weka.classifiers.meta

Class RandomCommittee

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.IteratedSingleClassifierEnhancer

weka.classifiers.ParallelIteratedSingleClassifierEnhancer

weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer

weka.classifiers.meta.RandomCommittee

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, PartitionGenerator, Randomizable, RevisionHandler, WeightedInstancesHandler

public class RandomCommittee
extends RandomizableParallelIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, PartitionGenerator

Class for building an ensemble of randomizable base classifiers. Each base classifiers is built using a different random number seed (but based one the same data). The final prediction is a straight average of the predictions generated by the individual base classifiers.

Valid options are:

 -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.RandomTree)

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

 -K <number of attributes>
  Number of attributes to randomly investigate
  (<1 = int(log(#attributes)+1)).

 -M <minimum number of instances>
  Set minimum number of instances per leaf.

 -S <num>
  Seed for random number generator.
  (default 1)

 -depth <num>
  The maximum depth of the tree, 0 for unlimited.
  (default 0)

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

Options after -- are passed to the designated classifier.

Version:

$Revision: 15800 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

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
RandomCommittee() Constructor.

Method Summary

Modifier and Type	Method and Description
java.lang.String	batchSizeTipText() Tool tip text for this property
void	buildClassifier(Instances data) Builds the committee of randomizable classifiers.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
double[][]	distributionsForInstances(Instances insts) Batch scoring method.
void	generatePartition(Instances data) Builds the classifier to generate a partition.
java.lang.String	getBatchSize() Gets the preferred batch size from the base learner if it implements BatchPredictor.
double[]	getMembershipValues(Instance inst) Computes an array that indicates leaf membership
java.lang.String	getRevision() Returns the revision string.
java.lang.String	globalInfo() Returns a string describing classifier
boolean	implementsMoreEfficientBatchPrediction() Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently
static void	main(java.lang.String[] argv) Main method for testing this class.
int	numElements() Returns the number of elements in the partition.
void	setBatchSize(java.lang.String size) Set the batch size to use.
java.lang.String	toString() Returns description of the committee.

Methods inherited from class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer

getOptions, getSeed, listOptions, seedTipText, setOptions, 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, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.CapabilitiesHandler

getCapabilities

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

RandomCommittee

public RandomCommittee()

Constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

buildClassifier

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

Builds the committee of randomizable classifiers.

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:

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

distributionForInstance

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

java.lang.Exception - if distribution can't be computed successfully

batchSizeTipText

public java.lang.String batchSizeTipText()

Tool tip text for this property

Overrides:

batchSizeTipText in class AbstractClassifier

Returns:

the tool tip for this property

setBatchSize

public void setBatchSize(java.lang.String size)

Set the batch size to use. Gets passed through to the base learner if it implements BatchPredictor. Otherwise it is just ignored.

Specified by:

setBatchSize in interface BatchPredictor

Overrides:

setBatchSize in class AbstractClassifier

Parameters:

size - the batch size to use

getBatchSize

public java.lang.String getBatchSize()

Gets the preferred batch size from the base learner if it implements BatchPredictor. Returns 1 as the preferred batch size otherwise.

Specified by:

getBatchSize in interface BatchPredictor

Overrides:

getBatchSize in class AbstractClassifier

Returns:

the batch size to use

distributionsForInstances

public double[][] distributionsForInstances(Instances insts)
                                     throws java.lang.Exception

Batch scoring method. Calls the appropriate method for the base learner if it implements BatchPredictor. Otherwise it simply calls the distributionForInstance() method repeatedly.

Specified by:

distributionsForInstances in interface BatchPredictor

Overrides:

distributionsForInstances in class AbstractClassifier

Parameters:

insts - the instances to get predictions for

Returns:

an array of probability distributions, one for each instance

Throws:

java.lang.Exception - if a problem occurs

implementsMoreEfficientBatchPrediction

public boolean implementsMoreEfficientBatchPrediction()

Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently

Specified by:

implementsMoreEfficientBatchPrediction in interface BatchPredictor

Overrides:

implementsMoreEfficientBatchPrediction in class AbstractClassifier

Returns:

true if the base classifier can generate batch predictions efficiently

toString

public java.lang.String toString()

Returns description of the committee.

Overrides:

toString in class java.lang.Object

Returns:

description of the committee as a string

generatePartition

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

Builds the classifier to generate a partition.

Specified by:

generatePartition in interface PartitionGenerator

Throws:

java.lang.Exception

getMembershipValues

public double[] getMembershipValues(Instance inst)
                             throws java.lang.Exception

Computes an array that indicates leaf membership

Specified by:

getMembershipValues in interface PartitionGenerator

Throws:

java.lang.Exception

numElements

public int numElements()
                throws java.lang.Exception

Returns the number of elements in the partition.

Specified by:

numElements in interface PartitionGenerator

Throws:

java.lang.Exception

getRevision

public java.lang.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(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options