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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
* -P <percent>
* The percentage of instances to use for training.
* (default 66)
*
*
*
* -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
*
*
*
* -R
* Set when data is not to be randomized and the data sets' size.
* Is not to be determined via probabilistic rounding.
*
*
*
* 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 ([email protected])
* @version $Revision: 10203 $
*/
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'.
*/
@Override
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
*/
@Override
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
*/
@Override
public Enumeration enumerateMeasures() {
Vector newVector = new Vector();
if (m_SplitEvaluator instanceof AdditionalMeasureProducer) {
Enumeration en = ((AdditionalMeasureProducer) m_SplitEvaluator)
.enumerateMeasures();
while (en.hasMoreElements()) {
String mname = en.nextElement();
newVector.add(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
*/
@Override
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'
*/
@Override
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.
*/
@Override
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
*/
@Override
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
*/
@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 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
*/
@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");
}
// 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 = 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
*/
@Override
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.
*/
@Override
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
*/
@Override
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.
*/
@Override
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
*/
@Override
public String getCompatibilityState() {
String result = "-P " + m_TrainPercent;
if (!getRandomizeData()) {
result += " -R";
}
if (m_SplitEvaluator == null) {
result += " ";
} 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 false";
}
/**
* 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.
*/
@Override
public Enumeration © 2015 - 2024 Weber Informatics LLC | Privacy Policy