/*
* 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.
*/
/*
* EvaluationUtils.java
* Copyright (C) 2002 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.evaluation;
import weka.classifiers.Classifier;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import java.util.Random;
/**
* Contains utility functions for generating lists of predictions in
* various manners.
*
* @author Len Trigg (len@reeltwo.com)
* @version $Revision: 1.11 $
*/
public class EvaluationUtils
implements RevisionHandler {
/** Seed used to randomize data in cross-validation */
private int m_Seed = 1;
/** Sets the seed for randomization during cross-validation */
public void setSeed(int seed) { m_Seed = seed; }
/** Gets the seed for randomization during cross-validation */
public int getSeed() { return m_Seed; }
/**
* Generate a bunch of predictions ready for processing, by performing a
* cross-validation on the supplied dataset.
*
* @param classifier the Classifier to evaluate
* @param data the dataset
* @param numFolds the number of folds in the cross-validation.
* @exception Exception if an error occurs
*/
public FastVector getCVPredictions(Classifier classifier,
Instances data,
int numFolds)
throws Exception {
FastVector predictions = new FastVector();
Instances runInstances = new Instances(data);
Random random = new Random(m_Seed);
runInstances.randomize(random);
if (runInstances.classAttribute().isNominal() && (numFolds > 1)) {
runInstances.stratify(numFolds);
}
int inst = 0;
for (int fold = 0; fold < numFolds; fold++) {
Instances train = runInstances.trainCV(numFolds, fold, random);
Instances test = runInstances.testCV(numFolds, fold);
FastVector foldPred = getTrainTestPredictions(classifier, train, test);
predictions.appendElements(foldPred);
}
return predictions;
}
/**
* Generate a bunch of predictions ready for processing, by performing a
* evaluation on a test set after training on the given training set.
*
* @param classifier the Classifier to evaluate
* @param train the training dataset
* @param test the test dataset
* @exception Exception if an error occurs
*/
public FastVector getTrainTestPredictions(Classifier classifier,
Instances train, Instances test)
throws Exception {
classifier.buildClassifier(train);
return getTestPredictions(classifier, test);
}
/**
* Generate a bunch of predictions ready for processing, by performing a
* evaluation on a test set assuming the classifier is already trained.
*
* @param classifier the pre-trained Classifier to evaluate
* @param test the test dataset
* @exception Exception if an error occurs
*/
public FastVector getTestPredictions(Classifier classifier,
Instances test)
throws Exception {
FastVector predictions = new FastVector();
for (int i = 0; i < test.numInstances(); i++) {
if (!test.instance(i).classIsMissing()) {
predictions.addElement(getPrediction(classifier, test.instance(i)));
}
}
return predictions;
}
/**
* Generate a single prediction for a test instance given the pre-trained
* classifier.
*
* @param classifier the pre-trained Classifier to evaluate
* @param test the test instance
* @exception Exception if an error occurs
*/
public Prediction getPrediction(Classifier classifier,
Instance test)
throws Exception {
double actual = test.classValue();
double [] dist = classifier.distributionForInstance(test);
if (test.classAttribute().isNominal()) {
return new NominalPrediction(actual, dist, test.weight());
} else {
return new NumericPrediction(actual, dist[0], test.weight());
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.11 $");
}
}