moa.classifiers.meta

Class LeveragingBag

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.meta.LeveragingBag

All Implemented Interfaces:

Serializable, Classifier, AWTRenderable, MOAObject, OptionHandler

public class LeveragingBag
extends AbstractClassifier

Leveraging Bagging for evolving data streams using ADWIN. Leveraging Bagging and Leveraging Bagging MC using Random Output Codes ( -o option).

See details in:
Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer. Leveraging Bagging for Evolving Data Streams Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD}, 2010.

Version:

$Revision: 7 $

Author:

Albert Bifet (abifet at cs dot waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected ADWIN[]	ADError
ClassOption	baseLearnerOption
FloatOption	deltaAdwinOption
protected Classifier[]	ensemble
IntOption	ensembleSizeOption
protected boolean	initMatrixCodes
MultiChoiceOption	leveraginBagAlgorithmOption
protected int[][]	matrixCodes
protected int	numberOfChangesDetected
FlagOption	outputCodesOption
FloatOption	weightShrinkOption

Fields inherited from class moa.classifiers.AbstractClassifier

classifierRandom, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel

Fields inherited from class moa.options.AbstractOptionHandler

classOptionNamesToPreparedObjects, options

Constructor Summary

Constructors

Constructor and Description
LeveragingBag()

Method Summary

Methods

Modifier and Type	Method and Description
void	getModelDescription(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.
String	getPurposeString() Gets the purpose of this object
Classifier[]	getSubClassifiers() Gets the classifiers of this ensemble.
double[]	getVotesForInstance(weka.core.Instance inst) Predicts the class memberships for a given instance.
double[]	getVotesForInstanceBinary(weka.core.Instance inst)
boolean	isRandomizable() Gets whether this classifier needs a random seed.
void	resetLearningImpl() Resets this classifier.
void	trainOnInstanceImpl(weka.core.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, getModelContext, getModelMeasurements, getNominalValueString, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance

Methods inherited from class moa.options.AbstractOptionHandler

discoverOptionsViaReflection, getCLICreationString, getOptions, getPreparedClassOption, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse

Methods inherited from class moa.AbstractMOAObject

copy, measureByteSize, measureByteSize, toString

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

Methods inherited from interface moa.MOAObject

measureByteSize

Field Detail

baseLearnerOption

public ClassOption baseLearnerOption

ensembleSizeOption

public IntOption ensembleSizeOption

weightShrinkOption

public FloatOption weightShrinkOption

deltaAdwinOption

public FloatOption deltaAdwinOption

outputCodesOption

public FlagOption outputCodesOption

leveraginBagAlgorithmOption

public MultiChoiceOption leveraginBagAlgorithmOption

ensemble

protected Classifier[] ensemble

ADError

protected ADWIN[] ADError

numberOfChangesDetected

protected int numberOfChangesDetected

matrixCodes

protected int[][] matrixCodes

initMatrixCodes

protected boolean initMatrixCodes

Constructor Detail

LeveragingBag

public LeveragingBag()

Method Detail

getPurposeString

public String getPurposeString()

Description copied from interface: OptionHandler

Gets the purpose of this object

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(weka.core.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(weka.core.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.

Parameters:

inst - the instance to be classified

Returns:

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

getVotesForInstanceBinary

public double[] getVotesForInstanceBinary(weka.core.Instance inst)

isRandomizable

public boolean isRandomizable()

Description copied from interface: Classifier

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

Returns:

true if the classifier needs a random seed.

getModelDescription

public void getModelDescription(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 classifier is a single classifier.

Specified by:

getSubClassifiers in interface Classifier

Overrides:

getSubClassifiers in class AbstractClassifier

Returns:

an array of the classifiers of the ensemble