Uses of Class
org.tribuo.sequence.SequenceModel

Packages that use SequenceModel

Package	Description
org.tribuo.classification.evaluation	Evaluation classes for multi-class classification.
org.tribuo.classification.sequence	Provides infrastructure for SequenceModels which emit Labels at each step of the sequence.
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.evaluation.metrics	This package contains the infrastructure classes for building evaluation metrics.
org.tribuo.interop.tensorflow.sequence	Provides an interface for working with TensorFlow sequence models, using Tribuo's SequenceModel abstraction.
org.tribuo.sequence	Provides core classes for working with sequences of Examples.

Uses of SequenceModel in org.tribuo.classification.evaluation

Constructors in org.tribuo.classification.evaluation with parameters of type SequenceModel

Modifier	Constructor	Description
	Context(SequenceModel<Label> model, List<Prediction<Label>> predictions)	Constructs a context and compute the confusion matrix using the specified model and predictions.

Uses of SequenceModel in org.tribuo.classification.sequence

Subclasses of SequenceModel in org.tribuo.classification.sequence

Modifier and Type	Class	Description
class	ConfidencePredictingSequenceModel	A Sequence model which can provide confidence predictions for subsequence predictions.

Methods in org.tribuo.classification.sequence with parameters of type SequenceModel

Modifier and Type	Method	Description
protected LabelMetric.Context	LabelSequenceEvaluator.createContext(SequenceModel<Label> model, List<List<Prediction<Label>>> predictions)
protected Set<LabelMetric>	LabelSequenceEvaluator.createMetrics(SequenceModel<Label> model)

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

Subclasses of SequenceModel in org.tribuo.classification.sequence.viterbi

Modifier and Type	Class	Description
class	ViterbiModel	An implementation of a viterbi model.

Methods in org.tribuo.classification.sequence.viterbi that return SequenceModel

Modifier and Type	Method	Description
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 SequenceModel in org.tribuo.classification.sgd.crf

Subclasses of SequenceModel in org.tribuo.classification.sgd.crf

Modifier and Type	Class	Description
class	CRFModel	An inference time model for a linear chain CRF trained using SGD.

Uses of SequenceModel in org.tribuo.evaluation.metrics

Methods in org.tribuo.evaluation.metrics that return SequenceModel

Modifier and Type	Method	Description
SequenceModel<T>	MetricContext.getSequenceModel()	Gets the SequenceModel used by this context.

Constructors in org.tribuo.evaluation.metrics with parameters of type SequenceModel

Modifier	Constructor	Description
protected	MetricContext(SequenceModel<T> model, List<Prediction<T>> predictions)

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

Subclasses of SequenceModel in org.tribuo.interop.tensorflow.sequence

Modifier and Type	Class	Description
class	TensorFlowSequenceModel<T extends Output<T>>	A TensorFlow model which implements SequenceModel, suitable for use in sequential prediction tasks.

Methods in org.tribuo.interop.tensorflow.sequence that return SequenceModel

Modifier and Type	Method	Description
SequenceModel<T>	TensorFlowSequenceTrainer.train(SequenceDataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of SequenceModel in org.tribuo.sequence

Subclasses of SequenceModel in org.tribuo.sequence

Modifier and Type	Class	Description
class	IndependentSequenceModel<T extends Output<T>>	A SequenceModel which independently predicts each element of the sequence.

Methods in org.tribuo.sequence that return SequenceModel

Modifier and Type	Method	Description
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.
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.

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

Modifier and Type	Method	Description
protected abstract C	AbstractSequenceEvaluator.createContext(SequenceModel<T> model, List<List<Prediction<T>>> predictions)	Create the context needed for evaluation.
protected abstract Set<M>	AbstractSequenceEvaluator.createMetrics(SequenceModel<T> model)	Creates the appropriate set of metrics for this model, by querying for it's OutputInfo.
final E	AbstractSequenceEvaluator.evaluate(SequenceModel<T> model, List<List<Prediction<T>>> predictions, DataProvenance dataProvenance)	Produces an evaluation for the supplied model and predictions by aggregating the appropriate statistics.
final E	AbstractSequenceEvaluator.evaluate(SequenceModel<T> model, SequenceDataset<T> dataset)	Produces an evaluation for the supplied model and dataset, by calling predict(org.tribuo.sequence.SequenceExample<T>) to create the predictions, then aggregating the appropriate statistics.
final E	AbstractSequenceEvaluator.evaluate(SequenceModel<T> model, SequenceDataSource<T> datasource)	Produces an evaluation for the supplied model and datasource, by calling predict(org.tribuo.sequence.SequenceExample<T>) to create the predictions, then aggregating the appropriate statistics.
E	SequenceEvaluator.evaluate(SequenceModel<T> model, List<List<Prediction<T>>> predictions, DataProvenance dataProvenance)	Evaluates the supplied model and predictions by aggregating the appropriate statistics.
E	SequenceEvaluator.evaluate(SequenceModel<T> model, SequenceDataset<T> dataset)	Evaluates the dataset using the supplied model, returning an immutable evaluation.
E	SequenceEvaluator.evaluate(SequenceModel<T> model, SequenceDataSource<T> datasource)	Evaluates the datasource using the supplied model, returning an immutable evaluation.

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

Modifier	Constructor	Description
	ImmutableSequenceDataset(SequenceDataSource<T> dataSource, SequenceModel<T> model)	Creates a dataset from a data source, taking the output and feature domains from the supplied model.