
 * <ul>
 *  <li>succHe: Success with recall>0.7, precision>0.5</li>
 *  <li>succZi: Success with recall>0.7, precision>0.7</li>
 *  <li>succG75: Success with gscore>0.75</li>
 *  <li>succG60: Success with gscore>0.6</li>
 *  <li>error</li>
 *  <li>recall</li>
 *  <li>precision</li>
 *  <li>fscore</li>
 *  <li>gscore</li>
 *  <li>AUC</li>
 *  <li>AUCEC (weighted by LOC, if applicable; 0.0 if LOC not available)</li>
 *  <li>tpr: true positive rate</li>
 *  <li>tnr: true negative rate</li>
 *  <li>tp: true positives</li>
 *  <li>fp: false positives</li>
 *  <li>tn: true negatives</li>
 *  <li>fn: false negatives</li>
 *  <li>errortrain: training error</li>
 *  <li>recalltrain: training recall</li>
 *  <li>precisiontrain: training precision</li>
 *  <li>succHetrain: training success with recall>0.7 and precision>0.5
 * </ul> 
 * @author Steffen Herbold
 */
public abstract class AbstractWekaEvaluation implements IEvaluationStrategy {

        /**
         * writer for the evaluation results
         */
        private PrintWriter output = new PrintWriter(System.out);
        
        private boolean outputIsSystemOut = true;
        
        /**
         * Creates the weka evaluator. Allows the creation of the evaluator in different ways, e.g., for cross-validation
         * or evaluation on the test data.
         * @param testdata test data
         * @param classifier classifier used
         * @return evaluator
         */
        protected abstract Evaluation createEvaluator(Instances testdata, Classifier classifier);
        
        /*
         * (non-Javadoc)
         * @see de.ugoe.cs.cpdp.eval.EvaluationStrategy#apply(weka.core.Instances, weka.core.Instances, java.util.List, boolean)
         */
        @Override
        public void apply(Instances testdata, Instances traindata, List<ITrainer> trainers,
                        boolean writeHeader) {
                final List<Classifier> classifiers = new LinkedList<Classifier>();
                for( ITrainer trainer : trainers ) {
                        if( trainer instanceof IWekaCompatibleTrainer ) {
                                classifiers.add(((IWekaCompatibleTrainer) trainer).getClassifier());
                        } else {
                                throw new RuntimeException("The selected evaluator only support Weka classifiers");
                        }
                }
                
                if( writeHeader ) {
                        output.append("version,size_test,size_training");
                        for( ITrainer trainer : trainers ) {
                                output.append(",succHe_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",succZi_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",succG75_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",succG60_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",error_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",recall_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",precision_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",fscore_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",gscore_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",mcc_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",auc_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",aucec_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",tpr_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",tnr_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",tp_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",fn_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",tn_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",fp_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",trainerror_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",trainrecall_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",trainprecision_" + ((IWekaCompatibleTrainer) trainer).getName());
                                output.append(",trainsuccHe_" + ((IWekaCompatibleTrainer) trainer).getName());
                        }
                        output.append(StringTools.ENDLINE);
                }
                
                output.append(testdata.relationName());
                output.append("," + testdata.numInstances());   
                output.append("," + traindata.numInstances());
                
                Evaluation eval = null;
                Evaluation evalTrain = null;
                for( Classifier classifier : classifiers ) {
                        eval = createEvaluator(testdata, classifier);
                        evalTrain = createEvaluator(traindata, classifier);
                        
                        double pf = eval.numFalsePositives(1)/(eval.numFalsePositives(1)+eval.numTrueNegatives(1));
                        double gmeasure = 2*eval.recall(1)*(1.0-pf)/(eval.recall(1)+(1.0-pf));
                        double mcc = (eval.numTruePositives(1)*eval.numTrueNegatives(1)-eval.numFalsePositives(1)*eval.numFalseNegatives(1))/Math.sqrt((eval.numTruePositives(1)+eval.numFalsePositives(1))*(eval.numTruePositives(1)+eval.numFalseNegatives(1))*(eval.numTrueNegatives(1)+eval.numFalsePositives(1))*(eval.numTrueNegatives(1)+eval.numFalseNegatives(1)));
                        double aucec = calculateReviewEffort(testdata, classifier);
                        
                        if( eval.recall(1)>=0.7 && eval.precision(1) >= 0.5 ) {
                                output.append(",1");
                        } else {
                                output.append(",0");
                        }
                        
                        if( eval.recall(1)>=0.7 && eval.precision(1) >= 0.7 ) {
                                output.append(",1");
                        } else {
                                output.append(",0");
                        }
                        
                        if( gmeasure>0.75 ) {
                                output.append(",1");
                        } else {
                                output.append(",0");
                        }
                        
                        if( gmeasure>0.6 ) {
                                output.append(",1");
                        } else {
                                output.append(",0");
                        }
                        
                        output.append("," + eval.errorRate());
                        output.append("," + eval.recall(1));
                        output.append("," + eval.precision(1));
                        output.append("," + eval.fMeasure(1));
                        output.append("," + gmeasure);
                        output.append("," + mcc);
                        output.append("," + eval.areaUnderROC(1));
                        output.append("," + aucec);
                        output.append("," + eval.truePositiveRate(1));
                        output.append("," + eval.trueNegativeRate(1));
                        output.append("," + eval.numTruePositives(1));
                        output.append("," + eval.numFalseNegatives(1));
                        output.append("," + eval.numTrueNegatives(1));
                        output.append("," + eval.numFalsePositives(1));
                        output.append("," + evalTrain.errorRate());
                        output.append("," + evalTrain.recall(1));
                        output.append("," + evalTrain.precision(1));
                        if( evalTrain.recall(1)>=0.7 && evalTrain.precision(1) >= 0.5 ) {
                                output.append(",1");
                        } else {
                                output.append(",0");
                        }
                }
                
                output.append(StringTools.ENDLINE);
                output.flush();
        }
        
        private double calculateReviewEffort(Instances testdata, Classifier classifier) {
                
                final Attribute loc = testdata.attribute("loc");
                if( loc==null ) {
                        return 0.0;
                }
                                
                final List<Integer> bugPredicted = new ArrayList<>();
                final List<Integer> nobugPredicted = new ArrayList<>(); 
                double totalLoc = 0.0d;
                int totalBugs = 0;
                for( int i=0 ; i<testdata.numInstances() ; i++ ) {
                        try {
                                if( Double.compare(classifier.classifyInstance(testdata.instance(i)),0.0d)==0 ) {
                                        nobugPredicted.add(i);
                                } else {
                                        bugPredicted.add(i);
                                }
                        } catch (Exception e) {
                                throw new RuntimeException("unexpected error during the evaluation of the review effort", e);
                        }
                        if(Double.compare(testdata.instance(i).classValue(),1.0d)==0) {
                                totalBugs++;
                        }
                        totalLoc += testdata.instance(i).value(loc);
                }
                
                final List<Double> reviewLoc = new ArrayList<>(testdata.numInstances());
                final List<Double> bugsFound = new ArrayList<>(testdata.numInstances());
                
                double currentBugsFound = 0;
                
                while( !bugPredicted.isEmpty() ) {
                        double minLoc = Double.MAX_VALUE;
                        int minIndex = -1;
                        for( int i=0 ; i<bugPredicted.size() ; i++ ) {
                                double currentLoc = testdata.instance(bugPredicted.get(i)).value(loc);
                                if( currentLoc<minLoc ) {
                                        minIndex = i;
                                        minLoc = currentLoc;
                                }
                        }
                        if( minIndex!=-1 ) {
                                reviewLoc.add(minLoc/totalLoc);
                                
                                currentBugsFound += testdata.instance(bugPredicted.get(minIndex)).classValue();
                                bugsFound.add(currentBugsFound);
                                
                                bugPredicted.remove(minIndex);
                        } else {
                                throw new RuntimeException("Shouldn't happen!");
                        }
                }
                
                while( !nobugPredicted.isEmpty() ) {
                        double minLoc = Double.MAX_VALUE;
                        int minIndex = -1;
                        for( int i=0 ; i<nobugPredicted.size() ; i++ ) {
                                double currentLoc = testdata.instance(nobugPredicted.get(i)).value(loc);
                                if( currentLoc<minLoc ) {
                                        minIndex = i;
                                        minLoc = currentLoc;
                                }
                        }
                        if( minIndex!=-1 ) {                            
                                reviewLoc.add(minLoc/totalLoc);
                                
                                currentBugsFound += testdata.instance(nobugPredicted.get(minIndex)).classValue();
                                bugsFound.add(currentBugsFound);
                                nobugPredicted.remove(minIndex);
                        } else {
                                throw new RuntimeException("Shouldn't happen!");
                        }
                }
                
                double auc = 0.0;
                for( int i=0 ; i<bugsFound.size() ; i++ ) {
                        auc += reviewLoc.get(i)*bugsFound.get(i)/totalBugs;
                }
                
                return auc;
        }

        /*
         * (non-Javadoc)
         * @see de.ugoe.cs.cpdp.Parameterizable#setParameter(java.lang.String)
         */
        @Override
        public void setParameter(String parameters) {
                if( output!=null && !outputIsSystemOut ) {
                        output.close();
                }
                if( "system.out".equals(parameters) || "".equals(parameters) ) {
                        output = new PrintWriter(System.out);
                        outputIsSystemOut = true;
                } else {
                        try {
                                output = new PrintWriter(new FileOutputStream(parameters));
                                outputIsSystemOut = false;
                        } catch (FileNotFoundException e) {
                                throw new RuntimeException(e);
                        }
                }
        }
}