Class ViterbiTrainer

java.lang.Object

org.tribuo.classification.sequence.viterbi.ViterbiTrainer

All Implemented Interfaces:

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

public final class ViterbiTrainer
extends Object
implements SequenceTrainer<Label>

Builds a Viterbi model using the supplied Trainer. Has a parameter to control the label features which are added to the features supplied by the data.

Constructor Summary

Constructors

Constructor	Description
ViterbiTrainer(Trainer<Label> trainer, LabelFeatureExtractor labelFeatureExtractor, int stackSize, ViterbiModel.ScoreAggregation scoreAggregation)
ViterbiTrainer(Trainer<Label> trainer, LabelFeatureExtractor labelFeatureExtractor, ViterbiModel.ScoreAggregation scoreAggregation)

Method Summary

Modifier and Type	Method	Description
int	getInvocationCount()	Returns the number of times the train method has been invoked.
TrainerProvenance	getProvenance()
String	toString()
SequenceModel<Label>	train(SequenceDataset<Label> dataset, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)	The viterbi train method is unique because it delegates to a regular Model train method, but before it does, it adds features derived from preceding labels.

Methods inherited from class java.lang.Object

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

Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Configurable

postConfig

Methods inherited from interface org.tribuo.sequence.SequenceTrainer

train

Constructor Details

ViterbiTrainer

public ViterbiTrainer(Trainer<Label> trainer,
 LabelFeatureExtractor labelFeatureExtractor,
 ViterbiModel.ScoreAggregation scoreAggregation)

ViterbiTrainer

public ViterbiTrainer(Trainer<Label> trainer,
 LabelFeatureExtractor labelFeatureExtractor,
 int stackSize,
 ViterbiModel.ScoreAggregation scoreAggregation)

Method Details

train

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

The viterbi train method is unique because it delegates to a regular Model train method, but before it does, it adds features derived from preceding labels. The pipeline upstream of this call should not care that these features are being added - that is, we would not want to make the upstream logic worry about what kind of trainer will be used and have conditional logic that says to add special label-derived features if using the ViterbiTrainer. So, these one-of-a-kind unique-in-the-world label-derived features are generated here and added to the sequence examples of the passed in dataset. If you pass in a MutableSequenceDataset, then please be aware that your dataset will be modified after calling this method and therefore subsequent calls to other SequenceModel.train methods with your dataset should be avoided. If you pass in an ImmutableSequenceDataset, then please be aware that your entire dataset is going to be copied as a MutableSequenceDataset - so there is a memory penalty.

Specified by:

train in interface SequenceTrainer<Label>

Parameters:

dataset - The input dataset.

runProvenance - Any additional information to record in the provenance.

Returns:

A SequenceModel using Viterbi wrapped around an inner Model.

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>