gov.sandia.cognition.learning.algorithm.ensemble

Class IVotingCategorizerLearner.OutOfBagErrorStoppingCriteria<InputType,CategoryType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.algorithm.event.AbstractIterativeAlgorithmListener

gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria<InputType,CategoryType>

gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner.OutOfBagErrorStoppingCriteria<InputType,CategoryType>

Type Parameters:

InputType - The input type the algorithm is learning over.

CategoryType - The category type the algorithm is learning over.

All Implemented Interfaces:

IterativeAlgorithmListener, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

IVotingCategorizerLearner<InputType,CategoryType>

public static class IVotingCategorizerLearner.OutOfBagErrorStoppingCriteria<InputType,CategoryType>
extends AbstractCategorizerOutOfBagStoppingCriteria<InputType,CategoryType>

Implements a stopping criteria for IVoting that uses the out-of-bag error to determine when to stop learning the ensemble. It tracks the out-of-bag error rate of the ensemble and keeps it in a given smoothing window. Once the smoothed error rate stops decreasing, it stops learning and removes all of the ensemble members back to the one that had the minimal error in that window.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected IVotingCategorizerLearner<InputType,CategoryType>	learner The learner the stopping criteria is for.

Fields inherited from class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria

DEFAULT_SMOOTHING_WINDOW_SIZE, outOfBagCorrect, outOfBagErrorCount, previousSmoothedErrorRate, rawErrorRates, smoothedErrorRates, smoothingBuffer, smoothingWindowSize

Constructor Summary

Constructors

Constructor and Description
OutOfBagErrorStoppingCriteria() Creates a new OutOfBagErrorStoppingCriteria.
OutOfBagErrorStoppingCriteria(int smoothingWindowSize) Creates a new OutOfBagErrorStoppingCriteria with the given smoothing window size.

Method Summary

Modifier and Type	Method and Description
void	algorithmStarted(IterativeAlgorithm algorithm) This method is called when a algorithm has started, before the first step of the algorithm.
DataDistribution<CategoryType>	getOutOfBagEstimate(int index) Gets the out-of-bag estimate distribution across categories for the training example with the given index.

Methods inherited from class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria

algorithmEnded, getSmoothingWindowSize, setSmoothingWindowSize, stepEnded

Methods inherited from class gov.sandia.cognition.algorithm.event.AbstractIterativeAlgorithmListener

stepStarted

Methods inherited from class gov.sandia.cognition.util.AbstractCloneableSerializable

clone

Methods inherited from class java.lang.Object

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

Field Detail

learner

protected transient IVotingCategorizerLearner<InputType,CategoryType> learner

The learner the stopping criteria is for.

Constructor Detail

OutOfBagErrorStoppingCriteria

public OutOfBagErrorStoppingCriteria()

Creates a new OutOfBagErrorStoppingCriteria.

OutOfBagErrorStoppingCriteria

public OutOfBagErrorStoppingCriteria(int smoothingWindowSize)

Creates a new OutOfBagErrorStoppingCriteria with the given smoothing window size.

Parameters:

smoothingWindowSize - The smoothing window size to use. Must be positive.

Method Detail

algorithmStarted

public void algorithmStarted(IterativeAlgorithm algorithm)

Description copied from interface: IterativeAlgorithmListener

This method is called when a algorithm has started, before the first step of the algorithm.

Specified by:

algorithmStarted in interface IterativeAlgorithmListener

Overrides:

algorithmStarted in class AbstractCategorizerOutOfBagStoppingCriteria<InputType,CategoryType>

Parameters:

algorithm - The algorithm that has started.

getOutOfBagEstimate

public DataDistribution<CategoryType> getOutOfBagEstimate(int index)

Description copied from class: AbstractCategorizerOutOfBagStoppingCriteria

Gets the out-of-bag estimate distribution across categories for the training example with the given index.

Specified by:

getOutOfBagEstimate in class AbstractCategorizerOutOfBagStoppingCriteria<InputType,CategoryType>

Parameters:

index - The 0-based index for the training example.

Returns:

The distribution over output categories for the out-of-bag estimate for the example at that index. May be empty.