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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* 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 3 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, see
* -X <number of folds>
* The number of folds to use for the cross-validation.
* (default 10)
*
*
*
* -D
* Save raw split evaluator output.
*
*
*
* -O <file/directory name/path>
* The filename where raw output will be stored.
* If a directory name is specified then then individual
* outputs will be gzipped, otherwise all output will be
* zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
*
*
*
* -W <class name>
* The full class name of a SplitEvaluator.
* eg: weka.experiment.ClassifierSplitEvaluator
*
*
*
* Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
*
*
*
* -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes
*
*
*
* -C <index>
* The index of the class for which IR statistics
* are to be output. (default 1)
*
*
*
* -I <index>
* The index of an attribute to output in the
* results. This attribute should identify an
* instance in order to know which instances are
* in the test set of a cross validation. if 0
* no output (default 0).
*
*
*
* -P
* Add target and prediction columns to the result
* for each fold.
*
*
*
* Options specific to classifier weka.classifiers.rules.ZeroR:
*
*
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
*
*
* All options after -- will be passed to the split evaluator.
*
* @author Len Trigg
* @author Eibe Frank
* @version $Revision: 10203 $
*/
public class CrossValidationSplitResultProducer extends
CrossValidationResultProducer {
/** for serialization */
static final long serialVersionUID = 1403798164046795073L;
/**
* Returns a string describing this result producer
*
* @return a description of the result producer suitable for displaying in the
* explorer/experimenter gui
*/
@Override
public String globalInfo() {
return "Carries out one split of a repeated k-fold cross-validation, "
+ "using the set SplitEvaluator to generate some results. "
+ "Note that the run number is actually the nth split of a repeated "
+ "k-fold cross-validation, i.e. if k=10, run number 100 is the 10th "
+ "fold of the 10th cross-validation run. This producer's sole purpose "
+ "is to allow more fine-grained distribution of cross-validation "
+ "experiments. If the class attribute is nominal, the dataset is stratified.";
}
/**
* Gets the keys for a specified run number. Different run numbers correspond
* to different randomizations of the data. Keys produced should be sent to
* the current ResultListener
*
* @param run the run number to get keys for.
* @throws Exception if a problem occurs while getting the keys
*/
@Override
public void doRunKeys(int run) throws Exception {
if (m_Instances == null) {
throw new Exception("No Instances set");
}
// Add in some fields to the key like run and fold number, dataset name
Object[] seKey = m_SplitEvaluator.getKey();
Object[] key = new Object[seKey.length + 3];
key[0] = Utils.backQuoteChars(m_Instances.relationName());
key[2] = "" + (((run - 1) % m_NumFolds) + 1);
key[1] = "" + (((run - 1) / m_NumFolds) + 1);
System.arraycopy(seKey, 0, key, 3, seKey.length);
if (m_ResultListener.isResultRequired(this, key)) {
try {
m_ResultListener.acceptResult(this, key, null);
} catch (Exception ex) {
// Save the train and test datasets for debugging purposes?
throw ex;
}
}
}
/**
* Gets the results for a specified run number. Different run numbers
* correspond to different randomizations of the data. Results produced should
* be sent to the current ResultListener
*
* @param run the run number to get results for.
* @throws Exception if a problem occurs while getting the results
*/
@Override
public void doRun(int run) throws Exception {
if (getRawOutput()) {
if (m_ZipDest == null) {
m_ZipDest = new OutputZipper(m_OutputFile);
}
}
if (m_Instances == null) {
throw new Exception("No Instances set");
}
// Compute run and fold number from given run
int fold = (run - 1) % m_NumFolds;
run = ((run - 1) / m_NumFolds) + 1;
// Randomize on a copy of the original dataset
Instances runInstances = new Instances(m_Instances);
Random random = new Random(run);
runInstances.randomize(random);
if (runInstances.classAttribute().isNominal()) {
runInstances.stratify(m_NumFolds);
}
// Add in some fields to the key like run and fold number, dataset name
Object[] seKey = m_SplitEvaluator.getKey();
Object[] key = new Object[seKey.length + 3];
key[0] = Utils.backQuoteChars(m_Instances.relationName());
key[1] = "" + run;
key[2] = "" + (fold + 1);
System.arraycopy(seKey, 0, key, 3, seKey.length);
if (m_ResultListener.isResultRequired(this, key)) {
// Just to make behaviour absolutely consistent with
// CrossValidationResultProducer
for (int tempFold = 0; tempFold < fold; tempFold++) {
runInstances.trainCV(m_NumFolds, tempFold, random);
}
Instances train = runInstances.trainCV(m_NumFolds, fold, random);
Instances test = runInstances.testCV(m_NumFolds, fold);
try {
Object[] seResults = m_SplitEvaluator.getResult(train, test);
Object[] results = new Object[seResults.length + 1];
results[0] = getTimestamp();
System.arraycopy(seResults, 0, results, 1, seResults.length);
if (m_debugOutput) {
String resultName = ("" + run + "." + (fold + 1) + "."
+ Utils.backQuoteChars(runInstances.relationName()) + "." + m_SplitEvaluator
.toString()).replace(' ', '_');
resultName = Utils.removeSubstring(resultName, "weka.classifiers.");
resultName = Utils.removeSubstring(resultName, "weka.filters.");
resultName = Utils.removeSubstring(resultName,
"weka.attributeSelection.");
m_ZipDest.zipit(m_SplitEvaluator.getRawResultOutput(), resultName);
}
m_ResultListener.acceptResult(this, key, results);
} catch (Exception ex) {
// Save the train and test datasets for debugging purposes?
throw ex;
}
}
}
/**
* Gets a text descrption of the result producer.
*
* @return a text description of the result producer.
*/
@Override
public String toString() {
String result = "CrossValidationSplitResultProducer: ";
result += getCompatibilityState();
if (m_Instances == null) {
result += ": ";
} else {
result += ": " + Utils.backQuoteChars(m_Instances.relationName());
}
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 10203 $");
}
} // CrossValidationSplitResultProducer
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