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## 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: 7433 $
Author:
  Mark Hall (mhall@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`WrapperSubsetEval()` Constructor.

Method Summary
`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
`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

### main

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

  Main method for testing this class.

  Parameters:
    `args` - the options

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