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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 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 .
*/
/*
* BVDecompose.java
* Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.Reader;
import java.util.Collections;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
import weka.core.Attribute;
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.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
/**
* Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
*
* Ron Kohavi, David H. Wolpert: Bias Plus Variance Decomposition for Zero-One Loss Functions. In: Machine Learning: Proceedings of the Thirteenth International Conference, 275-283, 1996.
*
*
* BibTeX:
*
* @inproceedings{Kohavi1996,
* author = {Ron Kohavi and David H. Wolpert},
* booktitle = {Machine Learning: Proceedings of the Thirteenth International Conference},
* editor = {Lorenza Saitta},
* pages = {275-283},
* publisher = {Morgan Kaufmann},
* title = {Bias Plus Variance Decomposition for Zero-One Loss Functions},
* year = {1996},
* PS = {http://robotics.stanford.edu/\~ronnyk/biasVar.ps}
* }
*
*
*
* Valid options are:
*
*
-c <class index>
* The index of the class attribute.
* (default last)
*
*
-t <name of arff file>
* The name of the arff file used for the decomposition.
*
*
-T <training pool size>
* The number of instances placed in the training pool.
* The remainder will be used for testing. (default 100)
*
*
-s <seed>
* The random number seed used.
*
*
-x <num>
* The number of training repetitions used.
* (default 50)
*
*
-D
* Turn on debugging output.
*
*
-W <classifier class name>
* Full class name of the learner used in the decomposition.
* eg: weka.classifiers.bayes.NaiveBayes
*
*
* Options specific to learner weka.classifiers.rules.ZeroR:
*
*
*
-D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
* Options after -- are passed to the designated sub-learner.
*
* @author Len Trigg ([email protected])
* @version $Revision: 10141 $
*/
public class BVDecompose
implements OptionHandler, TechnicalInformationHandler, RevisionHandler {
/** Debugging mode, gives extra output if true */
protected boolean m_Debug;
/** An instantiated base classifier used for getting and testing options. */
protected Classifier m_Classifier = new weka.classifiers.rules.ZeroR();
/** The options to be passed to the base classifier. */
protected String [] m_ClassifierOptions;
/** The number of train iterations */
protected int m_TrainIterations = 50;
/** The name of the data file used for the decomposition */
protected String m_DataFileName;
/** The index of the class attribute */
protected int m_ClassIndex = -1;
/** The random number seed */
protected int m_Seed = 1;
/** The calculated bias (squared) */
protected double m_Bias;
/** The calculated variance */
protected double m_Variance;
/** The calculated sigma (squared) */
protected double m_Sigma;
/** The error rate */
protected double m_Error;
/** The number of instances used in the training pool */
protected int m_TrainPoolSize = 100;
/**
* Returns a string describing this object
* @return a description of the classifier suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Class for performing a Bias-Variance decomposition on any classifier "
+ "using the method specified in:\n\n"
+ getTechnicalInformation().toString();
}
/**
* 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.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Ron Kohavi and David H. Wolpert");
result.setValue(Field.YEAR, "1996");
result.setValue(Field.TITLE, "Bias Plus Variance Decomposition for Zero-One Loss Functions");
result.setValue(Field.BOOKTITLE, "Machine Learning: Proceedings of the Thirteenth International Conference");
result.setValue(Field.PUBLISHER, "Morgan Kaufmann");
result.setValue(Field.EDITOR, "Lorenza Saitta");
result.setValue(Field.PAGES, "275-283");
result.setValue(Field.PS, "http://robotics.stanford.edu/~ronnyk/biasVar.ps");
return result;
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration
