weka.attributeSelection

Class ClassifierAttributeEval

java.lang.Object

weka.attributeSelection.ASEvaluation

weka.attributeSelection.ClassifierAttributeEval

All Implemented Interfaces:

Serializable, AttributeEvaluator, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler

public class ClassifierAttributeEval
extends ASEvaluation
implements AttributeEvaluator, OptionHandler

ClassifierAttributeEval :

Evaluates the worth of an attribute by using a user-specified classifier.

Valid options are:

 -L
  Evaluate an attribute by measuring the impact of leaving it out
  from the full set instead of considering its worth in isolation

 -execution-slots <integer>
  Number of attributes to evaluate in parallel.
  Default = 1 (i.e. no parallelism)

 -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%))

 -E <acc | rmse | mae | f-meas | auc | auprc>
  Performance evaluation measure to use for selecting attributes.
  (Default = accuracy for discrete class and rmse for numeric class)

 -IRclass <label | index>
  Optional class value (label or 1-based index) to use in conjunction with
  IR statistics (f-meas, auc or auprc). Omitting this option will use
  the class-weighted average.

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

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

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

Version:

$Revision: 14195 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz), FracPete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ClassifierAttributeEval() Constructor.

Method Summary

Modifier and Type	Method and Description
void	buildEvaluator(Instances data) Initializes a ClassifierAttribute attribute evaluator.
String	classifierTipText() Returns the tip text for this property
double	evaluateAttribute(int attribute) Evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
String	evaluationMeasureTipText() Returns the tip text for this property
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.
SelectedTag	getEvaluationMeasure() Gets the currently set performance evaluation measure used for selecting attributes for the decision table
int	getFolds() Get the number of folds used for accuracy estimation
String	getIRClassValue() Get the class value (label or index) to use with IR metric evaluation of subsets.
boolean	getLeaveOneAttributeOut() Get whether to evaluate the merit of an attribute based on the impact of leaving it out from the full set instead of considering its worth in isolation
int	getNumToEvaluateInParallel() Get the number of attributes to evaluate in parallel
String[]	getOptions() returns the current setup.
String	getRevision() Returns the revision string.
int	getSeed() Get the random number seed used for cross validation
double	getThreshold() Get the value of the threshold
String	globalInfo() Returns a string describing this attribute evaluator.
String	IRClassValueTipText() Returns the tip text for this property
String	leaveOneAttributeOutTipText() Tip text for this property
Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
static void	main(String[] args) Main method for executing this class.
String	numToEvaluateInParallelTipText() Tip text for this property.
String	seedTipText() Returns the tip text for this property
void	setClassifier(Classifier newClassifier) Set the classifier to use for accuracy estimation
void	setEvaluationMeasure(SelectedTag newMethod) Sets the performance evaluation measure to use for selecting attributes for the decision table
void	setFolds(int f) Set the number of folds to use for accuracy estimation
void	setIRClassValue(String val) Set the class value (label or index) to use with IR metric evaluation of subsets.
void	setLeaveOneAttributeOut(boolean l) Set whether to evaluate the merit of an attribute based on the impact of leaving it out from the full set instead of considering its worth in isolation
void	setNumToEvaluateInParallel(int n) Set the number of attributes to evaluate in parallel
void	setOptions(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
String	thresholdTipText() Returns the tip text for this property
String	toString() Return a description of the evaluator.

Methods inherited from class weka.attributeSelection.ASEvaluation

clean, doNotCheckCapabilitiesTipText, forName, getDoNotCheckCapabilities, makeCopies, postExecution, postProcess, preExecution, run, runEvaluator, setDoNotCheckCapabilities

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

ClassifierAttributeEval

public ClassifierAttributeEval()

Constructor.

Method Detail

globalInfo

public 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 Enumeration<Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class ASEvaluation

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -L
  Evaluate an attribute by measuring the impact of leaving it out
  from the full set instead of considering its worth in isolation

 -execution-slots <integer>
  Number of attributes to evaluate in parallel.
  Default = 1 (i.e. no parallelism)

 -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%))

 -E <acc | rmse | mae | f-meas | auc | auprc>
  Performance evaluation measure to use for selecting attributes.
  (Default = accuracy for discrete class and rmse for numeric class)

 -IRclass <label | index>
  Optional class value (label or 1-based index) to use in conjunction with
  IR statistics (f-meas, auc or auprc). Omitting this option will use
  the class-weighted average.

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

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

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class ASEvaluation

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public String[] getOptions()

returns the current setup.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class ASEvaluation

Returns:

the options of the current setup

leaveOneAttributeOutTipText

public String leaveOneAttributeOutTipText()

Tip text for this property

Returns:

the tip text for this property

setLeaveOneAttributeOut

public void setLeaveOneAttributeOut(boolean l)

Set whether to evaluate the merit of an attribute based on the impact of leaving it out from the full set instead of considering its worth in isolation

Parameters:

l - true if each attribute should be evaluated by measuring the impact of leaving it out from the full set

getLeaveOneAttributeOut

public boolean getLeaveOneAttributeOut()

Get whether to evaluate the merit of an attribute based on the impact of leaving it out from the full set instead of considering its worth in isolation

Returns:

true if each attribute should be evaluated by measuring the impact of leaving it out from the full set

numToEvaluateInParallelTipText

public String numToEvaluateInParallelTipText()

Tip text for this property.

Returns:

the tip text for this property

setNumToEvaluateInParallel

public void setNumToEvaluateInParallel(int n)

Set the number of attributes to evaluate in parallel

Parameters:

n - the number of attributes to evaluate in parallel

getNumToEvaluateInParallel

public int getNumToEvaluateInParallel()

Get the number of attributes to evaluate in parallel

Returns:

the number of attributes to evaluate in parallel

setIRClassValue

public void setIRClassValue(String val)

Set the class value (label or index) to use with IR metric evaluation of subsets. Leaving this unset will result in the class weighted average for the IR metric being used.

Parameters:

val - the class label or 1-based index of the class label to use when evaluating subsets with an IR metric

getIRClassValue

public String getIRClassValue()

Get the class value (label or index) to use with IR metric evaluation of subsets. Leaving this unset will result in the class weighted average for the IR metric being used.

Returns:

the class label or 1-based index of the class label to use when evaluating subsets with an IR metric

IRClassValueTipText

public String IRClassValueTipText()

Returns the tip text for this property

Returns:

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

evaluationMeasureTipText

public String evaluationMeasureTipText()

Returns the tip text for this property

Returns:

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

getEvaluationMeasure

public SelectedTag getEvaluationMeasure()

Gets the currently set performance evaluation measure used for selecting attributes for the decision table

Returns:

the performance evaluation measure

setEvaluationMeasure

public void setEvaluationMeasure(SelectedTag newMethod)

Sets the performance evaluation measure to use for selecting attributes for the decision table

Parameters:

newMethod - the new performance evaluation metric to use

thresholdTipText

public 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 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 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 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

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 Exception

Initializes a ClassifierAttribute attribute evaluator.

Specified by:

buildEvaluator in class ASEvaluation

Parameters:

data - set of instances serving as training data

Throws:

Exception - if the evaluator has not been generated successfully

evaluateAttribute

public double evaluateAttribute(int attribute)
                         throws Exception

Evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.

Specified by:

evaluateAttribute in interface AttributeEvaluator

Parameters:

attribute - the index of the attribute to be evaluated

Returns:

the evaluation

Throws:

Exception - if the attribute could not be evaluated

toString

public String toString()

Return a description of the evaluator.

Overrides:

toString in class Object

Returns:

description as a string

getRevision

public 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(String[] args)

Main method for executing this class.

Parameters:

args - the options