Package moa.classifiers.oneclass

Class HSTrees

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.oneclass.HSTrees

All Implemented Interfaces:

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

public class HSTrees
extends AbstractClassifier
implements Classifier, OneClassClassifier

Implements the Streaming Half-Space Trees one-class classifier described in S. C. Tan, K. M. Ting, and T. F. Liu, “Fast anomaly detection for streaming data,” in IJCAI Proceedings-International Joint Conference on Artificial Intelligence, 2011, vol. 22, no. 1, pp. 1511–1516.

Author:

Richard Hugh Moulton

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
FloatOption	anomalyThresholdOption
IntOption	maxDepthOption
IntOption	numTreesOption
FloatOption	sizeLimitOption
IntOption	windowSizeOption

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
HSTrees()

Method Summary

Modifier and Type	Method	Description
double	getAnomalyScore(Instance inst)	Returns the anomaly score for the argument instance.
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
double[]	getVotesForInstance(Instance inst)	Combine the anomaly scores from each HSTree in the forest and convert into a vote score.
void	initialize(java.util.Collection<Instance> trainingPoints)	Initializes the Streaming HS-Trees classifier on the argument trainingPoints.
boolean	isRandomizable()	HSTrees is randomizable.
void	resetLearningImpl()	Reset the classifier's parameters and data structures.
void	trainOnInstanceImpl(Instance inst)	Update the forest with the argument instance

Methods inherited from class moa.classifiers.AbstractClassifier

contextIsCompatible, copy, correctlyClassifies, defineImmutableCapabilities, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPredictionForInstance, getPredictionForInstance, getSubClassifiers, 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.gui.AWTRenderable

getAWTRenderer

Methods inherited from interface moa.capabilities.CapabilitiesHandler

getCapabilities

Methods inherited from interface moa.classifiers.Classifier

copy, correctlyClassifies, getPredictionForInstance, getSubClassifiers, trainOnInstance

Methods inherited from interface moa.learners.Learner

getModel, getModelContext, getModelMeasurements, getPredictionForInstance, getSublearners, getVotesForInstance, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance

Methods inherited from interface moa.MOAObject

getDescription, 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

windowSizeOption

public IntOption windowSizeOption

numTreesOption

public IntOption numTreesOption

maxDepthOption

public IntOption maxDepthOption

anomalyThresholdOption

public FloatOption anomalyThresholdOption

sizeLimitOption

public FloatOption sizeLimitOption

Constructor Detail

HSTrees

public HSTrees()

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()

Reset the classifier's parameters and data structures.

Specified by:

resetLearningImpl in class AbstractClassifier

trainOnInstanceImpl

public void trainOnInstanceImpl(Instance inst)

Update the forest with the argument instance

Specified by:

trainOnInstanceImpl in class AbstractClassifier

Parameters:

inst - the instance to pass to the forest

getVotesForInstance

public double[] getVotesForInstance(Instance inst)

Combine the anomaly scores from each HSTree in the forest and convert into a vote score.

Specified by:

getVotesForInstance in interface Classifier

Specified by:

getVotesForInstance in class AbstractClassifier

Parameters:

inst - the instance to get votes for

Returns:

the votes for the instance's label [normal, outlier]

getAnomalyScore

public double getAnomalyScore(Instance inst)

Returns the anomaly score for the argument instance.

Specified by:

getAnomalyScore in interface OneClassClassifier

Parameters:

inst - the argument instance

Returns:

inst's anomaly score

isRandomizable

public boolean isRandomizable()

HSTrees is randomizable.

Specified by:

isRandomizable in interface Learner<Example<Instance>>

Returns:

true if the learner needs a random seed.

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

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

initialize

public void initialize(java.util.Collection<Instance> trainingPoints)

Initializes the Streaming HS-Trees classifier on the argument trainingPoints.

Specified by:

initialize in interface OneClassClassifier

Parameters:

trainingPoints - the Collection of instance with which to initialize the Streaming Hs-Trees classifier.