Uses of Class
org.tribuo.Dataset

Packages that use Dataset

Package	Description
org.tribuo	Provides the core interfaces and classes for using Tribuo.
org.tribuo.dataset	Provides utility datasets which subsample or otherwise transform the wrapped dataset.
org.tribuo.datasource	Simple data sources for ingesting or aggregating data.
org.tribuo.ensemble	Provides an interface for model prediction combinations, two base classes for ensemble models, a base class for ensemble excuses, and a Bagging implementation.
org.tribuo.evaluation	Evaluation base classes, along with code for train/test splits and cross validation.
org.tribuo.evaluation.metrics	This package contains the infrastructure classes for building evaluation metrics.
org.tribuo.hash	Provides the base interface and implementations of the Model hashing which obscures the feature names stored in a model.
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.
org.tribuo.transform	Provides infrastructure for applying transformations to a Dataset.

Uses of Dataset in org.tribuo

Subclasses of Dataset in org.tribuo

Modifier and Type	Class	Description
class	ImmutableDataset<T extends Output<T>>	This is a Dataset which has an ImmutableFeatureMap to store the feature information.
class	MutableDataset<T extends Output<T>>	A MutableDataset is a Dataset with a MutableFeatureMap which grows over time.

Methods in org.tribuo that return Dataset

Modifier and Type	Method	Description
static <T extends Output<T>> Dataset<T>	Dataset.castDataset(Dataset<?> inputDataset, Class<T> outputType)	Casts the dataset to the specified output type, assuming it is valid.
static Dataset<?>	Dataset.deserialize(org.tribuo.protos.core.DatasetProto datasetProto)	Deserializes a dataset proto into a dataset.
static Dataset<?>	Dataset.deserializeFromFile(Path path)	Reads an instance of DatasetProto from the supplied path and deserializes it.
static Dataset<?>	Dataset.deserializeFromStream(InputStream is)	Reads an instance of DatasetProto from the supplied input stream and deserializes it.

Methods in org.tribuo with parameters of type Dataset

Modifier and Type	Method	Description
static <T extends Output<T>> Dataset<T>	Dataset.castDataset(Dataset<?> inputDataset, Class<T> outputType)	Casts the dataset to the specified output type, assuming it is valid.
static <T extends Output<T>> ImmutableDataset<T>	ImmutableDataset.copyDataset(Dataset<T> dataset)	Creates an immutable deep copy of the supplied dataset.
static <T extends Output<T>> ImmutableDataset<T>	ImmutableDataset.copyDataset(Dataset<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>> ImmutableDataset<T>	ImmutableDataset.copyDataset(Dataset<T> dataset, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, Merger merger)	Creates an immutable deep copy of the supplied dataset.
static <T extends Output<T>> MutableDataset<T>	MutableDataset.createDeepCopy(Dataset<T> other)	Creates a deep copy of the supplied Dataset which is mutable.
static <T extends Output<T>> ImmutableDataset<T>	ImmutableDataset.hashFeatureMap(Dataset<T> dataset, Hasher hasher)	Creates an immutable shallow copy of the supplied dataset, using the hasher to generate a HashedFeatureMap which transparently maps from the feature name to the hashed variant.
U	IncrementalTrainer.incrementalTrain(Dataset<T> newData, U model)	Incrementally trains the supplied model with the new data.
List<Prediction<T>>	Model.predict(Dataset<T> examples)	Uses the model to predict the outputs for multiple examples contained in a data set.
SelectedFeatureSet	FeatureSelector.select(Dataset<T> dataset)	Selects features according to this selection algorithm from the specified dataset.
default SparseModel<T>	SparseTrainer.train(Dataset<T> examples)	Trains a sparse predictive model using the examples in the given data set.
SparseModel<T>	SparseTrainer.train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)	Trains a sparse predictive model using the examples in the given data set.
default SparseModel<T>	SparseTrainer.train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)	Trains a predictive model using the examples in the given data set.
default Model<T>	Trainer.train(Dataset<T> examples)	Trains a predictive model using the examples in the given data set.
Model<T>	Trainer.train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)	Trains a predictive model using the examples in the given data set.
default Model<T>	Trainer.train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)	Trains a predictive model using the examples in the given data set.

Uses of Dataset in org.tribuo.dataset

Subclasses of Dataset in org.tribuo.dataset

Modifier and Type	Class	Description
final class	DatasetView<T extends Output<T>>	DatasetView provides an immutable view on another Dataset that only exposes selected examples.
class	MinimumCardinalityDataset<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.
final class	SelectedFeatureDataset<T extends Output<T>>	This class creates a pruned dataset which only contains the selected features.

Methods in org.tribuo.dataset with parameters of type Dataset

Modifier and Type	Method	Description
static <T extends Output<T>> DatasetView<T>	DatasetView.createBootstrapView(Dataset<T> dataset, int size, long seed)	Generates a DatasetView bootstrapped from the supplied Dataset.
static <T extends Output<T>> DatasetView<T>	DatasetView.createBootstrapView(Dataset<T> dataset, int size, long seed, ImmutableFeatureMap featureIDs, ImmutableOutputInfo<T> outputIDs)	Generates a DatasetView bootstrapped from the supplied Dataset.
static <T extends Output<T>> DatasetView<T>	DatasetView.createView(Dataset<T> dataset, Predicate<Example<T>> predicate, String tag)	Creates a view from the supplied dataset, using the specified predicate to test if each example should be in this view.
static <T extends Output<T>> DatasetView<T>	DatasetView.createWeightedBootstrapView(Dataset<T> dataset, int size, long seed, float[] exampleWeights)	Generates a DatasetView bootstrapped from the supplied Dataset using the supplied example weights.
static <T extends Output<T>> DatasetView<T>	DatasetView.createWeightedBootstrapView(Dataset<T> dataset, int size, long seed, float[] exampleWeights, ImmutableFeatureMap featureIDs, ImmutableOutputInfo<T> outputIDs)	Generates a DatasetView bootstrapped from the supplied Dataset using the supplied example weights.

Constructors in org.tribuo.dataset with parameters of type Dataset

Modifier	Constructor	Description
	DatasetView(Dataset<T> dataset, int[] exampleIndices, String tag)	Creates a DatasetView which includes the supplied indices from the dataset.
	DatasetView(Dataset<T> dataset, int[] exampleIndices, ImmutableFeatureMap featureIDs, ImmutableOutputInfo<T> labelIDs, String tag)	Creates a DatasetView which includes the supplied indices from the dataset.
	MinimumCardinalityDataset(Dataset<T> dataset, int minCardinality)
	SelectedFeatureDataset(Dataset<T> dataset, SelectedFeatureSet featureSet)	Constructs a selected feature dataset using all the features in the supplied feature set.
	SelectedFeatureDataset(Dataset<T> dataset, SelectedFeatureSet featureSet, int k)	Constructs a selected feature dataset.

Uses of Dataset in org.tribuo.datasource

Methods in org.tribuo.datasource with parameters of type Dataset

Modifier and Type	Method	Description
static <T extends Output<T>> void	LibSVMDataSource.writeLibSVMFormat(Dataset<T> dataset, PrintStream out, boolean zeroIndexed, Function<T,Number> transformationFunc)	Writes out a dataset in LibSVM format.

Uses of Dataset in org.tribuo.ensemble

Methods in org.tribuo.ensemble with parameters of type Dataset

Modifier and Type	Method	Description
EnsembleModel<T>	BaggingTrainer.train(Dataset<T> examples)
EnsembleModel<T>	BaggingTrainer.train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
EnsembleModel<T>	BaggingTrainer.train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)
protected Model<T>	BaggingTrainer.trainSingleModel(Dataset<T> examples, ImmutableFeatureMap featureIDs, ImmutableOutputInfo<T> labelIDs, int randInt, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)	Trains a single model.

Uses of Dataset in org.tribuo.evaluation

Methods in org.tribuo.evaluation with parameters of type Dataset

Modifier and Type	Method	Description
static <T extends Output<T>, C extends MetricContext<T>> com.oracle.labs.mlrg.olcut.util.Pair<Integer,Double>	EvaluationAggregator.argmax(EvaluationMetric<T,C> metric, List<? extends Model<T>> models, Dataset<T> dataset)	Calculates the argmax of a metric across the supplied models (i.e., the index of the model which performed the best).
final E	AbstractEvaluator.evaluate(Model<T> model, Dataset<T> dataset)	Produces an evaluation for the supplied model and dataset, by calling Model.predict(org.tribuo.Example<T>) to create the predictions, then aggregating the appropriate statistics.
E	Evaluator.evaluate(Model<T> model, Dataset<T> dataset)	Evaluates the dataset using the supplied model, returning an immutable Evaluation of the appropriate type.
Iterator<KFoldSplitter.TrainTestFold<T>>	KFoldSplitter.split(Dataset<T> dataset, boolean shuffle)	Splits a dataset into k consecutive folds; for each fold, the remaining k-1 folds form the training set.
static <T extends Output<T>, C extends MetricContext<T>> DescriptiveStats	EvaluationAggregator.summarize(List<? extends EvaluationMetric<T,C>> metrics, Model<T> model, Dataset<T> dataset)	Summarize model performance on dataset across several metrics.
static <T extends Output<T>, R extends Evaluation<T>> Map<MetricID<T>, DescriptiveStats>	EvaluationAggregator.summarize(Evaluator<T,R> evaluator, List<? extends Model<T>> models, Dataset<T> dataset)	Summarize performance using the supplied evaluator across several models on one dataset.
static <T extends Output<T>, C extends MetricContext<T>> DescriptiveStats	EvaluationAggregator.summarize(EvaluationMetric<T,C> metric, List<? extends Model<T>> models, Dataset<T> dataset)	Summarize performance w.r.t.

Method parameters in org.tribuo.evaluation with type arguments of type Dataset

Modifier and Type	Method	Description
static <T extends Output<T>, C extends MetricContext<T>> com.oracle.labs.mlrg.olcut.util.Pair<Integer,Double>	EvaluationAggregator.argmax(EvaluationMetric<T,C> metric, Model<T> model, List<? extends Dataset<T>> datasets)	Calculates the argmax of a metric across the supplied datasets.
static <T extends Output<T>, R extends Evaluation<T>> Map<MetricID<T>, DescriptiveStats>	EvaluationAggregator.summarize(Evaluator<T,R> evaluator, Model<T> model, List<? extends Dataset<T>> datasets)	Summarize performance according to evaluator for a single model across several datasets.
static <T extends Output<T>, C extends MetricContext<T>> DescriptiveStats	EvaluationAggregator.summarize(EvaluationMetric<T,C> metric, Model<T> model, List<? extends Dataset<T>> datasets)	Summarize a model's performance w.r.t.

Constructors in org.tribuo.evaluation with parameters of type Dataset

Modifier	Constructor	Description
	CrossValidation(Trainer<T> trainer, Dataset<T> data, Evaluator<T,E> evaluator, int k)	Builds a k-fold cross-validation loop.
	CrossValidation(Trainer<T> trainer, Dataset<T> data, Evaluator<T,E> evaluator, int k, long seed)	Builds a k-fold cross-validation loop.

Uses of Dataset in org.tribuo.evaluation.metrics

Methods in org.tribuo.evaluation.metrics with parameters of type Dataset

Modifier and Type	Method	Description
default C	EvaluationMetric.createContext(Model<T> model, Dataset<T> dataset)	Creates the metric context used to compute this metric's value, generating Predictions for each Example in the supplied dataset.

Uses of Dataset in org.tribuo.hash

Methods in org.tribuo.hash with parameters of type Dataset

Modifier and Type	Method	Description
Model<T>	HashingTrainer.train(Dataset<T> dataset, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)	This clones the Dataset, hashes each of the examples and rewrites their feature ids before passing it to the inner trainer.
Model<T>	HashingTrainer.train(Dataset<T> dataset, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount)

Uses of Dataset in org.tribuo.provenance

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

Modifier	Constructor	Description
<T extends Output<T>>	DatasetProvenance(DataProvenance sourceProvenance, com.oracle.labs.mlrg.olcut.provenance.ListProvenance<com.oracle.labs.mlrg.olcut.provenance.ObjectProvenance> transformationProvenance, Dataset<T> dataset)	Creates a dataset provenance from the supplied dataset.

Uses of Dataset in org.tribuo.sequence

Methods in org.tribuo.sequence that return Dataset

Modifier and Type	Method	Description
Dataset<T>	SequenceDataset.getFlatDataset()	Returns a view on this SequenceDataset which aggregates all the examples and ignores the sequence structure.

Uses of Dataset in org.tribuo.transform

Methods in org.tribuo.transform with parameters of type Dataset

Modifier and Type	Method	Description
List<Prediction<T>>	TransformedModel.predict(Dataset<T> examples)
TransformedModel<T>	TransformTrainer.train(Dataset<T> examples)
TransformedModel<T>	TransformTrainer.train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)
TransformedModel<T>	TransformTrainer.train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount)
<T extends Output<T>> MutableDataset<T>	TransformerMap.transformDataset(Dataset<T> dataset)	Copies the supplied dataset and applies the transformers to each example in it.
<T extends Output<T>> MutableDataset<T>	TransformerMap.transformDataset(Dataset<T> dataset, boolean densify)	Copies the supplied dataset and applies the transformers to each example in it.