Package moa.classifiers.meta

Class OzaBag

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.meta.OzaBag

All Implemented Interfaces:

Configurable, java.io.Serializable, CapabilitiesHandler, Classifier, MultiClassClassifier, AWTRenderable, Learner<Example<Instance>>, MOAObject, OptionHandler

Direct Known Subclasses:

OzaBagML

public class OzaBag
extends AbstractClassifier
implements MultiClassClassifier, CapabilitiesHandler

Incremental on-line bagging of Oza and Russell.

Oza and Russell developed online versions of bagging and boosting for Data Streams. They show how the process of sampling bootstrap replicates from training data can be simulated in a data stream context. They observe that the probability that any individual example will be chosen for a replicate tends to a Poisson(1) distribution.

[OR] N. Oza and S. Russell. Online bagging and boosting. In Artificial Intelligence and Statistics 2001, pages 105–112. Morgan Kaufmann, 2001.

Parameters:

• -l : Classifier to train
• -s : The number of models in the bag

Version:

$Revision: 7 $

Author:

Richard Kirkby ([email protected])

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
ClassOption	baseLearnerOption
protected Classifier[]	ensemble
IntOption	ensembleSizeOption

Fields inherited from class moa.classifiers.AbstractClassifier

classifierRandom, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel

Fields inherited from class moa.options.AbstractOptionHandler

config

Constructor Summary

Constructors

Constructor	Description
OzaBag()

Method Summary

Modifier and Type	Method	Description
ImmutableCapabilities	defineImmutableCapabilities()	Defines the set of capabilities the object has.
void	getModelDescription(java.lang.StringBuilder out, int indent)	Returns a string representation of the model.
protected Measurement[]	getModelMeasurementsImpl()	Gets the current measurements of this classifier. The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods.
java.lang.String	getPurposeString()	Dictionary with option texts and objects
Classifier[]	getSubClassifiers()	Gets the classifiers of this ensemble.
double[]	getVotesForInstance(Instance inst)	Predicts the class memberships for a given instance.
boolean	isRandomizable()	Gets whether this learner needs a random seed.
void	resetLearningImpl()	Resets this classifier.
void	trainOnInstanceImpl(Instance inst)	Trains this classifier incrementally using the given instance. The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods.

Methods inherited from class moa.classifiers.AbstractClassifier

contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPredictionForInstance, getPredictionForInstance, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance, trainOnInstance

Methods inherited from class moa.AbstractMOAObject

copy, measureByteSize, measureByteSize, toString

Methods inherited from class moa.options.AbstractOptionHandler

getCLICreationString, getOptions, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse

Methods inherited from interface moa.capabilities.CapabilitiesHandler

getCapabilities

Methods inherited from interface moa.MOAObject

measureByteSize

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface moa.options.OptionHandler

getCLICreationString, getOptions, prepareForUse, prepareForUse

Field Detail

baseLearnerOption

public ClassOption baseLearnerOption

ensembleSizeOption

public IntOption ensembleSizeOption

ensemble

protected Classifier[] ensemble

Constructor Detail

OzaBag

public OzaBag()

Method Detail

getPurposeString

public java.lang.String getPurposeString()

Description copied from class: AbstractOptionHandler

Dictionary with option texts and objects

Specified by:

getPurposeString in interface OptionHandler

Overrides:

getPurposeString in class AbstractClassifier

Returns:

the string with the purpose of this object

resetLearningImpl

public void resetLearningImpl()

Description copied from class: AbstractClassifier

Resets this classifier. It must be similar to starting a new classifier from scratch.

The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Specified by:

resetLearningImpl in class AbstractClassifier

trainOnInstanceImpl

public void trainOnInstanceImpl(Instance inst)

Description copied from class: AbstractClassifier

Trains this classifier incrementally using the given instance.

The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Specified by:

trainOnInstanceImpl in class AbstractClassifier

Parameters:

inst - the instance to be used for training

getVotesForInstance

public double[] getVotesForInstance(Instance inst)

Description copied from interface: Classifier

Predicts the class memberships for a given instance. If an instance is unclassified, the returned array elements must be all zero.

Specified by:

getVotesForInstance in interface Classifier

Specified by:

getVotesForInstance in class AbstractClassifier

Parameters:

inst - the instance to be classified

Returns:

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

isRandomizable

public boolean isRandomizable()

Description copied from interface: Learner

Gets whether this learner needs a random seed. Examples of methods that needs a random seed are bagging and boosting.

Specified by:

isRandomizable in interface Learner<Example<Instance>>

Returns:

true if the learner needs a random seed.

getModelDescription

public void getModelDescription(java.lang.StringBuilder out,
                                int indent)

Description copied from class: AbstractClassifier

Returns a string representation of the model.

Specified by:

getModelDescription in class AbstractClassifier

Parameters:

out - the stringbuilder to add the description

indent - the number of characters to indent

getModelMeasurementsImpl

protected Measurement[] getModelMeasurementsImpl()

Description copied from class: AbstractClassifier

Gets the current measurements of this classifier.

The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Specified by:

getModelMeasurementsImpl in class AbstractClassifier

Returns:

an array of measurements to be used in evaluation tasks

getSubClassifiers

public Classifier[] getSubClassifiers()

Description copied from interface: Classifier

Gets the classifiers of this ensemble. Returns null if this learner is a single learner.

Specified by:

getSubClassifiers in interface Classifier

Overrides:

getSubClassifiers in class AbstractClassifier

Returns:

an array of the learners of the ensemble

defineImmutableCapabilities

public ImmutableCapabilities defineImmutableCapabilities()

Description copied from interface: CapabilitiesHandler

Defines the set of capabilities the object has. Should be overridden if the object's capabilities do not change.

Specified by:

defineImmutableCapabilities in interface CapabilitiesHandler

Overrides:

defineImmutableCapabilities in class AbstractClassifier

Returns:

The capabilities of the object.