/*
* 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.
*/
/*
* RandomSplitResultProducer.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.experiment;
import weka.core.AdditionalMeasureProducer;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;
import java.io.File;
import java.util.Calendar;
import java.util.Enumeration;
import java.util.Random;
import java.util.TimeZone;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -P <percent>
* The percentage of instances to use for training.
* (default 66)</pre>
*
* <pre> -D
* Save raw split evaluator output.</pre>
*
* <pre> -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)</pre>
*
* <pre> -W <class name>
* The full class name of a SplitEvaluator.
* eg: weka.experiment.ClassifierSplitEvaluator</pre>
*
* <pre> -R
* Set when data is not to be randomized and the data sets' size.
* Is not to be determined via probabilistic rounding.</pre>
*
* <pre>
* Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
* </pre>
*
* <pre> -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes</pre>
*
* <pre> -C <index>
* The index of the class for which IR statistics
* are to be output. (default 1)</pre>
*
* <pre> -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).</pre>
*
* <pre> -P
* Add target and prediction columns to the result
* for each fold.</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* All options after -- will be passed to the split evaluator.
*
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @version $Revision: 1.20 $
*/
public class RandomSplitResultProducer
implements ResultProducer, OptionHandler, AdditionalMeasureProducer,
RevisionHandler {
/** for serialization */
static final long serialVersionUID = 1403798165056795073L;
/** The dataset of interest */
protected Instances m_Instances;
/** The ResultListener to send results to */
protected ResultListener m_ResultListener = new CSVResultListener();
/** The percentage of instances to use for training */
protected double m_TrainPercent = 66;
/** Whether dataset is to be randomized */
protected boolean m_randomize = true;
/** The SplitEvaluator used to generate results */
protected SplitEvaluator m_SplitEvaluator = new ClassifierSplitEvaluator();
/** The names of any additional measures to look for in SplitEvaluators */
protected String [] m_AdditionalMeasures = null;
/** Save raw output of split evaluators --- for debugging purposes */
protected boolean m_debugOutput = false;
/** The output zipper to use for saving raw splitEvaluator output */
protected OutputZipper m_ZipDest = null;
/** The destination output file/directory for raw output */
protected File m_OutputFile = new File(
new File(System.getProperty("user.dir")),
"splitEvalutorOut.zip");
/** The name of the key field containing the dataset name */
public static String DATASET_FIELD_NAME = "Dataset";
/** The name of the key field containing the run number */
public static String RUN_FIELD_NAME = "Run";
/** The name of the result field containing the timestamp */
public static String TIMESTAMP_FIELD_NAME = "Date_time";
/**
* Returns a string describing this result producer
* @return a description of the result producer suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Generates a single train/test split and calls the appropriate "
+ "SplitEvaluator to generate some results.";
}
/**
* Sets the dataset that results will be obtained for.
*
* @param instances a value of type 'Instances'.
*/
public void setInstances(Instances instances) {
m_Instances = instances;
}
/**
* Set a list of method names for additional measures to look for
* in SplitEvaluators. This could contain many measures (of which only a
* subset may be produceable by the current SplitEvaluator) if an experiment
* is the type that iterates over a set of properties.
* @param additionalMeasures an array of measure names, null if none
*/
public void setAdditionalMeasures(String [] additionalMeasures) {
m_AdditionalMeasures = additionalMeasures;
if (m_SplitEvaluator != null) {
System.err.println("RandomSplitResultProducer: setting additional "
+"measures for "
+"split evaluator");
m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures);
}
}
/**
* Returns an enumeration of any additional measure names that might be
* in the SplitEvaluator
* @return an enumeration of the measure names
*/
public Enumeration enumerateMeasures() {
Vector newVector = new Vector();
if (m_SplitEvaluator instanceof AdditionalMeasureProducer) {
Enumeration en = ((AdditionalMeasureProducer)m_SplitEvaluator).
enumerateMeasures();
while (en.hasMoreElements()) {
String mname = (String)en.nextElement();
newVector.addElement(mname);
}
}
return newVector.elements();
}
/**
* Returns the value of the named measure
* @param additionalMeasureName the name of the measure to query for its value
* @return the value of the named measure
* @throws IllegalArgumentException if the named measure is not supported
*/
public double getMeasure(String additionalMeasureName) {
if (m_SplitEvaluator instanceof AdditionalMeasureProducer) {
return ((AdditionalMeasureProducer)m_SplitEvaluator).
getMeasure(additionalMeasureName);
} else {
throw new IllegalArgumentException("RandomSplitResultProducer: "
+"Can't return value for : "+additionalMeasureName
+". "+m_SplitEvaluator.getClass().getName()+" "
+"is not an AdditionalMeasureProducer");
}
}
/**
* Sets the object to send results of each run to.
*
* @param listener a value of type 'ResultListener'
*/
public void setResultListener(ResultListener listener) {
m_ResultListener = listener;
}
/**
* Gets a Double representing the current date and time.
* eg: 1:46pm on 20/5/1999 -> 19990520.1346
*
* @return a value of type Double
*/
public static Double getTimestamp() {
Calendar now = Calendar.getInstance(TimeZone.getTimeZone("UTC"));
double timestamp = now.get(Calendar.YEAR) * 10000
+ (now.get(Calendar.MONTH) + 1) * 100
+ now.get(Calendar.DAY_OF_MONTH)
+ now.get(Calendar.HOUR_OF_DAY) / 100.0
+ now.get(Calendar.MINUTE) / 10000.0;
return new Double(timestamp);
}
/**
* Prepare to generate results.
*
* @throws Exception if an error occurs during preprocessing.
*/
public void preProcess() throws Exception {
if (m_SplitEvaluator == null) {
throw new Exception("No SplitEvalutor set");
}
if (m_ResultListener == null) {
throw new Exception("No ResultListener set");
}
m_ResultListener.preProcess(this);
}
/**
* Perform any postprocessing. When this method is called, it indicates
* that no more requests to generate results for the current experiment
* will be sent.
*
* @throws Exception if an error occurs
*/
public void postProcess() throws Exception {
m_ResultListener.postProcess(this);
if (m_debugOutput) {
if (m_ZipDest != null) {
m_ZipDest.finished();
m_ZipDest = null;
}
}
}
/**
* 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
*/
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 number, dataset name
Object [] seKey = m_SplitEvaluator.getKey();
Object [] key = new Object [seKey.length + 2];
key[0] = Utils.backQuoteChars(m_Instances.relationName());
key[1] = "" + run;
System.arraycopy(seKey, 0, key, 2, 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
*/
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");
}
// Add in some fields to the key like run number, dataset name
Object [] seKey = m_SplitEvaluator.getKey();
Object [] key = new Object [seKey.length + 2];
key[0] = Utils.backQuoteChars(m_Instances.relationName());
key[1] = "" + run;
System.arraycopy(seKey, 0, key, 2, seKey.length);
if (m_ResultListener.isResultRequired(this, key)) {
// Randomize on a copy of the original dataset
Instances runInstances = new Instances(m_Instances);
Instances train;
Instances test;
if (!m_randomize) {
// Don't do any randomization
int trainSize = Utils.round(runInstances.numInstances() * m_TrainPercent / 100);
int testSize = runInstances.numInstances() - trainSize;
train = new Instances(runInstances, 0, trainSize);
test = new Instances(runInstances, trainSize, testSize);
} else {
Random rand = new Random(run);
runInstances.randomize(rand);
// Nominal class
if (runInstances.classAttribute().isNominal()) {
// create the subset for each classs
int numClasses = runInstances.numClasses();
Instances[] subsets = new Instances[numClasses + 1];
for (int i=0; i < numClasses + 1; i++) {
subsets[i] = new Instances(runInstances, 10);
}
// divide instances into subsets
Enumeration e = runInstances.enumerateInstances();
while(e.hasMoreElements()) {
Instance inst = (Instance) e.nextElement();
if (inst.classIsMissing()) {
subsets[numClasses].add(inst);
} else {
subsets[(int) inst.classValue()].add(inst);
}
}
// Compactify them
for (int i=0; i < numClasses + 1; i++) {
subsets[i].compactify();
}
// merge into train and test sets
train = new Instances(runInstances, runInstances.numInstances());
test = new Instances(runInstances, runInstances.numInstances());
for (int i = 0; i < numClasses + 1; i++) {
int trainSize =
Utils.probRound(subsets[i].numInstances() * m_TrainPercent / 100, rand);
for (int j = 0; j < trainSize; j++) {
train.add(subsets[i].instance(j));
}
for (int j = trainSize; j < subsets[i].numInstances(); j++) {
test.add(subsets[i].instance(j));
}
// free memory
subsets[i] = null;
}
train.compactify();
test.compactify();
// randomize the final sets
train.randomize(rand);
test.randomize(rand);
} else {
// Numeric target
int trainSize =
Utils.probRound(runInstances.numInstances() * m_TrainPercent / 100, rand);
int testSize = runInstances.numInstances() - trainSize;
train = new Instances(runInstances, 0, trainSize);
test = new Instances(runInstances, trainSize, testSize);
}
}
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+"."+
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 the names of each of the columns produced for a single run.
* This method should really be static.
*
* @return an array containing the name of each column
*/
public String [] getKeyNames() {
String [] keyNames = m_SplitEvaluator.getKeyNames();
// Add in the names of our extra key fields
String [] newKeyNames = new String [keyNames.length + 2];
newKeyNames[0] = DATASET_FIELD_NAME;
newKeyNames[1] = RUN_FIELD_NAME;
System.arraycopy(keyNames, 0, newKeyNames, 2, keyNames.length);
return newKeyNames;
}
/**
* Gets the data types of each of the columns produced for a single run.
* This method should really be static.
*
* @return an array containing objects of the type of each column. The
* objects should be Strings, or Doubles.
*/
public Object [] getKeyTypes() {
Object [] keyTypes = m_SplitEvaluator.getKeyTypes();
// Add in the types of our extra fields
Object [] newKeyTypes = new String [keyTypes.length + 2];
newKeyTypes[0] = new String();
newKeyTypes[1] = new String();
System.arraycopy(keyTypes, 0, newKeyTypes, 2, keyTypes.length);
return newKeyTypes;
}
/**
* Gets the names of each of the columns produced for a single run.
* This method should really be static.
*
* @return an array containing the name of each column
*/
public String [] getResultNames() {
String [] resultNames = m_SplitEvaluator.getResultNames();
// Add in the names of our extra Result fields
String [] newResultNames = new String [resultNames.length + 1];
newResultNames[0] = TIMESTAMP_FIELD_NAME;
System.arraycopy(resultNames, 0, newResultNames, 1, resultNames.length);
return newResultNames;
}
/**
* Gets the data types of each of the columns produced for a single run.
* This method should really be static.
*
* @return an array containing objects of the type of each column. The
* objects should be Strings, or Doubles.
*/
public Object [] getResultTypes() {
Object [] resultTypes = m_SplitEvaluator.getResultTypes();
// Add in the types of our extra Result fields
Object [] newResultTypes = new Object [resultTypes.length + 1];
newResultTypes[0] = new Double(0);
System.arraycopy(resultTypes, 0, newResultTypes, 1, resultTypes.length);
return newResultTypes;
}
/**
* Gets a description of the internal settings of the result
* producer, sufficient for distinguishing a ResultProducer
* instance from another with different settings (ignoring
* those settings set through this interface). For example,
* a cross-validation ResultProducer may have a setting for the
* number of folds. For a given state, the results produced should
* be compatible. Typically if a ResultProducer is an OptionHandler,
* this string will represent the command line arguments required
* to set the ResultProducer to that state.
*
* @return the description of the ResultProducer state, or null
* if no state is defined
*/
public String getCompatibilityState() {
String result = "-P " + m_TrainPercent;
if (!getRandomizeData()) {
result += " -R";
}
if (m_SplitEvaluator == null) {
result += " <null SplitEvaluator>";
} else {
result += " -W " + m_SplitEvaluator.getClass().getName();
}
return result + " --";
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String outputFileTipText() {
return "Set the destination for saving raw output. If the rawOutput "
+"option is selected, then output from the splitEvaluator for "
+"individual train-test splits is saved. If the destination is a "
+"directory, "
+"then each output is saved to an individual gzip file; if the "
+"destination is a file, then each output is saved as an entry "
+"in a zip file.";
}
/**
* Get the value of OutputFile.
*
* @return Value of OutputFile.
*/
public File getOutputFile() {
return m_OutputFile;
}
/**
* Set the value of OutputFile.
*
* @param newOutputFile Value to assign to OutputFile.
*/
public void setOutputFile(File newOutputFile) {
m_OutputFile = newOutputFile;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String randomizeDataTipText() {
return "Do not randomize dataset and do not perform probabilistic rounding " +
"if true";
}
/**
* Get if dataset is to be randomized
* @return true if dataset is to be randomized
*/
public boolean getRandomizeData() {
return m_randomize;
}
/**
* Set to true if dataset is to be randomized
* @param d true if dataset is to be randomized
*/
public void setRandomizeData(boolean d) {
m_randomize = d;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String rawOutputTipText() {
return "Save raw output (useful for debugging). If set, then output is "
+"sent to the destination specified by outputFile";
}
/**
* Get if raw split evaluator output is to be saved
* @return true if raw split evalutor output is to be saved
*/
public boolean getRawOutput() {
return m_debugOutput;
}
/**
* Set to true if raw split evaluator output is to be saved
* @param d true if output is to be saved
*/
public void setRawOutput(boolean d) {
m_debugOutput = d;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String trainPercentTipText() {
return "Set the percentage of data to use for training.";
}
/**
* Get the value of TrainPercent.
*
* @return Value of TrainPercent.
*/
public double getTrainPercent() {
return m_TrainPercent;
}
/**
* Set the value of TrainPercent.
*
* @param newTrainPercent Value to assign to TrainPercent.
*/
public void setTrainPercent(double newTrainPercent) {
m_TrainPercent = newTrainPercent;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String splitEvaluatorTipText() {
return "The evaluator to apply to the test data. "
+"This may be a classifier, regression scheme etc.";
}
/**
* Get the SplitEvaluator.
*
* @return the SplitEvaluator.
*/
public SplitEvaluator getSplitEvaluator() {
return m_SplitEvaluator;
}
/**
* Set the SplitEvaluator.
*
* @param newSplitEvaluator new SplitEvaluator to use.
*/
public void setSplitEvaluator(SplitEvaluator newSplitEvaluator) {
m_SplitEvaluator = newSplitEvaluator;
m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures);
}
/**
* Returns an enumeration describing the available options..
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(5);
newVector.addElement(new Option(
"\tThe percentage of instances to use for training.\n"
+"\t(default 66)",
"P", 1,
"-P <percent>"));
newVector.addElement(new Option(
"Save raw split evaluator output.",
"D",0,"-D"));
newVector.addElement(new Option(
"\tThe filename where raw output will be stored.\n"
+"\tIf a directory name is specified then then individual\n"
+"\toutputs will be gzipped, otherwise all output will be\n"
+"\tzipped to the named file. Use in conjuction with -D."
+"\t(default splitEvalutorOut.zip)",
"O", 1,
"-O <file/directory name/path>"));
newVector.addElement(new Option(
"\tThe full class name of a SplitEvaluator.\n"
+"\teg: weka.experiment.ClassifierSplitEvaluator",
"W", 1,
"-W <class name>"));
newVector.addElement(new Option(
"\tSet when data is not to be randomized and the data sets' size.\n"
+ "\tIs not to be determined via probabilistic rounding.",
"R",0,"-R"));
if ((m_SplitEvaluator != null) &&
(m_SplitEvaluator instanceof OptionHandler)) {
newVector.addElement(new Option(
"",
"", 0, "\nOptions specific to split evaluator "
+ m_SplitEvaluator.getClass().getName() + ":"));
Enumeration enu = ((OptionHandler)m_SplitEvaluator).listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
}
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -P <percent>
* The percentage of instances to use for training.
* (default 66)</pre>
*
* <pre> -D
* Save raw split evaluator output.</pre>
*
* <pre> -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)</pre>
*
* <pre> -W <class name>
* The full class name of a SplitEvaluator.
* eg: weka.experiment.ClassifierSplitEvaluator</pre>
*
* <pre> -R
* Set when data is not to be randomized and the data sets' size.
* Is not to be determined via probabilistic rounding.</pre>
*
* <pre>
* Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
* </pre>
*
* <pre> -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes</pre>
*
* <pre> -C <index>
* The index of the class for which IR statistics
* are to be output. (default 1)</pre>
*
* <pre> -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).</pre>
*
* <pre> -P
* Add target and prediction columns to the result
* for each fold.</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* All options after -- will be passed to the split evaluator.
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
setRawOutput(Utils.getFlag('D', options));
setRandomizeData(!Utils.getFlag('R', options));
String fName = Utils.getOption('O', options);
if (fName.length() != 0) {
setOutputFile(new File(fName));
}
String trainPct = Utils.getOption('P', options);
if (trainPct.length() != 0) {
setTrainPercent((new Double(trainPct)).doubleValue());
} else {
setTrainPercent(66);
}
String seName = Utils.getOption('W', options);
if (seName.length() == 0) {
throw new Exception("A SplitEvaluator must be specified with"
+ " the -W option.");
}
// Do it first without options, so if an exception is thrown during
// the option setting, listOptions will contain options for the actual
// SE.
setSplitEvaluator((SplitEvaluator)Utils.forName(
SplitEvaluator.class,
seName,
null));
if (getSplitEvaluator() instanceof OptionHandler) {
((OptionHandler) getSplitEvaluator())
.setOptions(Utils.partitionOptions(options));
}
}
/**
* Gets the current settings of the result producer.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] seOptions = new String [0];
if ((m_SplitEvaluator != null) &&
(m_SplitEvaluator instanceof OptionHandler)) {
seOptions = ((OptionHandler)m_SplitEvaluator).getOptions();
}
String [] options = new String [seOptions.length + 9];
int current = 0;
options[current++] = "-P"; options[current++] = "" + getTrainPercent();
if (getRawOutput()) {
options[current++] = "-D";
}
if (!getRandomizeData()) {
options[current++] = "-R";
}
options[current++] = "-O";
options[current++] = getOutputFile().getName();
if (getSplitEvaluator() != null) {
options[current++] = "-W";
options[current++] = getSplitEvaluator().getClass().getName();
}
options[current++] = "--";
System.arraycopy(seOptions, 0, options, current,
seOptions.length);
current += seOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Gets a text descrption of the result producer.
*
* @return a text description of the result producer.
*/
public String toString() {
String result = "RandomSplitResultProducer: ";
result += getCompatibilityState();
if (m_Instances == null) {
result += ": <null Instances>";
} else {
result += ": " + Utils.backQuoteChars(m_Instances.relationName());
}
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.20 $");
}
} // RandomSplitResultProducer