org.tribuo.classification.sgd.crf

Class CRFTrainer

java.lang.Object

org.tribuo.classification.sgd.crf.CRFTrainer

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, SequenceTrainer<Label>, WeightedExamples

public class CRFTrainer
extends Object
implements SequenceTrainer<Label>, WeightedExamples

A trainer for CRFs using SGD. Modelled after FACTORIE's trainer for CRFs.

See:

 Lafferty J, McCallum A, Pereira FC.
 "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data"
 Proceedings of the 18th International Conference on Machine Learning 2001 (ICML 2001).
 

Constructor Summary

Constructors

Constructor and Description
CRFTrainer(StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, int minibatchSize, long seed) Creates a CRFTrainer which uses SGD to learn the parameters.
CRFTrainer(StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, long seed) Sets the minibatch size to 1.
CRFTrainer(StochasticGradientOptimiser optimiser, int epochs, long seed) Sets the minibatch size to 1 and the logging interval to 100.

Method Summary

Modifier and Type	Method and Description
int	getInvocationCount() Returns the number of times the train method has been invoked.
TrainerProvenance	getProvenance()
void	postConfig()
void	setShuffle(boolean shuffle) Turn on or off shuffling of examples.
String	toString()
CRFModel	train(SequenceDataset<Label> sequenceExamples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a sequence prediction model using the examples in the given data set.

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.tribuo.sequence.SequenceTrainer

train

Constructor Detail

CRFTrainer

public CRFTrainer(StochasticGradientOptimiser optimiser,
                  int epochs,
                  int loggingInterval,
                  int minibatchSize,
                  long seed)

Creates a CRFTrainer which uses SGD to learn the parameters.

Parameters:

optimiser - The gradient optimiser to use.

epochs - The number of SGD epochs (complete passes through the training data).

loggingInterval - Log the loss after this many iterations. If -1 don't log anything.

minibatchSize - The size of the minibatches used to aggregate gradients.

seed - A seed for the random number generator, used to shuffle the examples before each epoch.

CRFTrainer

public CRFTrainer(StochasticGradientOptimiser optimiser,
                  int epochs,
                  int loggingInterval,
                  long seed)

Sets the minibatch size to 1.

Parameters:

optimiser - The gradient optimiser to use.

epochs - The number of SGD epochs (complete passes through the training data).

loggingInterval - Log the loss after this many iterations. If -1 don't log anything.

seed - A seed for the random number generator, used to shuffle the examples before each epoch.

CRFTrainer

public CRFTrainer(StochasticGradientOptimiser optimiser,
                  int epochs,
                  long seed)

Sets the minibatch size to 1 and the logging interval to 100.

Parameters:

optimiser - The gradient optimiser to use.

epochs - The number of SGD epochs (complete passes through the training data).

seed - A seed for the random number generator, used to shuffle the examples before each epoch.

Method Detail

postConfig

public void postConfig()

Specified by:

postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable

setShuffle

public void setShuffle(boolean shuffle)

Turn on or off shuffling of examples.

This isn't exposed in the constructor as it defaults to on. This method should be used for debugging.

Parameters:

shuffle - If true shuffle the examples, if false leave them in their current order.

train

public CRFModel train(SequenceDataset<Label> sequenceExamples,
                      Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Description copied from interface: SequenceTrainer

Trains a sequence prediction model using the examples in the given data set.

Specified by:

train in interface SequenceTrainer<Label>

Parameters:

sequenceExamples - the data set containing the examples.

runProvenance - Training run specific provenance (e.g., fold number).

Returns:

a predictive model that can be used to generate predictions for new examples.

getInvocationCount

public int getInvocationCount()

Description copied from interface: SequenceTrainer

Returns the number of times the train method has been invoked.

Specified by:

getInvocationCount in interface SequenceTrainer<Label>

Returns:

The number of times train has been invoked.

toString

public String toString()

Overrides:

toString in class Object

getProvenance

public TrainerProvenance getProvenance()

Specified by:

getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>