weka.classifiers.meta
Class MultiScheme

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.MultipleClassifiersCombiner
              |
              +--weka.classifiers.RandomizableMultipleClassifiersCombiner
                    |
                    +--weka.classifiers.meta.MultiScheme

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, Randomizable, java.io.Serializable

public class MultiScheme

extends RandomizableMultipleClassifiersCombiner

Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data. Performance is measured based on percent correct (classification) or mean-squared error (regression).

Valid options from the command line are:

-D
Turn on debugging output.

-S seed
Random number seed (default 1).

-B classifierstring
Classifierstring should contain the full class name of a scheme included for selection followed by options to the classifier (required, option should be used once for each classifier).

-X num_folds
Use cross validation error as the basis for classifier selection. (default 0, is to use error on the training data instead)

Version:

$Revision: 1.17 $

Author:

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

See Also:

Serialized Form

Constructor Summary

MultiScheme()

Method Summary

void	buildClassifier(Instances data) Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
java.lang.String	classifiersTipText() Returns the tip text for this property
java.lang.String	debugTipText() Returns the tip text for this property
double[]	distributionForInstance(Instance instance) Returns class probabilities.
Classifier	getClassifier(int index) Gets a single classifier from the set of available classifiers.
Classifier[]	getClassifiers() Gets the list of possible classifers to choose from.
boolean	getDebug() Get whether debugging is turned on
int	getNumFolds() Gets the number of folds for cross-validation.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
int	getSeed() Gets the random number seed.
java.lang.String	globalInfo() Returns a string describing classifier
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
java.lang.String	seedTipText() Returns the tip text for this property
void	setClassifiers(Classifier[] classifiers) Sets the list of possible classifers to choose from.
void	setDebug(boolean debug) Set debugging mode
void	setNumFolds(int numFolds) Sets the number of folds for cross-validation.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(int seed) Sets the seed for random number generation.
java.lang.String	toString() Output a representation of this classifier

Methods inherited from class weka.classifiers.Classifier

classifyInstance, forName, makeCopies

Methods inherited from class java.lang.Object

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

Constructor Detail

MultiScheme

public MultiScheme()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableMultipleClassifiersCombiner

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:

-D
Turn on debugging output.

-S seed
Random number seed (default 1).

-B classifierstring
Classifierstring should contain the full class name of a scheme included for selection followed by options to the classifier (required, option should be used once for each classifier).

-X num_folds
Use cross validation error as the basis for classifier selection. (default 0, is to use error on the training data instead)

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableMultipleClassifiersCombiner

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 RandomizableMultipleClassifiersCombiner

Returns:

an array of strings suitable for passing to setOptions

classifiersTipText

public java.lang.String classifiersTipText()

Returns the tip text for this property

Overrides:

classifiersTipText in class MultipleClassifiersCombiner

Returns:

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

setClassifiers

public void setClassifiers(Classifier[] classifiers)

Sets the list of possible classifers to choose from.

Overrides:

setClassifiers in class MultipleClassifiersCombiner

Parameters:

classifiers - an array of classifiers with all options set.

getClassifiers

public Classifier[] getClassifiers()

Gets the list of possible classifers to choose from.

Overrides:

getClassifiers in class MultipleClassifiersCombiner

Returns:

the array of Classifiers

getClassifier

public Classifier getClassifier(int index)

Gets a single classifier from the set of available classifiers.

Overrides:

getClassifier in class MultipleClassifiersCombiner

Parameters:

index - the index of the classifier wanted

Returns:

the Classifier

seedTipText

public java.lang.String seedTipText()

Returns the tip text for this property

Overrides:

seedTipText in class RandomizableMultipleClassifiersCombiner

Returns:

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

setSeed

public void setSeed(int seed)

Sets the seed for random number generation.

Specified by:

setSeed in interface Randomizable

Overrides:

setSeed in class RandomizableMultipleClassifiersCombiner

Parameters:

seed - the random number seed

getSeed

public int getSeed()

Gets the random number seed.

Specified by:

getSeed in interface Randomizable

Overrides:

getSeed in class RandomizableMultipleClassifiersCombiner

Returns:

the random number seed

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 cross-validation. A number less than 2 specifies using training error rather than cross-validation.

Returns:

the number of folds for cross-validation

setNumFolds

public void setNumFolds(int numFolds)

Sets the number of folds for cross-validation. A number less than 2 specifies using training error rather than cross-validation.

Parameters:

numFolds - the number of folds for cross-validation

debugTipText

public java.lang.String debugTipText()

Returns the tip text for this property

Overrides:

debugTipText in class Classifier

Returns:

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

setDebug

public void setDebug(boolean debug)

Set debugging mode

Overrides:

setDebug in class Classifier

Parameters:

debug - true if debug output should be printed

getDebug

public boolean getDebug()

Get whether debugging is turned on

Overrides:

getDebug in class Classifier

Returns:

true if debugging output is on

buildClassifier

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

Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.

Specified by:

buildClassifier in class Classifier

Parameters:

data - the training data to be used for generating the boosted classifier.

Throws:

java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

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

Returns class probabilities.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class or the numeric prediction

Throws:

java.lang.Exception - if instance could not be classified successfully

toString

public java.lang.String toString()

Output a representation of this classifier

Overrides:

toString in class java.lang.Object

main

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

Main method for testing this class.

Parameters:

argv - should contain the following arguments: -t training file [-T test file] [-c class index]