weka.classifiers.meta
Class Bagging

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.SingleClassifierEnhancer
          weka.classifiers.IteratedSingleClassifierEnhancer
              weka.classifiers.ParallelIteratedSingleClassifierEnhancer
                  weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer
                      weka.classifiers.meta.Bagging

All Implemented Interfaces:

Serializable, Cloneable, Classifier, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class Bagging
extends RandomizableParallelIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler

Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner.

For more information, see

Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.

BibTeX:

 @article{Breiman1996,
    author = {Leo Breiman},
    journal = {Machine Learning},
    number = {2},
    pages = {123-140},
    title = {Bagging predictors},
    volume = {24},
    year = {1996}
 }
 

Valid options are:

 -P
  Size of each bag, as a percentage of the
  training set size. (default 100)

 -O
  Calculate the out of bag error.

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

 -I <num>
  Number of iterations.
  (default 10)

 -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.trees.REPTree)

 
 Options specific to classifier weka.classifiers.trees.REPTree:
 

 -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).

 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).

 -N <number of folds>
  Number of folds for reduced error pruning (default 3).

 -S <seed>
  Seed for random data shuffling (default 1).

 -P
  No pruning.

 -L
  Maximum tree depth (default -1, no maximum)

Options after -- are passed to the designated classifier.

Version:

$Revision: 8034 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (len@reeltwo.com), Richard Kirkby (rkirkby@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Bagging()
Constructor.

Method Summary

String	bagSizePercentTipText() Returns the tip text for this property
void	buildClassifier(Instances data) Bagging method.
String	calcOutOfBagTipText() Returns the tip text for this property
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names.
int	getBagSizePercent() Gets the size of each bag, as a percentage of the training set size.
boolean	getCalcOutOfBag() Get whether the out of bag error is calculated.
double	getMeasure(String additionalMeasureName) Returns the value of the named measure.
String[]	getOptions() Gets the current settings of the Classifier.
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.
String	globalInfo() Returns a string describing classifier
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for testing this class.
double	measureOutOfBagError() Gets the out of bag error that was calculated as the classifier was built.
Instances	resampleWithWeights(Instances data, Random random, boolean[] sampled) Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
void	setBagSizePercent(int newBagSizePercent) Sets the size of each bag, as a percentage of the training set size.
void	setCalcOutOfBag(boolean calcOutOfBag) Set whether the out of bag error is calculated.
void	setOptions(String[] options) Parses a given list of options.
String	toString() Returns description of the bagged classifier.

Methods inherited from class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.ParallelIteratedSingleClassifierEnhancer

getNumExecutionSlots, numExecutionSlotsTipText, setNumExecutionSlots

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

getNumIterations, numIterationsTipText, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getCapabilities, getClassifier, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

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

Methods inherited from class java.lang.Object

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

Constructor Detail

Bagging

public Bagging()

Constructor.

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier

Returns:

a description 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 listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableParallelIteratedSingleClassifierEnhancer

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:

 -P
  Size of each bag, as a percentage of the
  training set size. (default 100)

 -O
  Calculate the out of bag error.

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

 -I <num>
  Number of iterations.
  (default 10)

 -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.trees.REPTree)

 
 Options specific to classifier weka.classifiers.trees.REPTree:
 

 -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).

 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).

 -N <number of folds>
  Number of folds for reduced error pruning (default 3).

 -S <seed>
  Seed for random data shuffling (default 1).

 -P
  No pruning.

 -L
  Maximum tree depth (default -1, no maximum)

Options after -- are passed to the designated classifier.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableParallelIteratedSingleClassifierEnhancer

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 RandomizableParallelIteratedSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

bagSizePercentTipText

public String bagSizePercentTipText()

Returns the tip text for this property

Returns:

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

getBagSizePercent

public int getBagSizePercent()

Gets the size of each bag, as a percentage of the training set size.

Returns:

the bag size, as a percentage.

setBagSizePercent

public void setBagSizePercent(int newBagSizePercent)

Sets the size of each bag, as a percentage of the training set size.

Parameters:

newBagSizePercent - the bag size, as a percentage.

calcOutOfBagTipText

public String calcOutOfBagTipText()

Returns the tip text for this property

Returns:

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

setCalcOutOfBag

public void setCalcOutOfBag(boolean calcOutOfBag)

Set whether the out of bag error is calculated.

Parameters:

calcOutOfBag - whether to calculate the out of bag error

getCalcOutOfBag

public boolean getCalcOutOfBag()

Get whether the out of bag error is calculated.

Returns:

whether the out of bag error is calculated

measureOutOfBagError

public double measureOutOfBagError()

Gets the out of bag error that was calculated as the classifier was built.

Returns:

the out of bag error

enumerateMeasures

public Enumeration enumerateMeasures()

Returns an enumeration of the additional measure names.

Specified by:

enumerateMeasures in interface AdditionalMeasureProducer

Returns:

an enumeration of the measure names

getMeasure

public double getMeasure(String additionalMeasureName)

Returns the value of the named measure.

Specified by:

getMeasure in interface AdditionalMeasureProducer

Parameters:

additionalMeasureName - the name of the measure to query for its value

Returns:

the value of the named measure

Throws:

IllegalArgumentException - if the named measure is not supported

resampleWithWeights

public final Instances resampleWithWeights(Instances data,
                                           Random random,
                                           boolean[] sampled)

Creates a new dataset of the same size using random sampling with replacement according to the given weight vector. The weights of the instances in the new dataset are set to one. The length of the weight vector has to be the same as the number of instances in the dataset, and all weights have to be positive.

Parameters:

data - the data to be sampled from

random - a random number generator

sampled - indicating which instance has been sampled

Returns:

the new dataset

Throws:

IllegalArgumentException - if the weights array is of the wrong length or contains negative weights.

buildClassifier

public void buildClassifier(Instances data)
                     throws Exception

Bagging method.

Specified by:

buildClassifier in interface Classifier

Overrides:

buildClassifier in class ParallelIteratedSingleClassifierEnhancer

Parameters:

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

Throws:

Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws Exception

Calculates the class membership probabilities for the given test instance.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

preedicted class probability distribution

Throws:

Exception - if distribution can't be computed successfully

toString

public String toString()

Returns description of the bagged classifier.

Overrides:

toString in class Object

Returns:

description of the bagged classifier as a string

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 - the options