| 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. |
| Constructor and Description |
|---|
Context(SequenceModel<Label> model,
List<Prediction<Label>> predictions) |
| Modifier and Type | Class and Description |
|---|---|
class |
ConfidencePredictingSequenceModel
A Sequence model which can provide confidence predictions for subsequence predictions.
|
| Modifier and Type | Method and Description |
|---|---|
protected LabelMetric.Context |
LabelSequenceEvaluator.createContext(SequenceModel<Label> model,
List<List<Prediction<Label>>> predictions) |
protected Set<LabelMetric> |
LabelSequenceEvaluator.createMetrics(SequenceModel<Label> model) |
| Modifier and Type | Class and Description |
|---|---|
class |
ViterbiModel
An implementation of a viterbi model.
|
| Modifier and Type | Method and 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. |
| Modifier and Type | Class and Description |
|---|---|
class |
CRFModel
An inference time model for a CRF trained using SGD.
|
| Modifier and Type | Method and Description |
|---|---|
SequenceModel<T> |
MetricContext.getSequenceModel()
Gets the SequenceModel used by this context.
|
| Constructor and Description |
|---|
MetricContext(SequenceModel<T> model,
List<Prediction<T>> predictions) |
| Modifier and Type | Class and Description |
|---|---|
class |
TensorflowSequenceModel<T extends Output<T>>
A Tensorflow model which implements SequenceModel, suitable for use in sequential prediction tasks.
|
| Modifier and Type | Method and Description |
|---|---|
SequenceModel<T> |
TensorflowSequenceTrainer.train(SequenceDataset<T> examples,
Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) |
| Modifier and Type | Method and Description |
|---|---|
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.
|
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. |
| Modifier and Type | Method and 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. |
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 |
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.
|
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
predict(org.tribuo.sequence.SequenceExample<T>)
to create the predictions, then aggregating the appropriate statistics. |
E |
SequenceEvaluator.evaluate(SequenceModel<T> model,
SequenceDataSource<T> datasource)
Evaluates the datasource using the supplied model, returning an immutable evaluation.
|
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. |
| Constructor and Description |
|---|
ImmutableSequenceDataset(SequenceDataSource<T> dataSource,
SequenceModel<T> model) |
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