Uses of Class
org.tribuo.sequence.SequenceDataset

Packages that use SequenceDataset

Package	Description
org.tribuo.classification.sequence.viterbi	Provides an implementation of Viterbi for generating structured outputs, which can sit on top of any Label based classification model.
org.tribuo.classification.sgd.crf	Provides an implementation of a linear chain CRF trained using Stochastic Gradient Descent.
org.tribuo.interop.tensorflow.sequence	Provides an interface for working with TensorFlow sequence models, using Tribuo's SequenceModel abstraction.
org.tribuo.provenance	Provides Tribuo specific infrastructure for the Provenance system which tracks models and datasets.
org.tribuo.sequence	Provides core classes for working with sequences of Examples.

Uses of SequenceDataset in org.tribuo.classification.sequence.viterbi

Methods in org.tribuo.classification.sequence.viterbi with parameters of type SequenceDataset

Modifier and Type	Method and Description
List<List<Prediction<Label>>>	ViterbiModel.predict(SequenceDataset<Label> examples)
SequenceModel<Label>	ViterbiTrainer.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.

Uses of SequenceDataset in org.tribuo.classification.sgd.crf

Methods in org.tribuo.classification.sgd.crf with parameters of type SequenceDataset

Modifier and Type	Method and Description
CRFModel	CRFTrainer.train(SequenceDataset<Label> sequenceExamples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of SequenceDataset in org.tribuo.interop.tensorflow.sequence

Methods in org.tribuo.interop.tensorflow.sequence with parameters of type SequenceDataset

Modifier and Type	Method and Description
protected void	TensorFlowSequenceTrainer.preTrainingHook(org.tensorflow.Session session, SequenceDataset<T> examples) A hook for modifying the session state before training starts.
SequenceModel<T>	TensorFlowSequenceTrainer.train(SequenceDataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of SequenceDataset in org.tribuo.provenance

Constructors in org.tribuo.provenance with parameters of type SequenceDataset

Constructor and Description
DatasetProvenance(DataProvenance sourceProvenance, com.oracle.labs.mlrg.olcut.provenance.ListProvenance<com.oracle.labs.mlrg.olcut.provenance.ObjectProvenance> transformationProvenance, SequenceDataset<T> dataset)

Uses of SequenceDataset in org.tribuo.sequence

Subclasses of SequenceDataset in org.tribuo.sequence

Modifier and Type	Class and Description
class	ImmutableSequenceDataset<T extends Output<T>> This is a SequenceDataset which has an ImmutableFeatureMap to store the feature information.
class	MinimumCardinalitySequenceDataset<T extends Output<T>> This class creates a pruned dataset in which low frequency features that occur less than the provided minimum cardinality have been removed.
class	MutableSequenceDataset<T extends Output<T>> A MutableSequenceDataset is a SequenceDataset with a MutableFeatureMap which grows over time.

Methods in org.tribuo.sequence with parameters of type SequenceDataset

Modifier and Type	Method and Description
static <T extends Output<T>> ImmutableSequenceDataset<T>	ImmutableSequenceDataset.copyDataset(SequenceDataset<T> dataset) Creates an immutable deep copy of the supplied dataset.
static <T extends Output<T>> ImmutableSequenceDataset<T>	ImmutableSequenceDataset.copyDataset(SequenceDataset<T> dataset, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo) Creates an immutable deep copy of the supplied dataset, using a different feature and output map.
static <T extends Output<T>> ImmutableSequenceDataset<T>	ImmutableSequenceDataset.copyDataset(SequenceDataset<T> dataset, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, Merger merger) Creates an immutable deep copy of the supplied dataset.
E	SequenceEvaluator.evaluate(SequenceModel<T> model, SequenceDataset<T> dataset) Evaluates the dataset using the supplied model, returning an immutable evaluation.
E	AbstractSequenceEvaluator.evaluate(SequenceModel<T> model, SequenceDataset<T> dataset) Produces an evaluation for the supplied model and dataset, by calling SequenceModel.predict(org.tribuo.sequence.SequenceExample<T>) to create the predictions, then aggregating the appropriate statistics.
List<List<Prediction<T>>>	SequenceModel.predict(SequenceDataset<T> examples) Uses the model to predict the labels for multiple examples contained in a data set.
default SequenceModel<T>	SequenceTrainer.train(SequenceDataset<T> examples) Trains a sequence prediction model using the examples in the given data set.
SequenceModel<T>	SequenceTrainer.train(SequenceDataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a sequence prediction model using the examples in the given data set.
IndependentSequenceModel<T>	IndependentSequenceTrainer.train(SequenceDataset<T> sequenceExamples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
SequenceModel<T>	HashingSequenceTrainer.train(SequenceDataset<T> sequenceExamples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance) This clones the SequenceDataset, hashes each of the examples and rewrites their feature ids before passing it to the inner trainer.

Constructors in org.tribuo.sequence with parameters of type SequenceDataset

Constructor and Description
MinimumCardinalitySequenceDataset(SequenceDataset<T> sequenceDataset, int minCardinality)