Package weka.classifiers

Class BVDecompose

  • All Implemented Interfaces:
    OptionHandler, RevisionHandler, TechnicalInformationHandler
    public class BVDecompose
extends Object
implements OptionHandler, TechnicalInformationHandler, RevisionHandler
    Class for performing a Bias-Variance decomposition on any classifier using the method specified in:

    Ron Kohavi, David H. Wolpert: Bias Plus Variance Decomposition for Zero-One Loss Functions. In: Machine Learning: Proceedings of the Thirteenth International Conference, 275-283, 1996.

    BibTeX: 

     @inproceedings{Kohavi1996,
    author = {Ron Kohavi and David H. Wolpert},
    booktitle = {Machine Learning: Proceedings of the Thirteenth International Conference},
    editor = {Lorenza Saitta},
    pages = {275-283},
    publisher = {Morgan Kaufmann},
    title = {Bias Plus Variance Decomposition for Zero-One Loss Functions},
    year = {1996},
    PS = {http://robotics.stanford.edu/\~ronnyk/biasVar.ps}
 }

    Valid options are: 

     -c <class index>
  The index of the class attribute.
  (default last)
     -t <name of arff file>
  The name of the arff file used for the decomposition.
     -T <training pool size>
  The number of instances placed in the training pool.
  The remainder will be used for testing. (default 100)
     -s <seed>
  The random number seed used.
     -x <num>
  The number of training repetitions used.
  (default 50)
     -D
  Turn on debugging output.
     -W <classifier class name>
  Full class name of the learner used in the decomposition.
  eg: weka.classifiers.bayes.NaiveBayes
     Options specific to learner weka.classifiers.rules.ZeroR:
 
     -D
  If set, classifier is run in debug mode and
  may output additional info to the console
    Options after -- are passed to the designated sub-learner.

    Version:
    $Revision$
    Author:
    Len Trigg (trigg@cs.waikato.ac.nz)

    • Constructor Detail

      • BVDecompose

        public BVDecompose()

    • Method Detail

      • globalInfo

        public String globalInfo()
        Returns a string describing this object
        Returns:
        a description of the classifier 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<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​(String[] options)
                throws Exception
        Parses a given list of options.

        Valid options are: 

         -c <class index>
  The index of the class attribute.
  (default last)
         -t <name of arff file>
  The name of the arff file used for the decomposition.
         -T <training pool size>
  The number of instances placed in the training pool.
  The remainder will be used for testing. (default 100)
         -s <seed>
  The random number seed used.
         -x <num>
  The number of training repetitions used.
  (default 50)
         -D
  Turn on debugging output.
         -W <classifier class name>
  Full class name of the learner used in the decomposition.
  eg: weka.classifiers.bayes.NaiveBayes
         Options specific to learner weka.classifiers.rules.ZeroR:
 
         -D
  If set, classifier is run in debug mode and
  may output additional info to the console
        Options after -- are passed to the designated sub-learner.

        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

      • getOptions

        public String[] getOptions()
        Gets the current settings of the CheckClassifier.
        Specified by:
        getOptions in interface OptionHandler
        Returns:
        an array of strings suitable for passing to setOptions

      • getTrainPoolSize

        public int getTrainPoolSize()
        Get the number of instances in the training pool.
        Returns:
        number of instances in the training pool.

      • setTrainPoolSize

        public void setTrainPoolSize​(int numTrain)
        Set the number of instances in the training pool.
        Parameters:
        numTrain - number of instances in the training pool.

      • setClassifier

        public void setClassifier​(Classifier newClassifier)
        Set the classifiers being analysed
        Parameters:
        newClassifier - the Classifier to use.

      • getClassifier

        public Classifier getClassifier()
        Gets the name of the classifier being analysed
        Returns:
        the classifier being analysed.

      • setDebug

        public void setDebug​(boolean debug)
        Sets debugging mode
        Parameters:
        debug - true if debug output should be printed

      • getDebug

        public boolean getDebug()
        Gets whether debugging is turned on
        Returns:
        true if debugging output is on

      • setSeed

        public void setSeed​(int seed)
        Sets the random number seed
        Parameters:
        seed - the random number seed

      • getSeed

        public int getSeed()
        Gets the random number seed
        Returns:
        the random number seed

      • setTrainIterations

        public void setTrainIterations​(int trainIterations)
        Sets the maximum number of boost iterations
        Parameters:
        trainIterations - the number of boost iterations

      • getTrainIterations

        public int getTrainIterations()
        Gets the maximum number of boost iterations
        Returns:
        the maximum number of boost iterations

      • setDataFileName

        public void setDataFileName​(String dataFileName)
        Sets the name of the data file used for the decomposition
        Parameters:
        dataFileName - the data file to use

      • getDataFileName

        public String getDataFileName()
        Get the name of the data file used for the decomposition
        Returns:
        the name of the data file

      • getClassIndex

        public int getClassIndex()
        Get the index (starting from 1) of the attribute used as the class.
        Returns:
        the index of the class attribute

      • setClassIndex

        public void setClassIndex​(int classIndex)
        Sets index of attribute to discretize on
        Parameters:
        classIndex - the index (starting from 1) of the class attribute

      • getBias

        public double getBias()
        Get the calculated bias squared
        Returns:
        the bias squared

      • getVariance

        public double getVariance()
        Get the calculated variance
        Returns:
        the variance

      • getSigma

        public double getSigma()
        Get the calculated sigma squared
        Returns:
        the sigma squared

      • getError

        public double getError()
        Get the calculated error rate
        Returns:
        the error rate

      • decompose

        public void decompose()
               throws Exception
        Carry out the bias-variance decomposition
        Throws:
        Exception - if the decomposition couldn't be carried out

      • toString

        public String toString()
        Returns description of the bias-variance decomposition results.
        Overrides:
        toString in class Object
        Returns:
        the bias-variance decomposition results as a string

      • main

        public static void main​(String[] args)
        Test method for this class
        Parameters:
        args - the command line arguments