/*
* 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.
*/
/*
* CrossValidationSingleRun.java
* Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
*
*/
package wekaexamples.classifiers;
import weka.classifiers.AbstractClassifier;
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 a single run 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>-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 wekaexamples.classifiers.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)
* @version $Revision$
*/
public class CrossValidationSingleRun {
/**
* 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 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
Evaluation eval = new Evaluation(randData);
for (int n = 0; n < folds; n++) {
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 = AbstractClassifier.makeCopy(cls);
clsCopy.buildClassifier(train);
eval.evaluateModel(clsCopy, test);
}
// output evaluation
System.out.println();
System.out.println("=== Setup ===");
System.out.println("Classifier: " + Utils.toCommandLine(cls));
System.out.println("Dataset: " + data.relationName());
System.out.println("Folds: " + folds);
System.out.println("Seed: " + seed);
System.out.println();
System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false));
}
}