BVDecompose

java.lang.Object
- weka.classifiers.BVDecompose

All Implemented Interfaces:: OptionHandler, RevisionHandler, TechnicalInformationHandler

public class BVDecompose
extends java.lang.Object
implements OptionHandler, TechnicalInformationHandler, RevisionHandler

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.

Version:: $Revision: 1.15 $
Author:: Len Trigg (trigg@cs.waikato.ac.nz)

Constructor Summary

Constructors
Constructor and Description

BVDecompose()

Constructors
Constructor and Description
`BVDecompose()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`decompose()` Carry out the bias-variance decomposition
`double`	`getBias()` Get the calculated bias squared
`Classifier`	`getClassifier()` Gets the name of the classifier being analysed
`int`	`getClassIndex()` Get the index (starting from 1) of the attribute used as the class.
`java.lang.String`	`getDataFileName()` Get the name of the data file used for the decomposition
`boolean`	`getDebug()` Gets whether debugging is turned on
`double`	`getError()` Get the calculated error rate
`java.lang.String[]`	`getOptions()` Gets the current settings of the CheckClassifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`int`	`getSeed()` Gets the random number seed
`double`	`getSigma()` Get the calculated sigma squared
`TechnicalInformation`	`getTechnicalInformation()` 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.
`int`	`getTrainIterations()` Gets the maximum number of boost iterations
`int`	`getTrainPoolSize()` Get the number of instances in the training pool.
`double`	`getVariance()` Get the calculated variance
`java.lang.String`	`globalInfo()` Returns a string describing this object
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] args)` Test method for this class
`void`	`setClassifier(Classifier newClassifier)` Set the classifiers being analysed
`void`	`setClassIndex(int classIndex)` Sets index of attribute to discretize on
`void`	`setDataFileName(java.lang.String dataFileName)` Sets the name of the data file used for the decomposition
`void`	`setDebug(boolean debug)` Sets debugging mode
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setSeed(int seed)` Sets the random number seed
`void`	`setTrainIterations(int trainIterations)` Sets the maximum number of boost iterations
`void`	`setTrainPoolSize(int numTrain)` Set the number of instances in the training pool.
`java.lang.String`	`toString()` Returns description of the bias-variance decomposition results.

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - BVDecompose
```
public BVDecompose()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing this object
    
    Returns:
    
    a description of the classifier suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public TechnicalInformation getTechnicalInformation()
```
    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.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - listOptions
```
public java.util.Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    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.
    Specified by:
    
    setOptions in interface OptionHandler
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the CheckClassifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - getTrainPoolSize
```
public int getTrainPoolSize()
```
    Get the number of instances in the training pool.
    
    Returns:
    
    number of instances in the training pool.
  - setTrainPoolSize
```
public void setTrainPoolSize(int numTrain)
```
    Set the number of instances in the training pool.
    
    Parameters:
    
    numTrain - number of instances in the training pool.
  - setClassifier
```
public void setClassifier(Classifier newClassifier)
```
    Set the classifiers being analysed
    
    Parameters:
    
    newClassifier - the Classifier to use.
  - getClassifier
```
public Classifier getClassifier()
```
    Gets the name of the classifier being analysed
    
    Returns:
    
    the classifier being analysed.
  - setDebug
```
public void setDebug(boolean debug)
```
    Sets debugging mode
    
    Parameters:
    
    debug - true if debug output should be printed
  - getDebug
```
public boolean getDebug()
```
    Gets whether debugging is turned on
    
    Returns:
    
    true if debugging output is on
  - setSeed
```
public void setSeed(int seed)
```
    Sets the random number seed
    
    Parameters:
    
    seed - the random number seed
  - getSeed
```
public int getSeed()
```
    Gets the random number seed
    
    Returns:
    
    the random number seed
  - setTrainIterations
```
public void setTrainIterations(int trainIterations)
```
    Sets the maximum number of boost iterations
    
    Parameters:
    
    trainIterations - the number of boost iterations
  - getTrainIterations
```
public int getTrainIterations()
```
    Gets the maximum number of boost iterations
    
    Returns:
    
    the maximum number of boost iterations
  - setDataFileName
```
public void setDataFileName(java.lang.String dataFileName)
```
    Sets the name of the data file used for the decomposition
    
    Parameters:
    
    dataFileName - the data file to use
  - getDataFileName
```
public java.lang.String getDataFileName()
```
    Get the name of the data file used for the decomposition
    
    Returns:
    
    the name of the data file
  - getClassIndex
```
public int getClassIndex()
```
    Get the index (starting from 1) of the attribute used as the class.
    
    Returns:
    
    the index of the class attribute
  - setClassIndex
```
public void setClassIndex(int classIndex)
```
    Sets index of attribute to discretize on
    
    Parameters:
    
    classIndex - the index (starting from 1) of the class attribute
  - getBias
```
public double getBias()
```
    Get the calculated bias squared
    
    Returns:
    
    the bias squared
  - getVariance
```
public double getVariance()
```
    Get the calculated variance
    
    Returns:
    
    the variance
  - getSigma
```
public double getSigma()
```
    Get the calculated sigma squared
    
    Returns:
    
    the sigma squared
  - getError
```
public double getError()
```
    Get the calculated error rate
    
    Returns:
    
    the error rate
  - decompose
```
public void decompose()
               throws java.lang.Exception
```
    Carry out the bias-variance decomposition
    
    Throws:
    
    java.lang.Exception - if the decomposition couldn't be carried out
  - toString
```
public java.lang.String toString()
```
    Returns description of the bias-variance decomposition results.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    the bias-variance decomposition results as a string
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] args)
```
    Test method for this class
    
    Parameters:
    
    args - the command line arguments

Class BVDecompose

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

BVDecompose

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

getTrainPoolSize

setTrainPoolSize

setClassifier

getClassifier

setDebug

getDebug

setSeed

getSeed

setTrainIterations

getTrainIterations

setDataFileName

getDataFileName

getClassIndex

setClassIndex

getBias

getVariance

getSigma

getError

decompose

toString

getRevision

main