Package weka.attributeSelection

Class WrapperSubsetEval

  • All Implemented Interfaces:
    java.io.Serializable, SubsetEvaluator, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, 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)
 
     -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:
 
     -D
  If set, classifier is run in debug mode and
  may output additional info to the console
    Version:
    $Revision: 12170 $
    Author:
    Mark Hall (mhall@cs.waikato.ac.nz)
    See Also:
    Serialized Form

    • 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<Option> 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%))
 
         -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:
 
         -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

      • setIRClassValue

        public void setIRClassValue​(java.lang.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 java.lang.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 java.lang.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 java.lang.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 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()

      • 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

      • 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