WrapperSubsetEval (weka-dev 3.7.6-SNAPSHOT API)

java.lang.Object
- weka.attributeSelection.ASEvaluation
- - weka.attributeSelection.WrapperSubsetEval

All Implemented Interfaces:: 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%))

 -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)

 
 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: 8034 $
Author:: Mark Hall (mhall@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`EVAL_ACCURACY`
`static int`	`EVAL_AUC`
`static int`	`EVAL_AUPRC`
`static int`	`EVAL_DEFAULT`
`static int`	`EVAL_FMEASURE`
`static int`	`EVAL_MAE`
`static int`	`EVAL_RMSE`
`static Tag[]`	`TAGS_EVALUATION`

Constructor Summary

Constructors
Constructor and Description

WrapperSubsetEval()
Constructor.

Constructors
Constructor and Description
`WrapperSubsetEval()` Constructor.

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`buildEvaluator(Instances data)` Generates a attribute evaluator.
`String`	`classifierTipText()` Returns the tip text for this property
`double`	`evaluateSubset(BitSet subset)` Evaluates a subset of attributes
`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[]`	`getOptions()` Gets the current settings of WrapperSubsetEval.
`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
`String`	`globalInfo()` Returns a string describing this attribute evaluator
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] args)` Main method for testing this class.
`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`	`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()` Returns a string describing the wrapper

Methods inherited from class weka.attributeSelection.ASEvaluation
forName, makeCopies, postProcess, runEvaluator

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

- Field Detail
  - EVAL_DEFAULT
```
public static final int EVAL_DEFAULT
```
    See Also:
    Constant Field Values
  - EVAL_ACCURACY
```
public static final int EVAL_ACCURACY
```
    See Also:
    Constant Field Values
  - EVAL_RMSE
```
public static final int EVAL_RMSE
```
    See Also:
    Constant Field Values
  - EVAL_MAE
```
public static final int EVAL_MAE
```
    See Also:
    Constant Field Values
  - EVAL_FMEASURE
```
public static final int EVAL_FMEASURE
```
    See Also:
    Constant Field Values
  - EVAL_AUC
```
public static final int EVAL_AUC
```
    See Also:
    Constant Field Values
  - EVAL_AUPRC
```
public static final int EVAL_AUPRC
```
    See Also:
    Constant Field Values
  - TAGS_EVALUATION
```
public static final Tag[] TAGS_EVALUATION
```
- Constructor Detail
  - WrapperSubsetEval
```
public WrapperSubsetEval()
```
    Constructor. Calls restOptions to set default options
- 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
  - 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 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(String[] options)
                throws 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%))
```
```
 -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)
```
```
 
 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:
    
    Exception - if an option is not supported
  - 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
  - getOptions
```
public 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 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:
    
    Exception - if the evaluator has not been generated successfully
  - evaluateSubset
```
public double evaluateSubset(BitSet subset)
                      throws 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:
    
    Exception - if the subset could not be evaluated
  - toString
```
public String toString()
```
    Returns a string describing the wrapper
    
    Overrides:
    
    toString in class Object
    
    Returns:
    the 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 testing this class.
    
    Parameters:
    args - the options

Class WrapperSubsetEval

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.attributeSelection.ASEvaluation

Methods inherited from class java.lang.Object

Field Detail

EVAL_DEFAULT

EVAL_ACCURACY

EVAL_RMSE

EVAL_MAE

EVAL_FMEASURE

EVAL_AUC

EVAL_AUPRC

TAGS_EVALUATION

Constructor Detail

WrapperSubsetEval

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

evaluationMeasureTipText

getEvaluationMeasure

setEvaluationMeasure

thresholdTipText

setThreshold

getThreshold

foldsTipText

setFolds

getFolds

seedTipText

setSeed

getSeed

classifierTipText

setClassifier

getClassifier

getOptions

getCapabilities

buildEvaluator

evaluateSubset

toString

getRevision

main