cc.mallet.fst
Class CRFTrainerByLabelLikelihood

java.lang.Object
  cc.mallet.fst.TransducerTrainer
      cc.mallet.fst.CRFTrainerByLabelLikelihood

All Implemented Interfaces:

TransducerTrainer.ByOptimization

Direct Known Subclasses:

CRFTrainerByL1LabelLikelihood

public class CRFTrainerByLabelLikelihood
extends TransducerTrainer
implements TransducerTrainer.ByOptimization

Unlike ClassifierTrainer, TransducerTrainer is not "stateless" between calls to train. A TransducerTrainer is constructed paired with a specific Transducer, and can only train that Transducer. CRF stores and has methods for FeatureSelection and weight freezing. CRFTrainer stores and has methods for determining the contents/dimensions/sparsity/FeatureInduction of the CRF's weights as determined by training data.

Note: In the future this class may go away in favor of some default version of CRFTrainerByValueGradients.

Nested Class Summary

Nested classes/interfaces inherited from class cc.mallet.fst.TransducerTrainer

TransducerTrainer.ByIncrements, TransducerTrainer.ByInstanceIncrements, TransducerTrainer.ByOptimization

Field Summary

boolean

printGradient

Constructor Summary

CRFTrainerByLabelLikelihood(CRF crf)

Method Summary

CRF	getCRF()
double	getGaussianPriorVariance()
int	getIteration()
CRFOptimizableByLabelLikelihood	getOptimizableCRF(InstanceList trainingSet)
Optimizer	getOptimizer()
Optimizer	getOptimizer(InstanceList trainingSet)
Transducer	getTransducer()
double	getUseHyperbolicPriorSharpness()
double	getUseHyperbolicPriorSlope()
boolean	getUseSparseWeights()
boolean	isConverged()
boolean	isFinishedTraining()
void	setAddNoFactors(boolean flag) Use this method to specify whether or not factors are added to the CRF by this trainer.
void	setGaussianPriorVariance(double p)
void	setHyperbolicPriorSharpness(double p)
void	setHyperbolicPriorSlope(double p)
void	setUseHyperbolicPrior(boolean f)
void	setUseSomeUnsupportedTrick(boolean b) Sets whether to use the 'some unsupported trick.' This trick is, if training a CRF where some training has been done and sparse weights are used, to add a few weights for feaures that do not occur in the tainig data.
void	setUseSparseWeights(boolean b)
boolean	train(InstanceList trainingSet, int numIterations) Train the transducer associated with this TransducerTrainer.
boolean	train(InstanceList training, int numIterationsPerProportion, double[] trainingProportions) Train a CRF on various-sized subsets of the data.
boolean	trainIncremental(InstanceList training)
boolean	trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions)
boolean	trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, java.lang.String gainName) Train a CRF using feature induction to generate conjunctions of features.

Methods inherited from class cc.mallet.fst.TransducerTrainer

addEvaluator, addEvaluators, removeEvaluator, runEvaluators, train

Methods inherited from class java.lang.Object

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

Field Detail

printGradient

public boolean printGradient

Constructor Detail

CRFTrainerByLabelLikelihood

public CRFTrainerByLabelLikelihood(CRF crf)

Method Detail

getTransducer

public Transducer getTransducer()

Specified by:

getTransducer in class TransducerTrainer

getCRF

public CRF getCRF()

getOptimizer

public Optimizer getOptimizer()

Specified by:

getOptimizer in interface TransducerTrainer.ByOptimization

isConverged

public boolean isConverged()

isFinishedTraining

public boolean isFinishedTraining()

Specified by:

isFinishedTraining in class TransducerTrainer

getIteration

public int getIteration()

Specified by:

getIteration in class TransducerTrainer

setAddNoFactors

public void setAddNoFactors(boolean flag)

Use this method to specify whether or not factors are added to the CRF by this trainer. If you have already setup the factors in your CRF, you may not want the trainer to add additional factors.

Parameters:

flag - If true, this trainer adds no factors to the CRF.

getOptimizableCRF

public CRFOptimizableByLabelLikelihood getOptimizableCRF(InstanceList trainingSet)

getOptimizer

public Optimizer getOptimizer(InstanceList trainingSet)

trainIncremental

public boolean trainIncremental(InstanceList training)

train

public boolean train(InstanceList trainingSet,
                     int numIterations)

Description copied from class: TransducerTrainer

Train the transducer associated with this TransducerTrainer. You should be able to call this method with different trainingSet objects. Whether this causes the TransducerTrainer to combine both trainingSets or to view the second as a new alternative is at the discretion of the particular TransducerTrainer subclass involved.

Specified by:

train in class TransducerTrainer

train

public boolean train(InstanceList training,
                     int numIterationsPerProportion,
                     double[] trainingProportions)

Train a CRF on various-sized subsets of the data. This method is typically used to accelerate training by quickly getting to reasonable parameters on only a subset of the parameters first, then on progressively more data.

Parameters:

training - The training Instances.

numIterationsPerProportion - Maximum number of Maximizer iterations per training proportion.

trainingProportions - If non-null, train on increasingly larger portions of the data, e.g. new double[] {0.2, 0.5, 1.0}. This can sometimes speedup convergence. Be sure to end in 1.0 if you want to train on all the data in the end.

Returns:

True if training has converged.

trainWithFeatureInduction

public boolean trainWithFeatureInduction(InstanceList trainingData,
                                         InstanceList validationData,
                                         InstanceList testingData,
                                         TransducerEvaluator eval,
                                         int numIterations,
                                         int numIterationsBetweenFeatureInductions,
                                         int numFeatureInductions,
                                         int numFeaturesPerFeatureInduction,
                                         double trueLabelProbThreshold,
                                         boolean clusteredFeatureInduction,
                                         double[] trainingProportions)

trainWithFeatureInduction

public boolean trainWithFeatureInduction(InstanceList trainingData,
                                         InstanceList validationData,
                                         InstanceList testingData,
                                         TransducerEvaluator eval,
                                         int numIterations,
                                         int numIterationsBetweenFeatureInductions,
                                         int numFeatureInductions,
                                         int numFeaturesPerFeatureInduction,
                                         double trueLabelProbThreshold,
                                         boolean clusteredFeatureInduction,
                                         double[] trainingProportions,
                                         java.lang.String gainName)

Train a CRF using feature induction to generate conjunctions of features. Feature induction is run periodically during training. The features are added to improve performance on the mislabeled instances, with the specific scoring criterion given by the FeatureInducer specified by gainName

Parameters:

training - The training Instances.

validation - The validation Instances.

testing - The testing instances.

eval - For evaluation during training.

numIterations - Maximum number of Maximizer iterations.

numIterationsBetweenFeatureInductions - Number of maximizer iterations between each call to the Feature Inducer.

numFeatureInductions - Maximum number of rounds of feature induction.

numFeaturesPerFeatureInduction - Maximum number of features to induce at each round of induction.

trueLabelProbThreshold - If the model's probability of the true Label of an Instance is less than this value, it is added as an error instance to the FeatureInducer.

clusteredFeatureInduction - If true, a separate FeatureInducer is constructed for each label pair. This can avoid inducing a disproportionate number of features for a single label.

trainingProportions - If non-null, train on increasingly larger portions of the data (e.g. [0.2, 0.5, 1.0]. This can sometimes speedup convergence.

gainName - The type of FeatureInducer to use. One of "exp", "grad", or "info" for ExpGain, GradientGain, or InfoGain.

Returns:

True if training has converged.

setUseHyperbolicPrior

public void setUseHyperbolicPrior(boolean f)

setHyperbolicPriorSlope

public void setHyperbolicPriorSlope(double p)

setHyperbolicPriorSharpness

public void setHyperbolicPriorSharpness(double p)

getUseHyperbolicPriorSlope

public double getUseHyperbolicPriorSlope()

getUseHyperbolicPriorSharpness

public double getUseHyperbolicPriorSharpness()

setGaussianPriorVariance

public void setGaussianPriorVariance(double p)

getGaussianPriorVariance

public double getGaussianPriorVariance()

setUseSparseWeights

public void setUseSparseWeights(boolean b)

getUseSparseWeights

public boolean getUseSparseWeights()

setUseSomeUnsupportedTrick

public void setUseSomeUnsupportedTrick(boolean b)

Sets whether to use the 'some unsupported trick.' This trick is, if training a CRF where some training has been done and sparse weights are used, to add a few weights for feaures that do not occur in the tainig data.

This generally leads to better accuracy at only a small memory cost.

Parameters:

b - Whether to use the trick