weka.classifiers.meta
Class CVParameterSelection

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.SingleClassifierEnhancer
          weka.classifiers.RandomizableSingleClassifierEnhancer
              weka.classifiers.meta.CVParameterSelection

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, Drawable, OptionHandler, Randomizable, RevisionHandler, Summarizable, TechnicalInformationHandler

public class CVParameterSelection
extends RandomizableSingleClassifierEnhancer
implements Drawable, Summarizable, TechnicalInformationHandler

Class for performing parameter selection by cross-validation for any classifier.

For more information, see:

R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.

BibTeX:

 @phdthesis{Kohavi1995,
    address = {Department of Computer Science, Stanford University},
    author = {R. Kohavi},
    school = {Stanford University},
    title = {Wrappers for Performance Enhancement and Oblivious Decision Graphs},
    year = {1995}
 }
 

Valid options are:

 -X <number of folds>
  Number of folds used for cross validation (default 10).

 -P <classifier parameter>
  Classifier parameter options.
  eg: "N 1 5 10" Sets an optimisation parameter for the
  classifier with name -N, with lower bound 1, upper bound
  5, and 10 optimisation steps. The upper bound may be the
  character 'A' or 'I' to substitute the number of
  attributes or instances in the training data,
  respectively. This parameter may be supplied more than
  once to optimise over several classifier options
  simultaneously.

 -S <num>
  Random number seed.
  (default 1)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.rules.ZeroR)

 
 Options specific to classifier 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-classifier.

Version:

$Revision: 5790 $

Author:

Len Trigg ([email protected])

See Also:

Serialized Form

Field Summary

Fields inherited from interface weka.core.Drawable

BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

CVParameterSelection()

Method Summary

void	addCVParameter(java.lang.String cvParam) Adds a scheme parameter to the list of parameters to be set by cross-validation
void	buildClassifier(Instances instances) Generates the classifier.
java.lang.String	CVParametersTipText() Returns the tip text for this property
double[]	distributionForInstance(Instance instance) Predicts the class distribution for the given test instance.
java.lang.String[]	getBestClassifierOptions() Returns (a copy of) the best options found for the classifier.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
java.lang.String	getCVParameter(int index) Gets the scheme paramter with the given index.
java.lang.Object[]	getCVParameters() Get method for CVParameters.
int	getNumFolds() Gets the number of folds for the cross-validation.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.String	getRevision() Returns the revision string.
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.
java.lang.String	globalInfo() Returns a string describing this classifier
java.lang.String	graph() Returns graph describing the classifier (if possible).
int	graphType() Returns the type of graph this classifier represents.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	numFoldsTipText() Returns the tip text for this property
void	setCVParameters(java.lang.Object[] params) Set method for CVParameters.
void	setNumFolds(int numFolds) Sets the number of folds for the cross-validation.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Returns description of the cross-validated classifier.
java.lang.String	toSummaryString() A concise description of the model.

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, setClassifier

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

CVParameterSelection

public CVParameterSelection()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

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 java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableSingleClassifierEnhancer

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:

 -X <number of folds>
  Number of folds used for cross validation (default 10).

 -P <classifier parameter>
  Classifier parameter options.
  eg: "N 1 5 10" Sets an optimisation parameter for the
  classifier with name -N, with lower bound 1, upper bound
  5, and 10 optimisation steps. The upper bound may be the
  character 'A' or 'I' to substitute the number of
  attributes or instances in the training data,
  respectively. This parameter may be supplied more than
  once to optimise over several classifier options
  simultaneously.

 -S <num>
  Random number seed.
  (default 1)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.rules.ZeroR)

 
 Options specific to classifier 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-classifier.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableSingleClassifierEnhancer

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()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class RandomizableSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

getBestClassifierOptions

public java.lang.String[] getBestClassifierOptions()

Returns (a copy of) the best options found for the classifier.

Returns:

the best options

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class SingleClassifierEnhancer

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception

Generates the classifier.

Specified by:

buildClassifier in class Classifier

Parameters:

instances - set of instances serving as training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Predicts the class distribution for the given test instance.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

the predicted class value

Throws:

java.lang.Exception - if an error occurred during the prediction

addCVParameter

public void addCVParameter(java.lang.String cvParam)
                    throws java.lang.Exception

Adds a scheme parameter to the list of parameters to be set by cross-validation

Parameters:

cvParam - the string representation of a scheme parameter. The format is:
param_char lower_bound upper_bound number_of_steps
eg to search a parameter -P from 1 to 10 by increments of 1:
P 1 10 11

Throws:

java.lang.Exception - if the parameter specifier is of the wrong format

getCVParameter

public java.lang.String getCVParameter(int index)

Gets the scheme paramter with the given index.

Parameters:

index - the index for the parameter

Returns:

the scheme parameter

CVParametersTipText

public java.lang.String CVParametersTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getCVParameters

public java.lang.Object[] getCVParameters()

Get method for CVParameters.

Returns:

the CVParameters

setCVParameters

public void setCVParameters(java.lang.Object[] params)
                     throws java.lang.Exception

Set method for CVParameters.

Parameters:

params - the CVParameters to use

Throws:

java.lang.Exception - if the setting of the CVParameters fails

numFoldsTipText

public java.lang.String numFoldsTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getNumFolds

public int getNumFolds()

Gets the number of folds for the cross-validation.

Returns:

the number of folds for the cross-validation

setNumFolds

public void setNumFolds(int numFolds)
                 throws java.lang.Exception

Sets the number of folds for the cross-validation.

Parameters:

numFolds - the number of folds for the cross-validation

Throws:

java.lang.Exception - if parameter illegal

graphType

public int graphType()

Returns the type of graph this classifier represents.

Specified by:

graphType in interface Drawable

Returns:

the type of graph this classifier represents

graph

public java.lang.String graph()
                       throws java.lang.Exception

Returns graph describing the classifier (if possible).

Specified by:

graph in interface Drawable

Returns:

the graph of the classifier in dotty format

Throws:

java.lang.Exception - if the classifier cannot be graphed

toString

public java.lang.String toString()

Returns description of the cross-validated classifier.

Overrides:

toString in class java.lang.Object

Returns:

description of the cross-validated classifier as a string

toSummaryString

public java.lang.String toSummaryString()

A concise description of the model.

Specified by:

toSummaryString in interface Summarizable

Returns:

a concise description of the model

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Classifier

Returns:

the revision

main

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

Main method for testing this class.

Parameters:

argv - the options