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Class weka.classifiers.CVParameterSelection

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.CVParameterSelection

public class CVParameterSelection

extends Classifier

implements OptionHandler, Summarizable

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

Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of classifier to perform cross-validation selection on.

-X num
Number of folds used for cross validation (default 10).

-S seed
Random number seed (default 1).

-P "N 1 5 10"
Sets an optimisation parameter for the classifier with name -N, 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.

Options after -- are passed to the designated sub-classifier.

Version:

$Revision: 1.10 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz)

CVParameterSelection()

addCVParameter(String)

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

buildClassifier(Instances)

Generates the classifier.

classifyInstance(Instance)

Predicts the class value for the given test instance.

getClassifier()

Get the classifier used as the classifier

getCVParameter(int)

Gets the scheme paramter with the given index.

getDebug()

Gets whether debugging is turned on

getNumFolds()

Get the number of folds used for cross-validation.

getOptions()

Gets the current settings of the Classifier.

getSeed()

Gets the random number seed.

listOptions()

Returns an enumeration describing the available options

main(String[])

Main method for testing this class.

setClassifier(Classifier)

Set the classifier for boosting.

setDebug(boolean)

Sets debugging mode

setNumFolds(int)

Set the number of folds used for cross-validation.

setOptions(String[])

Parses a given list of options.

setSeed(int)

Sets the seed for random number generation.

toString()

Returns description of the cross-validated classifier.

toSummaryString()

 public CVParameterSelection()

 public Enumeration listOptions()

Returns an enumeration describing the available options

Returns:

an enumeration of all the available options

 public void setOptions(String options[]) throws Exception

Parses a given list of options. Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of classifier to perform cross-validation selection on.

-X num
Number of folds used for cross validation (default 10).

-S seed
Random number seed (default 1).

-P "N 1 5 10"
Sets an optimisation parameter for the classifier with name -N, 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.

Options after -- are passed to the designated sub-classifier.

Parameters:

options - the list of options as an array of strings

Throws: Exception

if an option is not supported

 public String[] getOptions()

Gets the current settings of the Classifier.

Returns:

an array of strings suitable for passing to setOptions

 public void buildClassifier(Instances instances) throws Exception

Generates the classifier.

Parameters:

instances - set of instances serving as training data

Throws: Exception

if the classifier has not been generated successfully

Overrides:

buildClassifier in class Classifier

 public double classifyInstance(Instance instance) throws Exception

Predicts the class value for the given test instance.

Parameters:

instance - the instance to be classified

Returns:

the predicted class value

Throws: Exception

if an error occurred during the prediction

Overrides:

classifyInstance in class Classifier

 public void setSeed(int seed)

Sets the seed for random number generation.

Parameters:

seed - the random number seed

 public int getSeed()

Gets the random number seed.

Returns:

the random number seed

 public void addCVParameter(String cvParam) throws 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 increment
eg to search a parameter -P from 1 to 10 by increments of 2:
P 1 10 2

Throws: Exception

if the parameter specifier is of the wrong format

 public String getCVParameter(int index)

Gets the scheme paramter with the given index.

 public void setDebug(boolean debug)

Sets debugging mode

Parameters:

debug - true if debug output should be printed

 public boolean getDebug()

Gets whether debugging is turned on

Returns:

true if debugging output is on

 public int getNumFolds()

Get the number of folds used for cross-validation.

Returns:

the number of folds used for cross-validation.

 public void setNumFolds(int newNumFolds)

Set the number of folds used for cross-validation.

Parameters:

newNumFolds - the number of folds used for cross-validation.

 public void setClassifier(Classifier newClassifier)

Set the classifier for boosting.

Parameters:

newClassifier - the Classifier to use.

 public Classifier getClassifier()

Get the classifier used as the classifier

Returns:

the classifier used as the classifier

 public String toString()

Returns description of the cross-validated classifier.

Returns:

description of the cross-validated classifier as a string

Overrides:

toString in class Object

 public String toSummaryString()

 public static void main(String argv[])

Main method for testing this class.

Parameters:

argv - the options

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