weka.attributeSelection

Class WrapperSubsetEval

java.lang.Object

weka.attributeSelection.ASEvaluation

weka.attributeSelection.WrapperSubsetEval

All Implemented Interfaces:

java.io.Serializable, SubsetEvaluator, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class WrapperSubsetEval
extends ASEvaluation
implements SubsetEvaluator, OptionHandler, TechnicalInformationHandler

WrapperSubsetEval:

Evaluates attribute sets by using a learning scheme. Cross validation is used to estimate the accuracy of the learning scheme for a set of attributes.

For more information see:

Ron Kohavi, George H. John (1997). Wrappers for feature subset selection. Artificial Intelligence. 97(1-2):273-324.

BibTeX:

 @article{Kohavi1997,
    author = {Ron Kohavi and George H. John},
    journal = {Artificial Intelligence},
    note = {Special issue on relevance},
    number = {1-2},
    pages = {273-324},
    title = {Wrappers for feature subset selection},
    volume = {97},
    year = {1997},
    ISSN = {0004-3702}
 }
 

Valid options are:

 -B <base learner>
  class name of base learner to use for  accuracy estimation.
  Place any classifier options LAST on the command line
  following a "--". eg.:
   -B weka.classifiers.bayes.NaiveBayes ... -- -K
  (default: weka.classifiers.rules.ZeroR)

 -F <num>
  number of cross validation folds to use for estimating accuracy.
  (default=5)

 -R <seed>
  Seed for cross validation accuracy testimation.
  (default = 1)

 -T <num>
  threshold by which to execute another cross validation
  (standard deviation---expressed as a percentage of the mean).
  (default: 0.01 (1%))

 
 Options specific to scheme weka.classifiers.rules.ZeroR:
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Version:

$Revision: 11851 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
WrapperSubsetEval() Constructor.

Method Summary

Modifier and Type	Method and Description
void	buildEvaluator(Instances data) Generates a attribute evaluator.
java.lang.String	classifierTipText() Returns the tip text for this property
void	clean() Tells the evaluator that the attribute selection process is complete.
double	evaluateSubset(java.util.BitSet subset) Evaluates a subset of attributes
java.lang.String	foldsTipText() Returns the tip text for this property
Capabilities	getCapabilities() Returns the capabilities of this evaluator.
Classifier	getClassifier() Get the classifier used as the base learner.
int	getFolds() Get the number of folds used for accuracy estimation
java.lang.String[]	getOptions() Gets the current settings of WrapperSubsetEval.
java.lang.String	getRevision() Returns the revision string.
int	getSeed() Get the random number seed used for cross validation
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.
double	getThreshold() Get the value of the threshold
java.lang.String	globalInfo() Returns a string describing this attribute evaluator
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] args) Main method for testing this class.
java.lang.String	seedTipText() Returns the tip text for this property
void	setClassifier(Classifier newClassifier) Set the classifier to use for accuracy estimation
void	setFolds(int f) Set the number of folds to use for accuracy estimation
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(int s) Set the seed to use for cross validation
void	setThreshold(double t) Set the value of the threshold for repeating cross validation
java.lang.String	thresholdTipText() Returns the tip text for this property
java.lang.String	toString() Returns a string describing the wrapper

Methods inherited from class weka.attributeSelection.ASEvaluation

forName, makeCopies, postProcess

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

WrapperSubsetEval

public WrapperSubsetEval()

Constructor. Calls restOptions to set default options

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this attribute evaluator

Returns:

a description of the evaluator 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:

 -B <base learner>
  class name of base learner to use for  accuracy estimation.
  Place any classifier options LAST on the command line
  following a "--". eg.:
   -B weka.classifiers.bayes.NaiveBayes ... -- -K
  (default: weka.classifiers.rules.ZeroR)

 -F <num>
  number of cross validation folds to use for estimating accuracy.
  (default=5)

 -R <seed>
  Seed for cross validation accuracy testimation.
  (default = 1)

 -T <num>
  threshold by which to execute another cross validation
  (standard deviation---expressed as a percentage of the mean).
  (default: 0.01 (1%))

 
 Options specific to scheme 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

thresholdTipText

public java.lang.String thresholdTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setThreshold

public void setThreshold(double t)

Set the value of the threshold for repeating cross validation

Parameters:

t - the value of the threshold

getThreshold

public double getThreshold()

Get the value of the threshold

Returns:

the threshold as a double

foldsTipText

public java.lang.String foldsTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setFolds

public void setFolds(int f)

Set the number of folds to use for accuracy estimation

Parameters:

f - the number of folds

getFolds

public int getFolds()

Get the number of folds used for accuracy estimation

Returns:

the number of folds

seedTipText

public java.lang.String seedTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setSeed

public void setSeed(int s)

Set the seed to use for cross validation

Parameters:

s - the seed

getSeed

public int getSeed()

Get the random number seed used for cross validation

Returns:

the seed

classifierTipText

public java.lang.String classifierTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setClassifier

public void setClassifier(Classifier newClassifier)

Set the classifier to use for accuracy estimation

Parameters:

newClassifier - the Classifier to use.

getClassifier

public Classifier getClassifier()

Get the classifier used as the base learner.

Returns:

the classifier used as the classifier

getOptions

public java.lang.String[] getOptions()

Gets the current settings of WrapperSubsetEval.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions()

getCapabilities

public Capabilities getCapabilities()

Returns the capabilities of this evaluator.

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class ASEvaluation

Returns:

the capabilities of this evaluator

See Also:

Capabilities

buildEvaluator

public void buildEvaluator(Instances data)
                    throws java.lang.Exception

Generates a attribute evaluator. Has to initialize all fields of the evaluator that are not being set via options.

Specified by:

buildEvaluator in class ASEvaluation

Parameters:

data - set of instances serving as training data

Throws:

java.lang.Exception - if the evaluator has not been generated successfully

evaluateSubset

public double evaluateSubset(java.util.BitSet subset)
                      throws java.lang.Exception

Evaluates a subset of attributes

Specified by:

evaluateSubset in interface SubsetEvaluator

Parameters:

subset - a bitset representing the attribute subset to be evaluated

Returns:

the error rate

Throws:

java.lang.Exception - if the subset could not be evaluated

toString

public java.lang.String toString()

Returns a string describing the wrapper

Overrides:

toString in class java.lang.Object

Returns:

the description as a string

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class ASEvaluation

Returns:

the revision

clean

public void clean()

Description copied from class: ASEvaluation

Tells the evaluator that the attribute selection process is complete. It can then clean up data structures, references to training data as necessary in order to save memory

Overrides:

clean in class ASEvaluation

main

public static void main(java.lang.String[] args)

Main method for testing this class.

Parameters:

args - the options