weka.classifiers.meta

Class MultiScheme

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.MultipleClassifiersCombiner

weka.classifiers.RandomizableMultipleClassifiersCombiner

weka.classifiers.meta.MultiScheme

All Implemented Interfaces:

Serializable, Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler

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 are:

 -X <number of folds>
  Use cross validation for model selection using the
  given number of folds. (default 0, is to
  use training error)

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

 -B <classifier specification>
  Full class name of classifier to include, followed
  by scheme options. May be specified multiple times.
  (default: "weka.classifiers.rules.ZeroR")

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

Version:

$Revision: 10141 $

Author:

Len Trigg ([email protected])

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
MultiScheme()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
String	classifiersTipText() Returns the tip text for this property
String	debugTipText() Returns the tip text for this property
double[]	distributionForInstance(Instance instance) Returns class probabilities.
int	getBestClassifierIndex() Get the index of the classifier that was determined as best during cross-validation.
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.
String[]	getOptions() Gets the current settings of the Classifier.
String	getRevision() Returns the revision string.
int	getSeed() Gets the random number seed.
String	globalInfo() Returns a string describing classifier
Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for testing this class.
String	numFoldsTipText() Returns the tip text for this property
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(String[] options) Parses a given list of options.
void	setSeed(int seed) Sets the seed for random number generation.
String	toString() Output a representation of this classifier

Methods inherited from class weka.classifiers.MultipleClassifiersCombiner

getCapabilities, postExecution, preExecution

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Constructor Detail

MultiScheme

public MultiScheme()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

listOptions

public Enumeration<Option> 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(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -X <number of folds>
  Use cross validation for model selection using the
  given number of folds. (default 0, is to
  use training error)

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

 -B <classifier specification>
  Full class name of classifier to include, followed
  by scheme options. May be specified multiple times.
  (default: "weka.classifiers.rules.ZeroR")

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

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableMultipleClassifiersCombiner

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 Classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class RandomizableMultipleClassifiersCombiner

Returns:

an array of strings suitable for passing to setOptions

classifiersTipText

public 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 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 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 String debugTipText()

Returns the tip text for this property

Overrides:

debugTipText in class AbstractClassifier

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 AbstractClassifier

Parameters:

debug - true if debug output should be printed

getDebug

public boolean getDebug()

Get whether debugging is turned on

Overrides:

getDebug in class AbstractClassifier

Returns:

true if debugging output is on

getBestClassifierIndex

public int getBestClassifierIndex()

Get the index of the classifier that was determined as best during cross-validation.

Returns:

the index in the classifier array

buildClassifier

public void buildClassifier(Instances data)
                     throws Exception

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

Parameters:

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

Throws:

Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws Exception

Returns class probabilities.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

the distribution for the instance

Throws:

Exception - if instance could not be classified successfully

toString

public String toString()

Output a representation of this classifier

Overrides:

toString in class Object

Returns:

a string representation of the classifier

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

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