/*
* 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.
*/
/*
* DedupingPRCurveCVResultProducer.java
* Copyright (c) 2003 Mikhail Bilenko
*
*/
package weka.experiment;
import java.util.*;
import java.io.*;
import weka.core.*;
import weka.core.OptionHandler;
import weka.core.Option;
import weka.core.Utils;
import weka.core.AdditionalMeasureProducer;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;
/**
* N-fold cross-validation learning curve for
* deduping applications
*
* @author Mikhail Bilenko */
public class DedupingPRCurveCVResultProducer
implements ResultProducer, OptionHandler, AdditionalMeasureProducer {
/** The dataset of interest */
protected Instances m_instances;
/** SVM-light can work in classification, regression and preference ranking modes */
public static final int FOLD_CREATION_MODE_STRATIFIED = 1;
public static final int FOLD_CREATION_MODE_RANDOM = 2;
public static final Tag[] TAGS_FOLD_CREATION_MODE = {
new Tag(FOLD_CREATION_MODE_STRATIFIED, "Stratified"),
new Tag(FOLD_CREATION_MODE_RANDOM, "Random")
};
protected int m_foldCreationMode = FOLD_CREATION_MODE_STRATIFIED;
/** The ResultListener to send results to */
protected ResultListener m_resultListener = new CSVResultListener();
/** The number of folds in the cross-validation */
protected int m_numFolds = 2;
/** 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 SplitEvaluator used to generate results */
protected SplitEvaluator m_splitEvaluator = new DeduperSplitEvaluator();
/** The names of any additional measures to look for in SplitEvaluators */
protected String [] m_additionalMeasures = null;
/** The specific points to plot, either integers representing specific numbers of training examples,
* or decimal fractions representing percentages of the full training set*/
protected double[] m_plotPoints = {0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45,
0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0};
/* 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 key field containing the fold number */
public static String FOLD_FIELD_NAME = "Fold";
/* The name of the result field containing the timestamp */
public static String TIMESTAMP_FIELD_NAME = "Date_time";
/* The name of the key field containing the learning rate step number */
public static String RECALL_FIELD_NAME = "Fraction_instances";
/**
* 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 "Performs a learning-curve cross validation run using a supplied " +
"deduping split evaluator. Trains on increasing subsets of the training data for each split";
}
/**
* Sets the dataset that results will be obtained for.
*
* @param instances a value of type 'Instances'.
*/
public void setInstances(Instances instances) {
m_instances = instances;
}
/**
* 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;
}
/**
* 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(" DedupingPRCurveCVResultProducer: 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 measureName the name of the measure to query for its value
* @return the value of the named measure
* @exception 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("DedupingPRCurveCVResultProducer: "
+"Can't return value for : "+additionalMeasureName
+". "+m_splitEvaluator.getClass().getName()+" "
+"is not an AdditionalMeasureProducer");
}
}
/**
* 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.
*
* @exception 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.
*
* @exception 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.
* @exception Exception if a problem occurs while getting the keys
*/
public void doRunKeys(int run) throws Exception {
int numExtraKeys = 4;
if (m_instances == null) {
throw new Exception("No Instances set");
}
if (m_resultListener == null) {
throw new Exception("No ResultListener set");
}
for (int fold = 0; fold < m_numFolds; fold++) {
int pointNum = 0;
// For each subsample size
for (int i = 0; i < m_plotPoints.length; i++) {
// 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 + numExtraKeys];
key[0] = Utils.backQuoteChars(m_instances.relationName());
key[1] = "" + run;
key[2] = "" + (fold + 1);
key[3] = "" + m_plotPoints[i];
System.arraycopy(seKey, 0, key, numExtraKeys, 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.
* @exception Exception if a problem occurs while getting the results
*/
public void doRun(int run) throws Exception {
int numExtraKeys = 4;
if (getRawOutput()) {
if (m_zipDest == null) {
m_zipDest = new OutputZipper(m_outputFile);
}
}
if (m_instances == null) {
throw new Exception("No Instances set");
}
if (m_resultListener == null) {
throw new Exception("No ResultListener set");
}
if (!m_instances.classAttribute().isNominal()) {
throw new Exception("Class attribute must be nominal - it is the true Object ID");
}
// Randomize on a copy of the original dataset
Instances runInstances = new Instances(m_instances);
runInstances.randomize(new Random(run));
ArrayList foldList = createFoldList(runInstances, m_numFolds);
for (int fold = 0; fold < m_numFolds; fold++) {
Instances train = getTrainingFold(foldList, fold);
// Randomly shuffle the training set for fold creation
train.randomize(new Random(fold));
Instances test = (Instances) foldList.get(fold);
System.out.println("Run:" + run + " Fold:" + fold + " TestSize=" + test.numInstances());
Object[] prResults = m_splitEvaluator.getResult(train, test);
for (int i = 0; i < m_plotPoints.length; i++) {
// 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 + numExtraKeys];
key[0] = Utils.backQuoteChars(m_instances.relationName());
key[1] = "" + run;
key[2] = "" + (fold + 1);
key[3] = "" + m_plotPoints[i];
System.arraycopy(seKey, 0, key, numExtraKeys, seKey.length);
if (m_resultListener.isResultRequired(this, key)) {
try {
Object [] seResults = processResults(prResults, m_plotPoints[i]);
System.out.println("Adding result: RLevel=" + m_plotPoints[i] +
"\tR=" + seResults[1] + "\tP=" + seResults[2] +
"\tFM=" + seResults[3]);
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.clusterers.");
resultName = Utils.removeSubstring(resultName,
"weka.filters.");
resultName = Utils.removeSubstring(resultName,
"weka.attributeSelection.");
resultName = Utils.removeSubstring(resultName,
"weka.deduping.");
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;
}
}
}
}
}
/** Given a set of instances with the class attribute containing true
* objectID, create a list of folds using the preferred method
*
* @param runInstances a set of instances with class labels
* @param numFolds the number of folds to create
* @return a list of Instances objects, every Instances contains a fold
*/
ArrayList createFoldList(Instances runInstances, int numFolds) {
ArrayList foldList = null;
switch (m_foldCreationMode) {
case FOLD_CREATION_MODE_STRATIFIED:
foldList = createFoldListStratified(runInstances, numFolds);
break;
case FOLD_CREATION_MODE_RANDOM:
foldList = createFoldListRandom(runInstances, numFolds);
break;
default:
System.err.println("Panic! Unknown fold creation mode: " + m_foldCreationMode);
System.exit(1);
}
return foldList;
}
/** Given a set of instances with the class attribute containing true
* objectID, create a list of folds containing an approximately equal number of class values (equivalence classes)
* Objects with the same class ID *always* end in the same fold
*
* @param runInstances a set of instances with class labels
* @param numFolds the number of folds to create
* @return a list of Instances objects, every Instances contains a fold
*/
ArrayList createFoldListStratified(Instances runInstances, int numFolds) {
HashMap classFoldHash = new HashMap();
ArrayList foldList = new ArrayList(numFolds);
int numInstances = runInstances.numInstances();
Random rand = new Random(numFolds + numInstances);
// initialize the folds
for (int i=0; i < numFolds; i++) {
Instances fold = new Instances(runInstances, numInstances/numFolds);
foldList.add(fold);
}
// assign each class to a random fold
for (int i=0; i < runInstances.numInstances(); i++) {
Instance instance = runInstances.instance(i);
Double classValue = new Double(instance.classValue());
if (classFoldHash.containsKey(classValue)) {
Instances fold = (Instances) classFoldHash.get(classValue);
fold.add(instance);
} else {
// this class has not been seen before, assign to a random fold
int foldIdx = rand.nextInt(numFolds);
Instances fold = (Instances) foldList.get(foldIdx);
fold.add(instance);
classFoldHash.put(classValue, fold);
}
}
return foldList;
}
/** Given a set of instances with the class attribute containing true
* objectID, create a list of *randomly assigned* folds disregarding stratification
*
* @param runInstances a set of instances with class labels
* @param numFolds the number of folds to create
* @return a list of Instances objects, every Instances contains a fold
*/
ArrayList createFoldListRandom(Instances runInstances, int numFolds) {
ArrayList foldList = new ArrayList(numFolds);
int numInstances = runInstances.numInstances();
Random rand = new Random(numFolds + numInstances);
// initialize the folds
for (int i=0; i < numFolds; i++) {
Instances fold = new Instances(runInstances, numInstances/numFolds);
foldList.add(fold);
}
// Create all positive pairs and assign them to random folds
// first, hash all classes
HashMap classInstanceListHash = new HashMap();
for (int i=0; i < runInstances.numInstances(); i++) {
Instance instance = runInstances.instance(i);
Double classValue = new Double(instance.classValue());
if (classInstanceListHash.containsKey(classValue)) {
ArrayList instanceList = (ArrayList) classInstanceListHash.get(classValue);
instanceList.add(instance);
} else {
// this class has not been seen before, create a new list for it
ArrayList instanceList = new ArrayList();
instanceList.add(instance);
classInstanceListHash.put(classValue, instanceList);
}
}
int [] foldAssignments = new int[runInstances.numInstances()];
Arrays.fill(foldAssignments, -1);
// go through the classes; each pair gets assigned to a random fold; singletons are also thrown
// into a random fold
Iterator iterator = classInstanceListHash.values().iterator();
while (iterator.hasNext()) {
ArrayList instanceList = (ArrayList) iterator.next();
int classSize = instanceList.size();
if (classSize > 1) {
// go through all pairs and assign both instances of each pair to a random fold
boolean[][] foldInstancesAdded = new boolean[numFolds][classSize];
for (int i=0; i < classSize-1; i++) {
Instance instance1 = (Instance) instanceList.get(i);
for (int j=i+1; j < classSize; j++) {
Instance instance2 = (Instance) instanceList.get(j);
int foldIdx = rand.nextInt(numFolds);
Instances fold = (Instances) foldList.get(foldIdx);
// add the two instances to the random fold unless they have previously been added
if (foldInstancesAdded[foldIdx][i] == false) {
fold.add(instance1);
foldInstancesAdded[foldIdx][i] = true;
}
if (foldInstancesAdded[foldIdx][j] == false) {
fold.add(instance2);
foldInstancesAdded[foldIdx][j] = true;
}
}
}
} else {
// singleton class, assign to a random fold
Instance instance = (Instance) instanceList.get(0);
int foldIdx = rand.nextInt(numFolds);
Instances fold = (Instances) foldList.get(foldIdx);
fold.add(instance);
}
}
return foldList;
}
/** Given a list of folds, merge together all but the test fold with the specified index
* and return the resulting training fold
* @param foldList a list containg folds
* @param testFoldIdx the index of the fold that will be used for testing
* @return an agglomeration of all folds except for the test one.
*/
protected Instances getTrainingFold(ArrayList foldList, int testFoldIdx) {
Instances trainFold = new Instances(m_instances, m_instances.numInstances());
System.out.println("Getting training fold " + testFoldIdx + " out of " + foldList.size());
for (int i = 0; i < foldList.size(); i++) {
if (i != testFoldIdx) {
Instances nextFold = (Instances) foldList.get(i);
for (int j = 0; j < nextFold.numInstances(); j++) {
Instance nextInstance = (Instance) nextFold.instance(j);
trainFold.add(nextInstance);
}
}
}
return trainFold;
}
/** Given an array containing the overall results of a deduping
* experiment, produce an array containing results for a specific
* recall level
*/
protected Object [] processResults(Object[] prResults, double recallLevel) {
double maxPrecision = 0;
double maxFM = 0;
Object[] results = (Object[]) prResults[prResults.length-1];
// System.out.println(results[1] + "\t" + results[2] + "\t" + results[3]);
for (int i = prResults.length-1; i >= 0; i--) {
Object[] nextResults = (Object[]) prResults[i];
//System.out.println(nextResults[1] + "\t" + nextResults[2] + "\t" + nextResults[3]);
double recall = ((Double) nextResults[1]).doubleValue();
if (recall < recallLevel) {
break;
}
double precision = ((Double) nextResults[2]).doubleValue();
if (precision > maxPrecision) {
maxPrecision = precision;
results = nextResults;
}
double fmeasure = ((Double) nextResults[3]).doubleValue();
if (fmeasure > maxFM) {
maxFM = fmeasure;
}
}
Object [] returnResults = new Object [results.length];
System.arraycopy(results, 0, returnResults, 0, results.length);
returnResults[1] = new Double(recallLevel);
returnResults[3] = new Double(maxFM);
return returnResults;
}
/**
* 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
int numExtraKeys = 4;
String [] newKeyNames = new String [keyNames.length + numExtraKeys];
newKeyNames[0] = DATASET_FIELD_NAME;
newKeyNames[1] = RUN_FIELD_NAME;
newKeyNames[2] = FOLD_FIELD_NAME;
newKeyNames[3] = RECALL_FIELD_NAME;
System.arraycopy(keyNames, 0, newKeyNames, numExtraKeys, 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();
int numExtraKeys = 4;
// Add in the types of our extra fields
Object [] newKeyTypes = new String [keyTypes.length + numExtraKeys];
newKeyTypes[0] = new String();
newKeyTypes[1] = new String();
newKeyTypes[2] = new String();
newKeyTypes[3] = new String();
System.arraycopy(keyTypes, 0, newKeyTypes, numExtraKeys, 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 = "-X " + m_numFolds + " ";
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 folds 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 numFoldsTipText() {
return "Number of folds to use in cross validation.";
}
/**
* Get the value of NumFolds.
*
* @return Value of NumFolds.
*/
public int getNumFolds() {
return m_numFolds;
}
/**
* Set the value of NumFolds.
*
* @param newNumFolds Value to assign to NumFolds.
*/
public void setNumFolds(int newNumFolds) {
m_numFolds = newNumFolds;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String plotPointsTipText() {
return "A list of recall levels separated by commas or spaces. ";
}
/**
* Get the value of PlotPoints.
*
* @return Value of PlotPoints.
*/
public String getPlotPoints() {
StringBuffer buf = new StringBuffer();
if (m_plotPoints != null)
for (int i=0; i < m_plotPoints.length; i++) {
buf.append(m_plotPoints[i]);
if (i != (m_plotPoints.length -1))
buf.append(" ");
}
return buf.toString();
}
/**
* Set the value of PlotPoints.
*
* @param plotPoints Value to assign to
* PlotPoints.
*/
public void setPlotPoints(String plotPoints) {
m_plotPoints = parsePlotPoints(plotPoints);
}
/**
* Parse a string of doubles separated by commas or spaces into a sorted array of doubles
*/
protected double[] parsePlotPoints(String plotPoints) {
StringTokenizer tokenizer = new StringTokenizer(plotPoints," ,\t");
double[] result = null;
int count = tokenizer.countTokens();
if (count > 0)
result = new double[count];
else
return null;
int i = 0;
while(tokenizer.hasMoreTokens()) {
result[i] = Double.parseDouble(tokenizer.nextToken());
i++;
}
Arrays.sort(result);
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 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;
}
/** Set the mode of creating folds
* @param mode stratified or random
*/
public void setFoldCreationMode(SelectedTag mode) {
if (mode.getTags() == TAGS_FOLD_CREATION_MODE) {
m_foldCreationMode = mode.getSelectedTag().getID();
}
}
/**
* return the fold creation mode
* @return one of stratified or random
*/
public SelectedTag getFoldCreationMode() {
return new SelectedTag(m_foldCreationMode, TAGS_FOLD_CREATION_MODE);
}
/**
* 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 clusterer to apply to the cross validation folds.";
}
/**
* 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(7);
newVector.addElement(new Option(
"\tThe number of folds to use for the cross-validation.\n"
+"\t(default 10)",
"X", 1,
"-X <number of folds>"));
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.ClustererSplitEvaluator",
"W", 1,
"-W <class name>"));
if ((m_splitEvaluator != null) &&
(m_splitEvaluator instanceof OptionHandler)) {
newVector.addElement(new Option(
"",
"", 0, "\nOptions specific to split evaluator "
+ m_splitEvaluator.getClass().getName() + ":"));
Enumeration enum = ((OptionHandler)m_splitEvaluator).listOptions();
while (enum.hasMoreElements()) {
newVector.addElement(enum.nextElement());
}
}
return newVector.elements();
}
/**
* Parses a given list of options. Valid options are:<p>
*
* -X num_folds <br>
* The number of folds to use for the cross-validation. <p>
*
* -D <br>
* Specify that raw split evaluator output is to be saved. <p>
*
* -O file/directory name <br>
* Specify the file or directory to which raw split evaluator output
* is to be saved. If a directory is specified, then each output string
* is saved as an individual gzip file. If a file is specified, then
* each output string is saved as an entry in a zip file. <p>
*
* -W classname <br>
* Specify the full class name of the split evaluator. <p>
*
* All option after -- will be passed to the split evaluator.
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
setRawOutput(Utils.getFlag('D', options));
String fName = Utils.getOption('O', options);
if (fName.length() != 0) {
setOutputFile(new File(fName));
}
String numFolds = Utils.getOption('X', options);
if (numFolds.length() != 0) {
setNumFolds(Integer.parseInt(numFolds));
} else {
setNumFolds(10);
}
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((DeduperSplitEvaluator)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 + 10];
int current = 0;
switch (m_foldCreationMode) {
case FOLD_CREATION_MODE_STRATIFIED:
options[current++] = "-Stratified";
break;
case FOLD_CREATION_MODE_RANDOM:
options[current++] = "-Random";
break;
}
options[current++] = "-X";
options[current++] = "" + getNumFolds();
if (getRawOutput()) {
options[current++] = "-D";
}
options[current++] = "-O";
options[current++] = getOutputFile().getName();
options[current++] = "-P";
options[current++] = getPlotPoints();
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 = "DedupingPRCurveCVResultProducer: ";
result += getCompatibilityState();
if (m_instances == null) {
result += ": <null Instances>";
} else {
result += ": " + Utils.backQuoteChars(m_instances.relationName());
}
return result;
}
// Quick test of timestamp
public static void main(String [] args) {
DedupingPRCurveCVResultProducer rp = new DedupingPRCurveCVResultProducer();
rp.setPlotPoints(args[0]);
System.out.println(rp.getPlotPoints());
}
} // DedupingPRCurveCVResultProducer