CrossValidationMultipleRuns.java Source code

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Here is the source code for CrossValidationMultipleRuns.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., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    CrossValidationMultipleRuns.java
 *    Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
 *
 */

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.Instances;
import weka.core.Utils;
import weka.core.converters.ConverterUtils.DataSource;

import java.util.Random;

/**
 * Performs multiple runs of cross-validation.
 *
 * Command-line parameters:
 * <ul>
 *    <li>-t filename - the dataset to use</li>
 *    <li>-x int - the number of folds to use</li>
 *    <li>-r int - the number of runs to perform</li>
 *    <li>-c int - the class index, "first" and "last" are accepted as well;
 *    "last" is used by default</li>
 *    <li>-W classifier - classname and options, enclosed by double quotes; 
 *    the classifier to cross-validate</li>
 * </ul>
 *
 * Example command-line:
 * <pre>
 * java wekaexamples.classifiers.CrossValidationMultipleRuns -t labor.arff -c last -x 10 -r 10 -W "weka.classifiers.trees.J48 -C 0.25"
 * </pre>
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class CrossValidationMultipleRuns {

    /**
     * Performs the cross-validation. See Javadoc of class for information
     * on command-line parameters.
     *
     * @param args   the command-line parameters
     * @throws Exception   if something goes wrong
     */
    public static void main(String[] args) throws Exception {
        // loads data and set class index
        Instances data = DataSource.read(Utils.getOption("t", args));
        String clsIndex = Utils.getOption("c", args);
        if (clsIndex.length() == 0)
            clsIndex = "last";
        if (clsIndex.equals("first"))
            data.setClassIndex(0);
        else if (clsIndex.equals("last"))
            data.setClassIndex(data.numAttributes() - 1);
        else
            data.setClassIndex(Integer.parseInt(clsIndex) - 1);

        // classifier
        String[] tmpOptions;
        String classname;
        tmpOptions = Utils.splitOptions(Utils.getOption("W", args));
        classname = tmpOptions[0];
        tmpOptions[0] = "";
        Classifier cls = (Classifier) Utils.forName(Classifier.class, classname, tmpOptions);

        // other options
        int runs = Integer.parseInt(Utils.getOption("r", args));
        int folds = Integer.parseInt(Utils.getOption("x", args));

        // perform cross-validation
        for (int i = 0; i < runs; i++) {
            // randomize data
            int seed = i + 1;
            Random rand = new Random(seed);
            Instances randData = new Instances(data);
            randData.randomize(rand);
            //if (randData.classAttribute().isNominal())
            //   randData.stratify(folds);

            Evaluation eval = new Evaluation(randData);

            StringBuilder optionsString = new StringBuilder();
            for (String s : cls.getOptions()) {
                optionsString.append(s);
                optionsString.append(" ");
            }

            // output evaluation
            System.out.println();
            System.out.println("=== Setup run " + (i + 1) + " ===");
            System.out.println("Classifier: " + optionsString.toString());
            System.out.println("Dataset: " + data.relationName());
            System.out.println("Folds: " + folds);
            System.out.println("Seed: " + seed);
            System.out.println();

            for (int n = 0; n < folds; n++) {
                Instances train = randData.trainCV(folds, n);
                Instances test = randData.testCV(folds, n);

                // build and evaluate classifier
                Classifier clsCopy = Classifier.makeCopy(cls);
                clsCopy.buildClassifier(train);
                eval.evaluateModel(clsCopy, test);
                System.out.println(eval.toClassDetailsString());
            }

            System.out.println(
                    eval.toSummaryString("=== " + folds + "-fold Cross-validation run " + (i + 1) + " ===", false));
        }
    }
}