entity.NfoldCrossValidationManager.java Source code

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Here is the source code for entity.NfoldCrossValidationManager.java

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/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 */

package entity;

import java.math.BigDecimal;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.Instances;

/**
 * Implements cross validation algorithm, with or without noise (fp, fn, fp&fn)
 * 
 * @author Edoardo Varasi
 * @email edoardo.varasi@gmail.com
 *
 */
public class NfoldCrossValidationManager {

    /**
     * n fold cross validation without noise
     * 
     * @param classifier
     * @param dataset
     * @param folds
     * @return
     */
    public Stats crossValidate(Classifier classifier, Instances dataset, int folds) {

        // randomizes order of instances
        Instances randDataset = new Instances(dataset);
        randDataset.randomize(RandomizationManager.randomGenerator);

        // cross-validation
        Evaluation eval = null;
        try {
            eval = new Evaluation(randDataset);
        } catch (Exception e) {
            e.printStackTrace();
        }
        for (int n = 0; n < folds; n++) {
            Instances test = randDataset.testCV(folds, n);
            Instances train = randDataset.trainCV(folds, n, RandomizationManager.randomGenerator);

            // build and evaluate classifier
            Classifier clsCopy;
            try {
                clsCopy = Classifier.makeCopy(classifier);
                clsCopy.buildClassifier(train);
                eval.evaluateModel(clsCopy, test);
            } catch (Exception e) {
                e.printStackTrace();
            }

        }

        // output evaluation for the nfold cross validation
        Double precision = eval.precision(Settings.classificationChoice);
        Double recall = eval.recall(Settings.classificationChoice);
        Double fmeasure = eval.fMeasure(Settings.classificationChoice);
        Double classificationTP = eval.numTruePositives(Settings.classificationChoice);
        Double classificationTN = eval.numTrueNegatives(Settings.classificationChoice);
        Double classificationFP = eval.numFalsePositives(Settings.classificationChoice);
        Double classificationFN = eval.numFalseNegatives(Settings.classificationChoice);
        Double kappa = eval.kappa();

        return new Stats(classificationTP, classificationTN, classificationFP, classificationFN, kappa, precision,
                recall, fmeasure);
    }

    /**
     * n fold cross validation with noise (independent fp and fn)
     * 
     * @param classifier
     * @param dataset
     * @param folds
     * @return
     */
    public Stats crossValidateWithNoise(Classifier classifier, Instances dataset, int folds,
            BigDecimal fpPercentage, BigDecimal fnPercentage) {

        // noise manager
        NoiseInjectionManager noiseInjectionManager = new NoiseInjectionManager();

        // randomizes order of instances
        Instances randDataset = new Instances(dataset);
        randDataset.randomize(RandomizationManager.randomGenerator);

        // cross-validation
        Evaluation eval = null;
        try {
            eval = new Evaluation(randDataset);
        } catch (Exception e) {
            e.printStackTrace();
        }
        for (int n = 0; n < folds; n++) {
            Instances test = randDataset.testCV(folds, n);
            Instances train = randDataset.trainCV(folds, n, RandomizationManager.randomGenerator);

            // copies instances of train set to not modify the original
            Instances noisyTrain = new Instances(train);
            // injects level of noise in the copied train set
            noiseInjectionManager.addNoiseToDataset(noisyTrain, fpPercentage, fnPercentage);

            // build and evaluate classifier
            Classifier clsCopy;
            try {
                clsCopy = Classifier.makeCopy(classifier);
                // trains the model using a noisy train set
                clsCopy.buildClassifier(noisyTrain);
                eval.evaluateModel(clsCopy, test);
            } catch (Exception e) {
                e.printStackTrace();
            }

        }

        // output evaluation for the nfold cross validation
        Double precision = eval.precision(Settings.classificationChoice);
        Double recall = eval.recall(Settings.classificationChoice);
        Double fmeasure = eval.fMeasure(Settings.classificationChoice);
        Double classificationTP = eval.numTruePositives(Settings.classificationChoice);
        Double classificationTN = eval.numTrueNegatives(Settings.classificationChoice);
        Double classificationFP = eval.numFalsePositives(Settings.classificationChoice);
        Double classificationFN = eval.numFalseNegatives(Settings.classificationChoice);
        Double kappa = eval.kappa();

        return new Stats(classificationTP, classificationTN, classificationFP, classificationFN, kappa, precision,
                recall, fmeasure);
    }

    /**
     * n fold cross validation with noise (combined fp and fn)
     * 
     * @param classifier
     * @param dataset
     * @param folds
     * @return
     */

    public Stats crossValidateWithNoise(Classifier classifier, Instances dataset, int folds,
            BigDecimal combinedFpFnPercentage) {

        // noise manager
        NoiseInjectionManager noiseInjectionManager = new NoiseInjectionManager();

        // randomizes order of instances
        Instances randDataset = new Instances(dataset);
        randDataset.randomize(RandomizationManager.randomGenerator);

        // cross-validation
        Evaluation eval = null;
        try {
            eval = new Evaluation(randDataset);
        } catch (Exception e) {
            e.printStackTrace();
        }
        for (int n = 0; n < folds; n++) {
            Instances test = randDataset.testCV(folds, n);
            Instances train = randDataset.trainCV(folds, n, RandomizationManager.randomGenerator);

            // copies instances of train set to not modify the original
            Instances noisyTrain = new Instances(train);
            // injects level of noise in the copied train set
            noiseInjectionManager.addNoiseToDataset(noisyTrain, combinedFpFnPercentage);

            // build and evaluate classifier
            Classifier clsCopy;
            try {
                clsCopy = Classifier.makeCopy(classifier);
                // trains the model using a noisy train set
                clsCopy.buildClassifier(noisyTrain);
                eval.evaluateModel(clsCopy, test);
            } catch (Exception e) {
                e.printStackTrace();
            }

        }

        // output evaluation for the nfold cross validation
        Double precision = eval.precision(Settings.classificationChoice);
        Double recall = eval.recall(Settings.classificationChoice);
        Double fmeasure = eval.fMeasure(Settings.classificationChoice);
        Double classificationTP = eval.numTruePositives(Settings.classificationChoice);
        Double classificationTN = eval.numTrueNegatives(Settings.classificationChoice);
        Double classificationFP = eval.numFalsePositives(Settings.classificationChoice);
        Double classificationFN = eval.numFalseNegatives(Settings.classificationChoice);
        Double kappa = eval.kappa();

        return new Stats(classificationTP, classificationTN, classificationFP, classificationFN, kappa, precision,
                recall, fmeasure);
    }
}