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

java.lang.Object
  gov.sandia.cognition.util.AbstractCloneableSerializable
      gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm
          gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm<ResultType>
              gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner<Collection<? extends InputOutputPair<? extends InputType,OutputType>>,ResultType>
                  gov.sandia.cognition.learning.algorithm.AbstractAnytimeSupervisedBatchLearner<InputType,CategoryType,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>
                      gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner<InputType,CategoryType>
                          gov.sandia.cognition.learning.algorithm.ensemble.CategoryBalancedIVotingLearner<InputType,CategoryType>

Type Parameters:

InputType - The type of the input for the categorizer to learn. This is the type passed to the internal batch learner to learn each ensemble member.

CategoryType - The type of the category that is the output for the categorizer to learn. It is also passed to the internal batch learner to learn each ensemble member. It must have a valid equals and hashCode method.

All Implemented Interfaces:

AnytimeAlgorithm<WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>, IterativeAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>, BatchLearner<Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>, BatchLearnerContainer<BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>>, SupervisedBatchLearner<InputType,CategoryType,WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>>>, CloneableSerializable, Randomized, Serializable, Cloneable

public class CategoryBalancedIVotingLearner<InputType,CategoryType>
extends IVotingCategorizerLearner<InputType,CategoryType>

An extension of IVoting for dealing with skew problems that makes sure that there are an equal number of examples from each category in each sample that an ensemble member is trained on.

Since:

3.3.0

Author:

Justin Basilico

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner

IVotingCategorizerLearner.OutOfBagErrorStoppingCriteria<InputType,CategoryType>

Field Summary

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

counterFactory, currentBag, currentCorrectIndices, currentEnsembleCorrect, currentIncorrectIndices, currentMember, currentMemberEstimates, dataFullEstimates, dataInBag, dataList, dataOutOfBagEstimates, DEFAULT_MAX_ITERATIONS, DEFAULT_PERCENT_TO_SAMPLE, DEFAULT_PROPORTION_INCORRECT_IN_SAMPLE, DEFAULT_VOTE_OUT_OF_BAG_ONLY, ensemble, learner, numCorrectToSample, numIncorrectToSample, percentToSample, proportionIncorrectInSample, random, sampleSize, voteOutOfBagOnly

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

data, keepGoing

Fields inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

maxIterations

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

DEFAULT_ITERATION, iteration

Constructor Summary

CategoryBalancedIVotingLearner()
Creates a new CategoryBalancedIVotingLearner.

CategoryBalancedIVotingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner, int maxIterations, double percentToSample, double proportionIncorrectInSample, boolean voteOutOfBagOnly, Factory<? extends DataDistribution<CategoryType>> counterFactory, Random random)
Creates a new CategoryBalancedIVotingLearner.

CategoryBalancedIVotingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner, int maxIterations, double percentToSample, Random random)
Creates a new CategoryBalancedIVotingLearner.

Method Summary

protected void

createBag(ArrayList<Integer> correctIndices, ArrayList<Integer> incorrectIndices) Create the next sample (bag) of examples to learn the next ensemble member from.

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

cleanupAlgorithm, getCounterFactory, getCurrentEnsembleCorrect, getDataFullEstimates, getDataOutOfBagEstimates, getLearner, getPercentToSample, getProportionIncorrectInSample, getRandom, getResult, initializeAlgorithm, isVoteOutOfBagOnly, sampleIndicesWithReplacementInto, setCounterFactory, setLearner, setPercentToSample, setProportionIncorrectInSample, setRandom, setVoteOutOfBagOnly, step

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

clone, getData, getKeepGoing, learn, setData, setKeepGoing, stop

Methods inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

getMaxIterations, isResultValid, setMaxIterations

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListeners

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

learn

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm

getMaxIterations, setMaxIterations

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm

addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm

isResultValid

Constructor Detail

CategoryBalancedIVotingLearner

public CategoryBalancedIVotingLearner()

Creates a new CategoryBalancedIVotingLearner.

CategoryBalancedIVotingLearner

public CategoryBalancedIVotingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner,
                                      int maxIterations,
                                      double percentToSample,
                                      Random random)

Creates a new CategoryBalancedIVotingLearner.

Parameters:

learner - The learner to use to create the categorizer on each iteration.

maxIterations - The maximum number of iterations to run for, which is also the number of learners to create.

percentToSample - The percentage of the total size of the data to sample on each iteration. Must be positive.

random - The random number generator to use.

CategoryBalancedIVotingLearner

public CategoryBalancedIVotingLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner,
                                      int maxIterations,
                                      double percentToSample,
                                      double proportionIncorrectInSample,
                                      boolean voteOutOfBagOnly,
                                      Factory<? extends DataDistribution<CategoryType>> counterFactory,
                                      Random random)

Creates a new CategoryBalancedIVotingLearner.

Parameters:

learner - The learner to use to create the categorizer on each iteration.

maxIterations - The maximum number of iterations to run for, which is also the number of learners to create.

percentToSample - The percentage of the total size of the data to sample on each iteration. Must be positive.

proportionIncorrectInSample - The percentage of incorrect examples to put in each sample. Must be between 0.0 and 1.0 (inclusive).

voteOutOfBagOnly - Controls whether or not in-bag or out-of-bag votes are used to determine accuracy.

counterFactory - The factory for counting votes.

random - The random number generator to use.

Method Detail

createBag

protected void createBag(ArrayList<Integer> correctIndices,
                         ArrayList<Integer> incorrectIndices)

Description copied from class: IVotingCategorizerLearner

Create the next sample (bag) of examples to learn the next ensemble member from.

Overrides:

createBag in class IVotingCategorizerLearner<InputType,CategoryType>

Parameters:

correctIndices - The list of indices the ensemble is currently getting correct.

incorrectIndices - The list of indices the ensemble is currently getting incorrect.