Package weka.attributeSelection

Class ClassifierAttributeEval

  • All Implemented Interfaces:
    java.io.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
     -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).
     -execution-slots <integer>
  Number of attributes to evaluate in parallel.
  Default = 1 (i.e. no parallelism)
    Version:
    $Revision: 14195 $
    Author:
    Mark Hall (mhall@cs.waikato.ac.nz), FracPete (fracpete at waikato dot ac dot nz)
    See Also:
    Serialized Form

    • Constructor Detail

      • ClassifierAttributeEval

        public ClassifierAttributeEval()
        Constructor.

    • 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<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:
 
         -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).
         -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)
        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

      • getOptions

        public java.lang.String[] getOptions()
        returns the current setup.
        Specified by:
        getOptions in interface OptionHandler
        Returns:
        the options of the current setup

      • leaveOneAttributeOutTipText

        public java.lang.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 java.lang.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​(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

      • buildEvaluator

        public void buildEvaluator​(Instances data)
                    throws java.lang.Exception
        Initializes a ClassifierAttribute attribute evaluator.
        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

      • evaluateAttribute

        public double evaluateAttribute​(int attribute)
                         throws java.lang.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:
        java.lang.Exception - if the attribute could not be evaluated

      • toString

        public java.lang.String toString()
        Return a description of the evaluator.
        Overrides:
        toString in class java.lang.Object
        Returns:
        description as a string

      • main

        public static void main​(java.lang.String[] args)
        Main method for executing this class.
        Parameters:
        args - the options