BVDecomposeSegCVSub

java.lang.Object
- weka.classifiers.BVDecomposeSegCVSub

All Implemented Interfaces:: OptionHandler, RevisionHandler, TechnicalInformationHandler

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

This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1).
The Kohavi and Wolpert definition of bias and variance is specified in (2).
The Webb definition of bias and variance is specified in (3).

Geoffrey I. Webb, Paul Conilione (2002). Estimating bias and variance from data. School of Computer Science and Software Engineering, Victoria, Australia.

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.

Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. 40(2):159-196.

BibTeX:

 @misc{Webb2002,
    address = {School of Computer Science and Software Engineering, Victoria, Australia},
    author = {Geoffrey I. Webb and Paul Conilione},
    institution = {Monash University},
    title = {Estimating bias and variance from data},
    year = {2002},
    PDF = {http://www.csse.monash.edu.au/\~webb/Files/WebbConilione04.pdf}
 }

 @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}
 }

 @article{Webb2000,
    author = {Geoffrey I. Webb},
    journal = {Machine Learning},
    number = {2},
    pages = {159-196},
    title = {MultiBoosting: A Technique for Combining Boosting and Wagging},
    volume = {40},
    year = {2000}
 }

Valid options are:

 -c <class index>
  The index of the class attribute.
  (default last)

 -D
  Turn on debugging output.

 -l <num>
  The number of times each instance is classified.
  (default 10)

 -p <proportion of objects in common>
  The average proportion of instances common between any two training sets

 -s <seed>
  The random number seed used.

 -t <name of arff file>
  The name of the arff file used for the decomposition.

 -T <number of instances in training set>
  The number of instances in the training set.

 -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: 10141 $
Author:: Paul Conilione (paulc4321@yahoo.com.au)

Constructor Summary

Constructors
Constructor and Description

BVDecomposeSegCVSub()

Constructors
Constructor and Description
`BVDecomposeSegCVSub()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`decompose()` Carry out the bias-variance decomposition using the sub-sampled cross-validation method.
`java.util.Vector<java.lang.Integer>`	`findCentralTendencies(double[] predProbs)` Finds the central tendency, given the classifications for an instance.
`Classifier`	`getClassifier()` Gets the name of the classifier being analysed
`int`	`getClassifyIterations()` Gets the number of times an instance is classified
`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
`double`	`getKWBias()` Get the calculated bias squared according to the Kohavi and Wolpert definition
`double`	`getKWSigma()` Get the calculated sigma according to the Kohavi and Wolpert definition
`double`	`getKWVariance()` Get the calculated variance according to the Kohavi and Wolpert definition
`java.lang.String[]`	`getOptions()` Gets the current settings of the CheckClassifier.
`double`	`getP()` Get the proportion of instances that are common between two training sets.
`java.lang.String`	`getRevision()` Returns the revision string.
`int`	`getSeed()` Gets the random number seed
`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`	`getTrainSize()` Get the training size
`double`	`getWBias()` Get the calculated bias according to the Webb definition
`double`	`getWVariance()` Get the calculated variance according to the Webb definition
`java.lang.String`	`globalInfo()` Returns a string describing this object
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] args)` Test method for this class
`void`	`randomize(int[] index, java.util.Random random)` Accepts an array of ints and randomises the values in the array, using the random seed.
`void`	`setClassifier(Classifier newClassifier)` Set the classifiers being analysed
`void`	`setClassifyIterations(int classifyIterations)` Sets the number of times an instance is classified
`void`	`setClassIndex(int classIndex)` Sets index of attribute to discretize on
`void`	`setDataFileName(java.lang.String dataFileName)` Sets the name of the dataset file.
`void`	`setDebug(boolean debug)` Sets debugging mode
`void`	`setOptions(java.lang.String[] options)` Sets the OptionHandler's options using the given list.
`void`	`setP(double proportion)` Set the proportion of instances that are common between two training sets used to train a classifier.
`void`	`setSeed(int seed)` Sets the random number seed
`void`	`setTrainSize(int size)` Set the training size.
`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

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - BVDecomposeSegCVSub
```
public BVDecomposeSegCVSub()
```
- 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<Option> 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
```
    Sets the OptionHandler's options using the given list. All options will be set (or reset) during this call (i.e. incremental setting of options is not possible).
    Valid options are:
```
 -c <class index>
  The index of the class attribute.
  (default last)
```
```
 -D
  Turn on debugging output.
```
```
 -l <num>
  The number of times each instance is classified.
  (default 10)
```
```
 -p <proportion of objects in common>
  The average proportion of instances common between any two training sets
```
```
 -s <seed>
  The random number seed used.
```
```
 -t <name of arff file>
  The name of the arff file used for the decomposition.
```
```
 -T <number of instances in training set>
  The number of instances in the training set.
```
```
 -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
```
    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
  - 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
  - setClassifyIterations
```
public void setClassifyIterations(int classifyIterations)
```
    Sets the number of times an instance is classified
    
    Parameters:
    
    classifyIterations - number of times an instance is classified
  - getClassifyIterations
```
public int getClassifyIterations()
```
    Gets the number of times an instance is classified
    
    Returns:
    
    the maximum number of times an instance is classified
  - setDataFileName
```
public void setDataFileName(java.lang.String dataFileName)
```
    Sets the name of the dataset file.
    
    Parameters:
    
    dataFileName - name of dataset file.
  - 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
  - getKWBias
```
public double getKWBias()
```
    Get the calculated bias squared according to the Kohavi and Wolpert definition
    
    Returns:
    
    the bias squared
  - getWBias
```
public double getWBias()
```
    Get the calculated bias according to the Webb definition
    
    Returns:
    
    the bias
  - getKWVariance
```
public double getKWVariance()
```
    Get the calculated variance according to the Kohavi and Wolpert definition
    
    Returns:
    
    the variance
  - getWVariance
```
public double getWVariance()
```
    Get the calculated variance according to the Webb definition
    
    Returns:
    
    the variance according to Webb
  - getKWSigma
```
public double getKWSigma()
```
    Get the calculated sigma according to the Kohavi and Wolpert definition
    
    Returns:
    
    the sigma
  - setTrainSize
```
public void setTrainSize(int size)
```
    Set the training size.
    
    Parameters:
    
    size - the size of the training set
  - getTrainSize
```
public int getTrainSize()
```
    Get the training size
    
    Returns:
    
    the size of the training set
  - setP
```
public void setP(double proportion)
```
    Set the proportion of instances that are common between two training sets used to train a classifier.
    
    Parameters:
    
    proportion - the proportion of instances that are common between training sets.
  - getP
```
public double getP()
```
    Get the proportion of instances that are common between two training sets.
    
    Returns:
    
    the proportion
  - 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 using the sub-sampled cross-validation method.
    
    Throws:
    
    java.lang.Exception - if the decomposition couldn't be carried out
  - findCentralTendencies
```
public java.util.Vector<java.lang.Integer> findCentralTendencies(double[] predProbs)
```
    Finds the central tendency, given the classifications for an instance. Where the central tendency is defined as the class that was most commonly selected for a given instance.
    For example, instance 'x' may be classified out of 3 classes y = {1, 2, 3}, so if x is classified 10 times, and is classified as follows, '1' = 2 times, '2' = 5 times and '3' = 3 times. Then the central tendency is '2'.
    However, it is important to note that this method returns a list of all classes that have the highest number of classifications. In cases where there are several classes with the largest number of classifications, then all of these classes are returned. For example if 'x' is classified '1' = 4 times, '2' = 4 times and '3' = 2 times. Then '1' and '2' are returned.
    
    Parameters:
    
    predProbs - the array of classifications for a single instance.
    
    Returns:
    
    a Vector containing Integer objects which store the class(s) which are the central tendency.
  - 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
  - randomize
```
public final void randomize(int[] index,
                            java.util.Random random)
```
    Accepts an array of ints and randomises the values in the array, using the random seed.
    
    Parameters:
    
    index - is the array of integers
    
    random - is the Random seed.

Class BVDecomposeSegCVSub

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

BVDecomposeSegCVSub

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

setClassifier

getClassifier

setDebug

getDebug

setSeed

getSeed

setClassifyIterations

getClassifyIterations

setDataFileName

getDataFileName

getClassIndex

setClassIndex

getKWBias

getWBias

getKWVariance

getWVariance

getKWSigma

setTrainSize

getTrainSize

setP

getP

getError

decompose

findCentralTendencies

toString

getRevision

main

randomize