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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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.
*/
/*
* PairedCorrectedTTester.java
* Copyright (C) 2003 University of Waikato, Hamilton, New Zealand
*
*/
package weka.experiment;
import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformationHandler;
import java.io.BufferedReader;
import java.io.FileReader;
import java.util.Enumeration;
/**
* Behaves the same as PairedTTester, only it uses the corrected
* resampled t-test statistic.
*
* For more information see:
*
* Claude Nadeau, Yoshua Bengio (2001). Inference for the Generalization Error. Machine Learning..
*
*
*
* BibTeX:
*
* @article{Nadeau2001,
* author = {Claude Nadeau and Yoshua Bengio},
* journal = {Machine Learning},
* title = {Inference for the Generalization Error},
* year = {2001},
* PDF = {http://www.iro.umontreal.ca/\~lisa/bib/pub_subject/comparative/pointeurs/nadeau_MLJ1597.pdf}
* }
*
*
*
* Valid options are:
*
* -D <index,index2-index4,...>
* Specify list of columns that specify a unique
* dataset.
* First and last are valid indexes. (default none)
*
* -R <index>
* Set the index of the column containing the run number
*
* -F <index>
* Set the index of the column containing the fold number
*
* -G <index1,index2-index4,...>
* Specify list of columns that specify a unique
* 'result generator' (eg: classifier name and options).
* First and last are valid indexes. (default none)
*
* -S <significance level>
* Set the significance level for comparisons (default 0.05)
*
* -V
* Show standard deviations
*
* -L
* Produce table comparisons in Latex table format
*
* -csv
* Produce table comparisons in CSV table format
*
* -html
* Produce table comparisons in HTML table format
*
* -significance
* Produce table comparisons with only the significance values
*
* -gnuplot
* Produce table comparisons output suitable for GNUPlot
*
*
* @author Richard Kirkby ([email protected])
* @version $Revision: 1.13 $
*/
public class PairedCorrectedTTester
extends PairedTTester
implements TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -3105268939845653323L;
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.ARTICLE);
result.setValue(Field.AUTHOR, "Claude Nadeau and Yoshua Bengio");
result.setValue(Field.YEAR, "2001");
result.setValue(Field.TITLE, "Inference for the Generalization Error");
result.setValue(Field.JOURNAL, "Machine Learning");
result.setValue(Field.PDF, "http://www.iro.umontreal.ca/~lisa/bib/pub_subject/comparative/pointeurs/nadeau_MLJ1597.pdf");
return result;
}
/**
* Computes a paired t-test comparison for a specified dataset between
* two resultsets.
*
* @param datasetSpecifier the dataset specifier
* @param resultset1Index the index of the first resultset
* @param resultset2Index the index of the second resultset
* @param comparisonColumn the column containing values to compare
* @return the results of the paired comparison
* @throws Exception if an error occurs
*/
public PairedStats calculateStatistics(Instance datasetSpecifier,
int resultset1Index,
int resultset2Index,
int comparisonColumn) throws Exception {
if (m_Instances.attribute(comparisonColumn).type()
!= Attribute.NUMERIC) {
throw new Exception("Comparison column " + (comparisonColumn + 1)
+ " ("
+ m_Instances.attribute(comparisonColumn).name()
+ ") is not numeric");
}
if (!m_ResultsetsValid) {
prepareData();
}
Resultset resultset1 = (Resultset) m_Resultsets.elementAt(resultset1Index);
Resultset resultset2 = (Resultset) m_Resultsets.elementAt(resultset2Index);
FastVector dataset1 = resultset1.dataset(datasetSpecifier);
FastVector dataset2 = resultset2.dataset(datasetSpecifier);
String datasetName = templateString(datasetSpecifier);
if (dataset1 == null) {
throw new Exception("No results for dataset=" + datasetName
+ " for resultset=" + resultset1.templateString());
} else if (dataset2 == null) {
throw new Exception("No results for dataset=" + datasetName
+ " for resultset=" + resultset2.templateString());
} else if (dataset1.size() != dataset2.size()) {
throw new Exception("Results for dataset=" + datasetName
+ " differ in size for resultset="
+ resultset1.templateString()
+ " and resultset="
+ resultset2.templateString()
);
}
// calculate the test/train ratio
double testTrainRatio = 0.0;
int trainSizeIndex = -1;
int testSizeIndex = -1;
// find the columns with the train/test sizes
for (int i=0; i= 0 && testSizeIndex >= 0) {
double totalTrainSize = 0.0;
double totalTestSize = 0.0;
for (int k = 0; k < dataset1.size(); k ++) {
Instance current = (Instance) dataset1.elementAt(k);
totalTrainSize += current.value(trainSizeIndex);
totalTestSize += current.value(testSizeIndex);
}
testTrainRatio = totalTestSize / totalTrainSize;
}
PairedStats pairedStats =
new PairedStatsCorrected(m_SignificanceLevel, testTrainRatio);
for (int k = 0; k < dataset1.size(); k ++) {
Instance current1 = (Instance) dataset1.elementAt(k);
Instance current2 = (Instance) dataset2.elementAt(k);
if (current1.isMissing(comparisonColumn)) {
System.err.println("Instance has missing value in comparison "
+ "column!\n" + current1);
continue;
}
if (current2.isMissing(comparisonColumn)) {
System.err.println("Instance has missing value in comparison "
+ "column!\n" + current2);
continue;
}
if (current1.value(m_RunColumn) != current2.value(m_RunColumn)) {
System.err.println("Run numbers do not match!\n"
+ current1 + current2);
}
if (m_FoldColumn != -1) {
if (current1.value(m_FoldColumn) != current2.value(m_FoldColumn)) {
System.err.println("Fold numbers do not match!\n"
+ current1 + current2);
}
}
double value1 = current1.value(comparisonColumn);
double value2 = current2.value(comparisonColumn);
pairedStats.add(value1, value2);
}
pairedStats.calculateDerived();
return pairedStats;
}
/**
* Test the class from the command line.
*
* @param args contains options for the instance ttests
*/
public static void main(String args[]) {
try {
PairedCorrectedTTester tt = new PairedCorrectedTTester();
String datasetName = Utils.getOption('t', args);
String compareColStr = Utils.getOption('c', args);
String baseColStr = Utils.getOption('b', args);
boolean summaryOnly = Utils.getFlag('s', args);
boolean rankingOnly = Utils.getFlag('r', args);
try {
if ((datasetName.length() == 0)
|| (compareColStr.length() == 0)) {
throw new Exception("-t and -c options are required");
}
tt.setOptions(args);
Utils.checkForRemainingOptions(args);
} catch (Exception ex) {
String result = "";
Enumeration enu = tt.listOptions();
while (enu.hasMoreElements()) {
Option option = (Option) enu.nextElement();
result += option.synopsis() + '\n'
+ option.description() + '\n';
}
throw new Exception(
"Usage:\n\n"
+ "-t \n"
+ "\tSet the dataset containing data to evaluate\n"
+ "-b \n"
+ "\tSet the resultset to base comparisons against (optional)\n"
+ "-c \n"
+ "\tSet the column to perform a comparison on\n"
+ "-s\n"
+ "\tSummarize wins over all resultset pairs\n\n"
+ "-r\n"
+ "\tGenerate a resultset ranking\n\n"
+ result);
}
Instances data = new Instances(new BufferedReader(
new FileReader(datasetName)));
tt.setInstances(data);
// tt.prepareData();
int compareCol = Integer.parseInt(compareColStr) - 1;
System.out.println(tt.header(compareCol));
if (rankingOnly) {
System.out.println(tt.multiResultsetRanking(compareCol));
} else if (summaryOnly) {
System.out.println(tt.multiResultsetSummary(compareCol));
} else {
System.out.println(tt.resultsetKey());
if (baseColStr.length() == 0) {
for (int i = 0; i < tt.getNumResultsets(); i++) {
System.out.println(tt.multiResultsetFull(i, compareCol));
}
} else {
int baseCol = Integer.parseInt(baseColStr) - 1;
System.out.println(tt.multiResultsetFull(baseCol, compareCol));
}
}
} catch(Exception e) {
e.printStackTrace();
System.err.println(e.getMessage());
}
}
/**
* returns the name of the tester
*
* @return the display name
*/
public String getDisplayName() {
return "Paired T-Tester (corrected)";
}
/**
* returns a string that is displayed as tooltip on the "perform test"
* button in the experimenter
*
* @return the string for the tool tip
*/
public String getToolTipText() {
return "Performs test using corrected resampled t-test statistic (Nadeau and Bengio)";
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.13 $");
}
}
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