Performs a single run of cross-validation on weka - Java Machine Learning AI

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Description

Performs a single run of cross-validation on weka

Demo Code

/*//  ww w .  j  a  va  2 s .com
 *    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.
 */

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

import java.io.FileReader;
import java.util.Random;

/**
 * Performs a single run of cross-validation. Outputs the Confusion matrices
 * for each single fold.
 *
 * Command-line parameters:
 * <ul>
 *    <li>-t filename - the dataset to use</li>
 *    <li>-x int - the number of folds to use</li>
 *    <li>-s int - the seed for the random number generator</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 CrossValidationSingleRun -t anneal.arff -c last -x 10 -s 1 -W "weka.classifiers.trees.J48 -C 0.25"
 * </pre>
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 */
public class CrossValidationSingleRunVariant {

    public static void main(String[] args) throws Exception {
        // loads data and set class index
        Instances data = new Instances(new FileReader(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 seed = Integer.parseInt(Utils.getOption("s", args));
        int folds = Integer.parseInt(Utils.getOption("x", args));

        // randomize data
        Random rand = new Random(seed);
        Instances randData = new Instances(data);
        randData.randomize(rand);
        if (randData.classAttribute().isNominal())
            randData.stratify(folds);

        // perform cross-validation
        System.out.println();
        System.out.println("=== Setup ===");
        System.out.println("Classifier: " + cls.getClass().getName() + " "
                + Utils.joinOptions(cls.getOptions()));
        System.out.println("Dataset: " + data.relationName());
        System.out.println("Folds: " + folds);
        System.out.println("Seed: " + seed);
        System.out.println();
        Evaluation evalAll = new Evaluation(randData);
        for (int n = 0; n < folds; n++) {
            Evaluation eval = new Evaluation(randData);
            Instances train = randData.trainCV(folds, n);
            Instances test = randData.testCV(folds, n);
            // the above code is used by the StratifiedRemoveFolds filter, the
            // code below by the Explorer/Experimenter:
            // Instances train = randData.trainCV(folds, n, rand);

            // build and evaluate classifier
            Classifier clsCopy = Classifier.makeCopy(cls);
            clsCopy.buildClassifier(train);
            eval.evaluateModel(clsCopy, test);
            evalAll.evaluateModel(clsCopy, test);

            // output evaluation
            System.out.println();
            System.out.println(eval
                    .toMatrixString("=== Confusion matrix for fold "
                            + (n + 1) + "/" + folds + " ===\n"));
        }

        // output evaluation
        System.out.println();
        System.out.println(evalAll.toSummaryString("=== " + folds
                + "-fold Cross-validation ===", false));
    }
}

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