weka.attributeSelection

Class ClassifierSubsetEval

java.lang.Object

weka.attributeSelection.ASEvaluation

weka.attributeSelection.HoldOutSubsetEvaluator

weka.attributeSelection.ClassifierSubsetEval

All Implemented Interfaces:

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

public class ClassifierSubsetEval
extends HoldOutSubsetEvaluator
implements OptionHandler, ErrorBasedMeritEvaluator

Classifier subset evaluator:

Evaluates attribute subsets on training data or a seperate hold out testing set. Uses a classifier to estimate the 'merit' of a set of attributes.

Valid options are:

 -B <classifier>
  class name of the classifier 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)

 -T
  Use the training data to estimate accuracy.

 -H <filename>
  Name of the hold out/test set to 
  estimate accuracy on.

 
 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: 5511 $

Author:

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

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ClassifierSubsetEval()

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
double	evaluateSubset(java.util.BitSet subset) Evaluates a subset of attributes
double	evaluateSubset(java.util.BitSet subset, Instance holdOut, boolean retrain) Evaluates a subset of attributes with respect to a single instance.
double	evaluateSubset(java.util.BitSet subset, Instances holdOut) Evaluates a subset of attributes with respect to a set of instances.
Capabilities	getCapabilities() Returns the capabilities of this evaluator.
Classifier	getClassifier() Get the classifier used as the base learner.
java.io.File	getHoldOutFile() Gets the file that holds hold out/test instances.
java.lang.String[]	getOptions() Gets the current settings of ClassifierSubsetEval
java.lang.String	getRevision() Returns the revision string.
boolean	getUseTraining() Get if training data is to be used instead of hold out/test data
java.lang.String	globalInfo() Returns a string describing this attribute evaluator
java.lang.String	holdOutFileTipText() Returns the tip text for this property
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] args) Main method for testing this class.
void	setClassifier(Classifier newClassifier) Set the classifier to use for accuracy estimation
void	setHoldOutFile(java.io.File h) Set the file that contains hold out/test instances
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setUseTraining(boolean t) Set if training data is to be used instead of hold out/test data
java.lang.String	toString() Returns a string describing classifierSubsetEval
java.lang.String	useTrainingTipText() Returns the tip text for this property

Methods inherited from class weka.attributeSelection.ASEvaluation

clean, forName, makeCopies, postProcess

Methods inherited from class java.lang.Object

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

Constructor Detail

ClassifierSubsetEval

public ClassifierSubsetEval()

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

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 <classifier>
  class name of the classifier 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)

 -T
  Use the training data to estimate accuracy.

 -H <filename>
  Name of the hold out/test set to 
  estimate accuracy on.

 
 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

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

holdOutFileTipText

public java.lang.String holdOutFileTipText()

Returns the tip text for this property

Returns:

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

getHoldOutFile

public java.io.File getHoldOutFile()

Gets the file that holds hold out/test instances.

Returns:

File that contains hold out instances

setHoldOutFile

public void setHoldOutFile(java.io.File h)

Set the file that contains hold out/test instances

Parameters:

h - the hold out file

useTrainingTipText

public java.lang.String useTrainingTipText()

Returns the tip text for this property

Returns:

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

getUseTraining

public boolean getUseTraining()

Get if training data is to be used instead of hold out/test data

Returns:

true if training data is to be used instead of hold out data

setUseTraining

public void setUseTraining(boolean t)

Set if training data is to be used instead of hold out/test data

Parameters:

t - true if training data is to be used instead of hold out data

getOptions

public java.lang.String[] getOptions()

Gets the current settings of ClassifierSubsetEval

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

evaluateSubset

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

Evaluates a subset of attributes with respect to a set of instances. Calling this function overides any test/hold out instancs set from setHoldOutFile.

Specified by:

evaluateSubset in class HoldOutSubsetEvaluator

Parameters:

subset - a bitset representing the attribute subset to be evaluated

holdOut - a set of instances (possibly seperate and distinct from those use to build/train the evaluator) with which to evaluate the merit of the subset

Returns:

the "merit" of the subset on the holdOut data

Throws:

java.lang.Exception - if the subset cannot be evaluated

evaluateSubset

public double evaluateSubset(java.util.BitSet subset,
                             Instance holdOut,
                             boolean retrain)
                      throws java.lang.Exception

Evaluates a subset of attributes with respect to a single instance. Calling this function overides any hold out/test instances set through setHoldOutFile.

Specified by:

evaluateSubset in class HoldOutSubsetEvaluator

Parameters:

subset - a bitset representing the attribute subset to be evaluated

holdOut - a single instance (possibly not one of those used to build/train the evaluator) with which to evaluate the merit of the subset

retrain - true if the classifier should be retrained with respect to the new subset before testing on the holdOut instance.

Returns:

the "merit" of the subset on the holdOut instance

Throws:

java.lang.Exception - if the subset cannot be evaluated

toString

public java.lang.String toString()

Returns a string describing classifierSubsetEval

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

main

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

Main method for testing this class.

Parameters:

args - the options