statistics.BinaryStatisticsEvaluator.java Source code

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Here is the source code for statistics.BinaryStatisticsEvaluator.java

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/*
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 */
package statistics;

import java.util.logging.Level;
import java.util.logging.Logger;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.functions.SMO;
import weka.classifiers.lazy.IBk;
import weka.classifiers.trees.J48;
import weka.core.Instances;

/**
 *
 * @author nikos
 */
public class BinaryStatisticsEvaluator implements StatisticsEvaluator {
    @Override
    public double getAccuracy(double[][] ConfMatrix) {
        //Overall effectiveness of a classifier
        return (ConfMatrix[1][1] + ConfMatrix[0][0])
                / (ConfMatrix[0][0] + ConfMatrix[0][1] + ConfMatrix[1][0] + ConfMatrix[1][1]);
    }

    @Override
    public double getPrecision(double[][] ConfMatrix) {
        //Class agreement of the data labels with the positive labels given by the classifier
        return ConfMatrix[1][1] / (ConfMatrix[1][1] + ConfMatrix[0][1]);
        //return TruePositives / TruePositives + FalsePositives;
    }

    @Override
    public double getRecall(double[][] ConfMatrix) {
        //Effectiveness of a classifier to identify positive labels
        return ConfMatrix[1][1] / (ConfMatrix[1][1] + ConfMatrix[1][0]);
        //return TruePositives / TruePositives + FalseNegatives;
    }

    @Override
    public double getSpecificity(double[][] ConfMatrix) {
        //How effectively a classifier identifies negative labels
        return ConfMatrix[0][0] / (ConfMatrix[0][1] + ConfMatrix[0][0]);
        //return TrueNegatives / FalsePositives + TrueNegatives;
    }

    @Override
    public double getAUC(double[][] ConfMatrix) {
        //Classifier's ability to avoid false classification
        return 0.5 * (getSpecificity(ConfMatrix) + getRecall(ConfMatrix));
    }

    @Override
    public double getfScore(double[][] ConfMatrix) {
        return 2 * this.getAccuracy(ConfMatrix) * this.getPrecision(ConfMatrix)
                / (this.getAccuracy(ConfMatrix) + this.getPrecision(ConfMatrix)); //To change body of generated methods, choose Tools | Templates.
    }

    @Override
    public double[][] getConfusionMatrix(Instances Training_Instances, Instances Testing_Instances,
            String classifier) {

        Classifier cModel = null;
        if ("NB".equals(classifier)) {
            cModel = (Classifier) new NaiveBayes();
            try {
                cModel.buildClassifier(Training_Instances);
            } catch (Exception ex) {
                Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
            }
        } else if ("DT".equals(classifier)) {
            cModel = (Classifier) new J48();
            try {
                cModel.buildClassifier(Training_Instances);
            } catch (Exception ex) {
                Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
            }
        } else if ("SVM".equals(classifier)) {
            cModel = (Classifier) new SMO();

            try {
                cModel.buildClassifier(Training_Instances);
            } catch (Exception ex) {
                Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
            }
        } else if ("KNN".equals(classifier)) {
            cModel = (Classifier) new IBk();
            try {
                cModel.buildClassifier(Training_Instances);
            } catch (Exception ex) {
                Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
            }
        }
        //Test the model
        Evaluation eTest;
        try {
            eTest = new Evaluation(Training_Instances);
            eTest.evaluateModel(cModel, Testing_Instances);
            //Print the result
            String strSummary = eTest.toSummaryString();
            System.out.println(strSummary);
            String strSummary1 = eTest.toMatrixString();
            System.out.println(strSummary1);
            String strSummary2 = eTest.toClassDetailsString();
            System.out.println(strSummary2);

            //Get the confusion matrix
            double[][] cmMatrix = eTest.confusionMatrix();
            return cmMatrix;
        } catch (Exception ex) {
            Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
        }
        return null;
    }
}