Index
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form
A
- a - Variable in class org.tribuo.util.infotheory.impl.CachedTriple
-
The first element.
- ABSOLUTE - Enum constant in enum class org.tribuo.regression.sgd.fm.TrainTest.LossEnum
-
Uses
AbsoluteLoss
. - ABSOLUTE - Enum constant in enum class org.tribuo.regression.sgd.TrainTest.LossEnum
-
Uses
AbsoluteLoss
. - AbsoluteLoss - Class in org.tribuo.regression.sgd.objectives
-
Absolute loss (i.e., l1).
- AbsoluteLoss() - Constructor for class org.tribuo.regression.sgd.objectives.AbsoluteLoss
-
Constructs an absolute loss.
- AbstractCARTTrainer<T extends Output<T>> - Class in org.tribuo.common.tree
-
Base class for
Trainer
's that use an approximation of the CART algorithm to build a decision tree. - AbstractCARTTrainer(int, float, float, float, boolean, long) - Constructor for class org.tribuo.common.tree.AbstractCARTTrainer
-
After calls to this superconstructor subclasses must call postConfig().
- AbstractCARTTrainer.AbstractCARTTrainerProvenance - Class in org.tribuo.common.tree
-
Deprecated.
- AbstractCARTTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.common.tree.AbstractCARTTrainer.AbstractCARTTrainerProvenance
-
Deprecated.Deserialization constructor.
- AbstractCARTTrainerProvenance(AbstractCARTTrainer<T>) - Constructor for class org.tribuo.common.tree.AbstractCARTTrainer.AbstractCARTTrainerProvenance
-
Deprecated.Constructs a provenance for the host AbstractCARTTrainer.
- AbstractEvaluator<T extends Output<T>,
C extends MetricContext<T>, E extends Evaluation<T>, M extends EvaluationMetric<T, C>> - Class in org.tribuo.evaluation -
Base class for evaluators.
- AbstractEvaluator() - Constructor for class org.tribuo.evaluation.AbstractEvaluator
- AbstractFMModel<T extends Output<T>> - Class in org.tribuo.common.sgd
-
A quadratic factorization machine model trained using SGD.
- AbstractFMModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, FMParameters, boolean) - Constructor for class org.tribuo.common.sgd.AbstractFMModel
-
Constructs a factorization machine model trained via SGD.
- AbstractFMTrainer<T extends Output<T>,
U, V extends AbstractFMModel<T>> - Class in org.tribuo.common.sgd -
A trainer for a quadratic factorization machine model which uses SGD.
- AbstractFMTrainer() - Constructor for class org.tribuo.common.sgd.AbstractFMTrainer
-
For olcut.
- AbstractFMTrainer(StochasticGradientOptimiser, int, int, int, long, int, double) - Constructor for class org.tribuo.common.sgd.AbstractFMTrainer
-
Constructs an SGD trainer for a factorization machine.
- AbstractLinearSGDModel<T extends Output<T>> - Class in org.tribuo.common.sgd
-
A linear model trained using SGD.
- AbstractLinearSGDModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, LinearParameters, boolean) - Constructor for class org.tribuo.common.sgd.AbstractLinearSGDModel
-
Constructs a linear model trained via SGD.
- AbstractLinearSGDTrainer<T extends Output<T>,
U, V extends AbstractLinearSGDModel<T>> - Class in org.tribuo.common.sgd -
A trainer for a linear model which uses SGD.
- AbstractLinearSGDTrainer() - Constructor for class org.tribuo.common.sgd.AbstractLinearSGDTrainer
-
For olcut.
- AbstractLinearSGDTrainer(StochasticGradientOptimiser, int, int, int, long) - Constructor for class org.tribuo.common.sgd.AbstractLinearSGDTrainer
-
Constructs an SGD trainer for a linear model.
- AbstractSequenceEvaluator<T extends Output<T>,
C extends MetricContext<T>, E extends SequenceEvaluation<T>, M extends EvaluationMetric<T, C>> - Class in org.tribuo.sequence -
Base class for sequence evaluators.
- AbstractSequenceEvaluator() - Constructor for class org.tribuo.sequence.AbstractSequenceEvaluator
- AbstractSGDModel<T extends Output<T>> - Class in org.tribuo.common.sgd
-
A model trained using SGD.
- AbstractSGDModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, FeedForwardParameters, boolean, boolean) - Constructor for class org.tribuo.common.sgd.AbstractSGDModel
-
Constructs a linear model trained via SGD.
- AbstractSGDModel.PredAndActive - Class in org.tribuo.common.sgd
-
A nominal tuple used to capture the prediction and the number of active features used by the model.
- AbstractSGDTrainer<T extends Output<T>,
U, V extends Model<T>, X extends FeedForwardParameters> - Class in org.tribuo.common.sgd -
A trainer for a model which uses SGD.
- AbstractSGDTrainer(boolean) - Constructor for class org.tribuo.common.sgd.AbstractSGDTrainer
-
Base constructor called by subclass no-args constructors used by OLCUT.
- AbstractSGDTrainer(StochasticGradientOptimiser, int, int, int, long, boolean) - Constructor for class org.tribuo.common.sgd.AbstractSGDTrainer
-
Constructs an SGD trainer.
- AbstractTrainingNode<T extends Output<T>> - Class in org.tribuo.common.tree
-
Base class for decision tree nodes used at training time.
- AbstractTrainingNode(int, int, AbstractTrainingNode.LeafDeterminer) - Constructor for class org.tribuo.common.tree.AbstractTrainingNode
-
Builds an abstract training node.
- AbstractTrainingNode.LeafDeterminer - Class in org.tribuo.common.tree
-
Contains parameters needed to determine whether a node is a leaf.
- accuracy() - Method in interface org.tribuo.classification.evaluation.LabelEvaluation
-
The overall accuracy of the evaluation.
- accuracy() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
-
The accuracy.
- accuracy(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation
-
The per label accuracy of the evaluation.
- accuracy(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
-
Gets the accuracy for this label.
- accuracy(EvaluationMetric.Average, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
-
Calculates the accuracy using the specified average type and confusion matrix.
- accuracy(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
-
Calculates the accuracy given this confusion matrix.
- accuracy(T, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
-
Calculates a per label accuracy given this confusion matrix.
- ACCURACY - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics
-
The accuracy.
- ADABOOST - Enum constant in enum class org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType
-
Creates an
AdaBoostTrainer
. - AdaBoostTrainer - Class in org.tribuo.classification.ensemble
-
Implements Adaboost.SAMME one of the more popular algorithms for multiclass boosting.
- AdaBoostTrainer(Trainer<Label>, int) - Constructor for class org.tribuo.classification.ensemble.AdaBoostTrainer
-
Constructs an adaboost trainer using the supplied weak learner trainer and the specified number of boosting rounds.
- AdaBoostTrainer(Trainer<Label>, int, long) - Constructor for class org.tribuo.classification.ensemble.AdaBoostTrainer
-
Constructs an adaboost trainer using the supplied weak learner trainer, the specified number of boosting rounds and the supplied seed.
- AdaDelta - Class in org.tribuo.math.optimisers
-
An implementation of the AdaDelta gradient optimiser.
- AdaDelta() - Constructor for class org.tribuo.math.optimisers.AdaDelta
-
Sets rho to 0.95 and epsilon to 1e-6.
- AdaDelta(double) - Constructor for class org.tribuo.math.optimisers.AdaDelta
-
Keeps rho at 0.95, passes through epsilon.
- AdaDelta(double, double) - Constructor for class org.tribuo.math.optimisers.AdaDelta
-
It's recommended to keep rho at 0.95.
- ADADELTA - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser
-
The AdaDelta optimiser.
- ADADELTA - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
-
The AdaDelta optimiser.
- AdaGrad - Class in org.tribuo.math.optimisers
-
An implementation of the AdaGrad gradient optimiser.
- AdaGrad(double) - Constructor for class org.tribuo.math.optimisers.AdaGrad
-
Creates an AdaGrad optimiser using the specified initial learning rate.
- AdaGrad(double, double) - Constructor for class org.tribuo.math.optimisers.AdaGrad
-
Creates an AdaGrad optimiser using the specified learning rate and epsilon.
- AdaGrad(double, double, double) - Constructor for class org.tribuo.math.optimisers.AdaGrad
-
Creates an AdaGrad optimiser using the specified learning rate, epsilon and initial accumulator value.
- ADAGRAD - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser
-
The AdaGrad optimiser.
- ADAGRAD - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
-
The AdaGrad optimiser.
- ADAGRADDA - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser
-
The AdaGrad Dual Averaging optimiser.
- AdaGradRDA - Class in org.tribuo.math.optimisers
-
An implementation of the AdaGrad gradient optimiser with regularized dual averaging.
- AdaGradRDA(double, double) - Constructor for class org.tribuo.math.optimisers.AdaGradRDA
-
Creates an AdaGradRDA optimiser with the specified parameter values.
- AdaGradRDA(double, double, double, double, int) - Constructor for class org.tribuo.math.optimisers.AdaGradRDA
-
Creates an AdaGradRDA optimiser with the specified parameter values.
- ADAGRADRDA - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
-
The AdaGrad Regularised Dual Averaging optimiser.
- Adam - Class in org.tribuo.math.optimisers
-
An implementation of the Adam gradient optimiser.
- Adam() - Constructor for class org.tribuo.math.optimisers.Adam
-
Sets initialLearningRate to 0.001, betaOne to 0.9, betaTwo to 0.999, epsilon to 1e-6.
- Adam(double, double) - Constructor for class org.tribuo.math.optimisers.Adam
-
Sets betaOne to 0.9 and betaTwo to 0.999
- Adam(double, double, double, double) - Constructor for class org.tribuo.math.optimisers.Adam
-
It's highly recommended not to modify these parameters, use one of the other constructors.
- ADAM - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser
-
The Adam optimiser.
- ADAM - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
-
The Adam optimiser.
- ADAMAX - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser
-
The Adamax optimiser.
- add - Enum constant in enum class org.tribuo.transform.transformations.SimpleTransform.Operation
-
Adds the specified constant.
- add() - Static method in interface org.tribuo.util.Merger
-
A merger which adds the elements.
- add(double) - Static method in class org.tribuo.transform.transformations.SimpleTransform
-
Generate a SimpleTransform that adds the operand to each value.
- add(int) - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
-
Adds an index where the feature value occurs.
- add(int, double) - Method in class org.tribuo.math.la.DenseVector
- add(int, double) - Method in interface org.tribuo.math.la.SGDVector
-
Adds
value
to the element atindex
. - add(int, double) - Method in class org.tribuo.math.la.SparseVector
- add(int, int, double) - Method in class org.tribuo.math.la.DenseMatrix
- add(int, int, double) - Method in class org.tribuo.math.la.DenseSparseMatrix
- add(int, int, double) - Method in interface org.tribuo.math.la.Matrix
-
Adds the argument value to the value at the supplied index.
- add(int, Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList
-
Unsupported.
- add(String) - Method in class org.tribuo.impl.BinaryFeaturesExample
-
Adds a single feature with a value of 1.
- add(String, double) - Method in class org.tribuo.impl.ArrayExample
-
Adds a single feature.
- add(String, double) - Method in class org.tribuo.MutableFeatureMap
-
Adds an occurrence of a feature with a given name.
- add(Example<T>) - Method in class org.tribuo.ImmutableDataset
-
Adds an
Example
to the dataset, which will remove features with unknown names. - add(Example<T>) - Method in class org.tribuo.MutableDataset
-
Adds an example to the dataset, which observes the output and each feature value.
- add(Example<T>, Merger) - Method in class org.tribuo.ImmutableDataset
-
Adds a
Example
to the dataset, which will insert feature ids, remove unknown features and sort the examples by the feature ids (merging duplicate ids). - add(Feature) - Method in class org.tribuo.Example
-
Adds a feature.
- add(Feature) - Method in class org.tribuo.impl.ArrayExample
- add(Feature) - Method in class org.tribuo.impl.BinaryFeaturesExample
-
Adds a feature to this example.
- add(Feature) - Method in class org.tribuo.impl.IndexedArrayExample
- add(Feature) - Method in class org.tribuo.impl.ListExample
- add(SGDVector) - Method in class org.tribuo.math.la.DenseVector
-
Adds
other
to this vector, producing a newDenseVector
. - add(SGDVector) - Method in interface org.tribuo.math.la.SGDVector
-
Adds
other
to this vector, producing a newSGDVector
. - add(SGDVector) - Method in class org.tribuo.math.la.SparseVector
-
Adds
other
to this vector, producing a newSGDVector
. - add(SequenceExample<T>) - Method in class org.tribuo.sequence.ImmutableSequenceDataset
-
Adds a
SequenceExample
to the dataset, which will insert feature ids, remove unknown features and sort the examples by the feature ids. - add(SequenceExample<T>) - Method in class org.tribuo.sequence.MutableSequenceDataset
-
Adds a
SequenceExample
to this dataset. - add(SequenceExample<T>, Merger) - Method in class org.tribuo.sequence.ImmutableSequenceDataset
-
Adds a
SequenceExample
to the dataset, which will insert feature ids, remove unknown features and sort the examples by the feature ids. - add(Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList
-
Unsupported.
- ADD - Enum constant in enum class org.tribuo.classification.sequence.viterbi.ViterbiModel.ScoreAggregation
-
Adds the scores.
- ADD - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators
-
Element-wise addition with broadcasting.
- addAcrossDim1(int[], double) - Method in class org.tribuo.math.la.DenseMatrix
-
Adds the specified value to the specified elements across dimension 1.
- addAcrossDim2(int[], double) - Method in class org.tribuo.math.la.DenseMatrix
-
Adds the specified value to the specified elements across dimension 2.
- addAll(int, Collection<? extends Row<T>>) - Method in class org.tribuo.util.infotheory.impl.RowList
-
Unsupported.
- addAll(Collection<? extends Example<T>>) - Method in class org.tribuo.MutableDataset
-
Adds all the Examples in the supplied collection to this dataset.
- addAll(Collection<? extends Feature>) - Method in class org.tribuo.Example
-
Adds a collection of features.
- addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.ArrayExample
- addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.BinaryFeaturesExample
-
Adds a collection of features to this example.
- addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.IndexedArrayExample
- addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.ListExample
- addAll(Collection<? extends Row<T>>) - Method in class org.tribuo.util.infotheory.impl.RowList
-
Unsupported.
- addAll(Collection<SequenceExample<T>>) - Method in class org.tribuo.sequence.MutableSequenceDataset
-
Adds all the SequenceExamples in the supplied collection to this dataset.
- addBias - Variable in class org.tribuo.common.sgd.AbstractSGDModel
- addBias - Variable in class org.tribuo.common.sgd.AbstractSGDTrainer
- addChar() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
-
Add a character to the buffer that we're building for a token.
- addConsideration(CommandInterpreter, String) - Method in class org.tribuo.interop.modelcard.ModelCardCLI
-
Adds a consideration for the model documented by the ModelCard to its list of considerations.
- addExample(Example<T>) - Method in class org.tribuo.sequence.SequenceExample
-
Adds an
Example
to this sequence. - addFactor(CommandInterpreter, String) - Method in class org.tribuo.interop.modelcard.ModelCardCLI
-
Adds a factor for the model documented by the ModelCard to its list of factors.
- addFeatureProcessor(FeatureProcessor) - Method in class org.tribuo.data.columnar.RowProcessor.Builder
-
Add a single feature processor to the builder.
- addFieldProcessor(FieldProcessor) - Method in class org.tribuo.data.columnar.RowProcessor.Builder
-
Add a single field processor to the builder.
- addMetadataExtractor(FieldExtractor<?>) - Method in class org.tribuo.data.columnar.RowProcessor.Builder
-
Add a single metadata extractor to the builder.
- addOutOfScopeUse(CommandInterpreter, String) - Method in class org.tribuo.interop.modelcard.ModelCardCLI
-
Adds an out-of-scope use of the model documented by the ModelCard to its list of out-of-scope uses.
- addPreProcessingStep(CommandInterpreter, String) - Method in class org.tribuo.interop.modelcard.ModelCardCLI
-
Adds a pre-processing step for the model documented by the ModelCard to its list of pre-processing steps.
- addRegexMappingProcessor(String, FieldProcessor) - Method in class org.tribuo.data.columnar.RowProcessor.Builder
-
Add a single regex FieldProcessor mapping to the builder.
- addResource(CommandInterpreter, String) - Method in class org.tribuo.interop.modelcard.ModelCardCLI
-
Adds a resource for the model documented by the ModelCard to its list of resources.
- ADJUSTED_MI - Enum constant in enum class org.tribuo.clustering.evaluation.ClusteringMetrics
-
The normalized mutual information adjusted for chance.
- adjustedMI() - Method in interface org.tribuo.clustering.evaluation.ClusteringEvaluation
-
Measures the adjusted normalized mutual information between the predicted ids and the supplied ids.
- adjustedMI(List<Integer>, List<Integer>) - Static method in enum class org.tribuo.clustering.evaluation.ClusteringMetrics
-
Calculates the adjusted normalized mutual information between two clusterings.
- adjustedMI(ClusteringMetric.Context) - Static method in enum class org.tribuo.clustering.evaluation.ClusteringMetrics
-
Calculates the adjusted normalized mutual information between two clusterings.
- advance() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer
- advance() - Method in class org.tribuo.util.tokens.impl.NonTokenizer
- advance() - Method in class org.tribuo.util.tokens.impl.ShapeTokenizer
- advance() - Method in class org.tribuo.util.tokens.impl.SplitFunctionTokenizer
- advance() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer
- advance() - Method in class org.tribuo.util.tokens.impl.wordpiece.WordpieceTokenizer
- advance() - Method in interface org.tribuo.util.tokens.Tokenizer
-
Advances the tokenizer to the next token.
- advance() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
- aggregate(List<Feature>) - Method in interface org.tribuo.data.text.FeatureAggregator
-
Aggregates feature values with the same names.
- aggregate(List<Feature>) - Method in class org.tribuo.data.text.impl.AverageAggregator
- aggregate(List<Feature>) - Method in class org.tribuo.data.text.impl.SumAggregator
- aggregate(List<Feature>) - Method in class org.tribuo.data.text.impl.UniqueAggregator
- AggregateConfigurableDataSource<T extends Output<T>> - Class in org.tribuo.datasource
-
Aggregates multiple
ConfigurableDataSource
s, usesAggregateDataSource.IterationOrder
to control the iteration order. - AggregateConfigurableDataSource(List<ConfigurableDataSource<T>>) - Constructor for class org.tribuo.datasource.AggregateConfigurableDataSource
-
Creates an aggregate data source which will iterate the provided sources in the order of the list (i.e., using
AggregateDataSource.IterationOrder.SEQUENTIAL
. - AggregateConfigurableDataSource(List<ConfigurableDataSource<T>>, AggregateDataSource.IterationOrder) - Constructor for class org.tribuo.datasource.AggregateConfigurableDataSource
-
Creates an aggregate data source using the supplied sources and iteration order.
- AggregateConfigurableDataSource.AggregateConfigurableDataSourceProvenance - Class in org.tribuo.datasource
-
Provenance for the
AggregateConfigurableDataSource
. - AggregateConfigurableDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.datasource.AggregateConfigurableDataSource.AggregateConfigurableDataSourceProvenance
-
Deserialization constructor.
- AggregateDataSource<T extends Output<T>> - Class in org.tribuo.datasource
-
Aggregates multiple
DataSource
s, usesAggregateDataSource.IterationOrder
to control the iteration order. - AggregateDataSource(List<DataSource<T>>) - Constructor for class org.tribuo.datasource.AggregateDataSource
-
Creates an aggregate data source which will iterate the provided sources in the order of the list (i.e., using
AggregateDataSource.IterationOrder.SEQUENTIAL
. - AggregateDataSource(List<DataSource<T>>, AggregateDataSource.IterationOrder) - Constructor for class org.tribuo.datasource.AggregateDataSource
-
Creates an aggregate data source using the supplied sources and iteration order.
- AggregateDataSource.AggregateDataSourceProvenance - Class in org.tribuo.datasource
-
Provenance for the
AggregateDataSource
. - AggregateDataSource.IterationOrder - Enum Class in org.tribuo.datasource
-
Specifies the iteration order of the inner sources.
- AggregateDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.datasource.AggregateDataSource.AggregateDataSourceProvenance
-
Deserialization constructor.
- algorithm - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
-
Type of learner (or base learner).
- algorithm - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
-
Type of SVR.
- algorithm - Variable in class org.tribuo.regression.slm.TrainTest.SLMOptions
-
Choose the training algorithm (stepwise forward selection or least angle regression).
- ALL - Enum constant in enum class org.tribuo.json.StripProvenance.ProvenanceTypes
-
Selects all provenance stored in the model.
- ALL_OUTPUTS - Static variable in class org.tribuo.Model
-
Used in getTopFeatures when the Model doesn't support per output feature lists.
- AllClassificationOptions() - Constructor for class org.tribuo.classification.experiments.TrainTest.AllClassificationOptions
- allProvenances() - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance
- allProvenances() - Method in class org.tribuo.dataset.MinimumCardinalityDataset.MinimumCardinalityDatasetProvenance
- allProvenances() - Method in class org.tribuo.dataset.SelectedFeatureDataset.SelectedFeatureDatasetProvenance
- allProvenances() - Method in class org.tribuo.provenance.DatasetProvenance
-
Returns a list of all the provenances.
- allProvenances() - Method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset.MinimumCardinalitySequenceDatasetProvenance
- AllTrainerOptions - Class in org.tribuo.classification.experiments
-
Aggregates all the classification algorithms.
- AllTrainerOptions() - Constructor for class org.tribuo.classification.experiments.AllTrainerOptions
- AllTrainerOptions.AlgorithmType - Enum Class in org.tribuo.classification.experiments
-
Types of algorithms supported.
- alpha - Variable in class org.tribuo.regression.slm.TrainTest.SLMOptions
-
Regularisation strength in the Elastic Net.
- alpha - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
-
L1 regularization term for weights (default 0).
- alpha - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
-
L1 regularization term for weights (default 0).
- alphas - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults
-
The alpha values array from forward propagation.
- annotateGraph(Graph, Session) - Static method in class org.tribuo.interop.tensorflow.TensorFlowUtil
-
Annotates a graph with an extra placeholder and assign operation for each VariableV2.
- ANOMALOUS - Enum constant in enum class org.tribuo.anomaly.Event.EventType
-
An anomalous event, with id 1.
- ANOMALOUS_EVENT - Static variable in class org.tribuo.anomaly.AnomalyFactory
-
The anomalous event.
- ANOMALY_DETECTION - Enum constant in enum class org.tribuo.interop.oci.OCIUtil.OCIModelType
-
Anomaly detection, maps to Tribuo's
Event
. - anomalyCount - Variable in class org.tribuo.anomaly.AnomalyInfo
-
The number of anomalous events observed.
- AnomalyDataGenerator - Class in org.tribuo.anomaly.example
-
Generates three example train and test datasets, used for unit testing.
- AnomalyDataGenerator() - Constructor for class org.tribuo.anomaly.example.AnomalyDataGenerator
- AnomalyEvaluation - Interface in org.tribuo.anomaly.evaluation
-
An
Evaluation
for anomaly detectionEvent
s. - AnomalyEvaluator - Class in org.tribuo.anomaly.evaluation
- AnomalyEvaluator() - Constructor for class org.tribuo.anomaly.evaluation.AnomalyEvaluator
- AnomalyFactory - Class in org.tribuo.anomaly
-
A factory for generating events.
- AnomalyFactory() - Constructor for class org.tribuo.anomaly.AnomalyFactory
-
Create an AnomalyFactory.
- AnomalyFactory.AnomalyFactoryProvenance - Class in org.tribuo.anomaly
-
Provenance for
AnomalyFactory
. - AnomalyFactoryProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.anomaly.AnomalyFactory.AnomalyFactoryProvenance
-
Constructs an anomaly factory provenance from the marshalled form.
- AnomalyInfo - Class in org.tribuo.anomaly
-
The base class for tracking anomalous events.
- AnomalyInfo() - Constructor for class org.tribuo.anomaly.AnomalyInfo
-
Constructs a new empty anomaly info.
- AnomalyInfo(long, long, int) - Constructor for class org.tribuo.anomaly.AnomalyInfo
-
Deserialization constructor.
- AnomalyInfo(AnomalyInfo) - Constructor for class org.tribuo.anomaly.AnomalyInfo
-
Copies the supplied anomaly info.
- AnomalyMetric - Class in org.tribuo.anomaly.evaluation
-
A metric for evaluating anomaly detection problems.
- AnomalyMetric(MetricTarget<Event>, String, ToDoubleBiFunction<MetricTarget<Event>, AnomalyMetric.Context>) - Constructor for class org.tribuo.anomaly.evaluation.AnomalyMetric
-
Creates an anomaly detection metric, with a specific name, using the supplied evaluation function.
- AnomalyMetrics - Enum Class in org.tribuo.anomaly.evaluation
-
Default metrics for evaluating anomaly detection.
- apply(int, int, CharSequence) - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer.SplitCharactersSplitterFunction
- apply(int, int, CharSequence) - Method in interface org.tribuo.util.tokens.impl.SplitFunctionTokenizer.SplitFunction
-
Applies the split function.
- apply(E) - Method in interface org.tribuo.evaluation.EvaluationRenderer
-
Convert the evaluation to a string.
- apply(ONNXOperator) - Method in class org.tribuo.util.onnx.ONNXRef
-
Convenience method that calls
ONNXContext.operation(ONNXOperator, List, String)
, using this ONNXRef as the argument toinputs
. - apply(ONNXOperator, String) - Method in class org.tribuo.util.onnx.ONNXRef
-
Convenience method that calls
ONNXContext.operation(ONNXOperator, List, String)
, using this ONNXRef as the argument toinputs
. - apply(ONNXOperator, List<String>, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef
-
Convenience method that calls
ONNXContext.operation(ONNXOperator, List, List, Map)
, using this ONNXRef as the argument toinputs
. - apply(ONNXOperator, List<ONNXRef<?>>) - Method in class org.tribuo.util.onnx.ONNXRef
-
Convenience method that calls
ONNXContext.operation(ONNXOperator, List, String, Map)
, using this ONNXRef as the first argument toinputs
, withotherInputs
append as subsequent arguments. - apply(ONNXOperator, List<ONNXRef<?>>, String) - Method in class org.tribuo.util.onnx.ONNXRef
-
Convenience method that calls
ONNXContext.operation(ONNXOperator, List, String, Map)
, using this ONNXRef as the argument toinputs
, withotherInputs
append as subsequent arguments. - apply(ONNXOperator, List<ONNXRef<?>>, List<String>, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef
-
Convenience method that calls
ONNXContext.operation(ONNXOperator, List, List, Map)
, using this ONNXRef as the first argument toinputs
, withotherInputs
append as subsequent arguments. - apply(ONNXOperator, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef
-
Convenience method that calls
ONNXContext.operation(ONNXOperator, List, String, Map)
, using this ONNXRef as the argument toinputs
. - apply(ONNXOperator, ONNXRef<?>) - Method in class org.tribuo.util.onnx.ONNXRef
-
Convenience method that calls
ONNXContext.operation(ONNXOperator, List, String, Map)
, passing this ONNXRef andother
as a length 2 list toinputs
. - apply(ONNXOperator, ONNXRef<?>, String) - Method in class org.tribuo.util.onnx.ONNXRef
-
Convenience method that calls
ONNXContext.operation(ONNXOperator, List, String)
, passing this ONNXRef andother
as a length 2 list toinputs
. - apply(ONNXOperator, ONNXRef<?>, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef
-
Convenience method that calls
ONNXContext.operation(ONNXOperator, List, String, Map)
, passing this ONNXRef andother
as a length 2 list toinputs
. - applyCase(String) - Method in enum class org.tribuo.data.text.impl.CasingPreprocessor.CasingOperation
-
Apply the appropriate casing operation.
- applyOptimiser(Graph, Operand<T>, Map<String, Float>) - Method in enum class org.tribuo.interop.tensorflow.GradientOptimiser
-
Applies the optimiser to the graph and returns the optimiser step operation.
- applyTransformerList(double, List<Transformer>) - Static method in class org.tribuo.transform.TransformerMap
-
Applies a
List
ofTransformer
s to the supplied double value, returning the transformed value. - APPROX - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.TreeMethod
-
Approximate greedy algorithm, using a quantile sketch of the data and a gradient histogram.
- approximateParentNode(SGDVector) - Method in class org.tribuo.math.neighbour.kdtree.KDTree
-
This makes a fast approximation of the provided point's parent node, if it were being inserted into the tree.
- ARCH_STRING - Static variable in class org.tribuo.provenance.ModelProvenance
-
The name of the architecture name field.
- archString - Variable in class org.tribuo.provenance.ModelProvenance
-
The system CPU architecture string.
- argmax(List<R>, Function<R, Double>) - Static method in class org.tribuo.evaluation.EvaluationAggregator
-
Calculates the argmax of a metric across the supplied evaluations.
- argmax(List<T>) - Static method in class org.tribuo.util.Util
-
Find the index of the maximum value in a list.
- argmax(EvaluationMetric<T, C>, List<? extends Model<T>>, Dataset<T>) - Static method in class org.tribuo.evaluation.EvaluationAggregator
-
Calculates the argmax of a metric across the supplied models (i.e., the index of the model which performed the best).
- argmax(EvaluationMetric<T, C>, Model<T>, List<? extends Dataset<T>>) - Static method in class org.tribuo.evaluation.EvaluationAggregator
-
Calculates the argmax of a metric across the supplied datasets.
- argmin(List<T>) - Static method in class org.tribuo.util.Util
-
Find the index of the minimum value in a list.
- array - Variable in class org.tribuo.common.tree.impl.IntArrayContainer
-
The array of ints.
- array(String, double[]) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates a float tensor for this ONNXContext, populated according to parameters.
- array(String, double[], boolean) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates a tensor for this ONNXContext, populated according to parameters.
- array(String, float[]) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates a float tensor for this ONNXContext, populated according to parameters.
- array(String, int[]) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates an int tensor for this ONNXContext, populated according to parameters.
- array(String, long[]) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates a long tensor for this ONNXContext, populated according to parameters.
- ARRAY_FEATURE_EXTRACTOR - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators
-
Array feature extractor, selects the indices specified by the second tensor from the last dimension of the first tensor.
- arrayBuilder(ONNXContext, String, double[]) - Static method in class org.tribuo.util.onnx.ONNXUtils
-
Builds a TensorProto containing the array.
- arrayBuilder(ONNXContext, String, double[], boolean) - Static method in class org.tribuo.util.onnx.ONNXUtils
-
Builds a TensorProto containing the array.
- arrayBuilder(ONNXContext, String, float[]) - Static method in class org.tribuo.util.onnx.ONNXUtils
-
Builds a TensorProto containing the array.
- arrayBuilder(ONNXContext, String, int[]) - Static method in class org.tribuo.util.onnx.ONNXUtils
-
Builds a TensorProto containing the array.
- arrayBuilder(ONNXContext, String, long[]) - Static method in class org.tribuo.util.onnx.ONNXUtils
-
Builds a TensorProto containing the array.
- ArrayExample<T extends Output<T>> - Class in org.tribuo.impl
-
An
Example
backed by two arrays, one of String and one of double. - ArrayExample(Example<T>) - Constructor for class org.tribuo.impl.ArrayExample
-
Copy constructor.
- ArrayExample(T) - Constructor for class org.tribuo.impl.ArrayExample
-
Constructs an example from an output.
- ArrayExample(T, float) - Constructor for class org.tribuo.impl.ArrayExample
-
Constructs an example from an output and a weight.
- ArrayExample(T, float, int) - Constructor for class org.tribuo.impl.ArrayExample
-
Constructs an example from an output and a weight, with an initial size for the feature arrays.
- ArrayExample(T, float, Map<String, Object>) - Constructor for class org.tribuo.impl.ArrayExample
-
Constructs an example from an output, a weight and the metadata.
- ArrayExample(T, String[], double[]) - Constructor for class org.tribuo.impl.ArrayExample
-
Constructs an example from an output, an array of names and an array of values.
- ArrayExample(T, List<? extends Feature>) - Constructor for class org.tribuo.impl.ArrayExample
-
Constructs an example from an output and a list of features.
- ArrayExample(T, Map<String, Object>) - Constructor for class org.tribuo.impl.ArrayExample
-
Constructs an example from an output and the metadata.
- ArrayExample(T, Example<U>, float) - Constructor for class org.tribuo.impl.ArrayExample
-
Clones an example's features, but uses the supplied output and weight.
- asMap() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
- asMap() - Method in interface org.tribuo.evaluation.Evaluation
-
Get a map of all the metrics stored in this evaluation.
- asMap() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
- asMap() - Method in interface org.tribuo.sequence.SequenceEvaluation
-
Get a map of all the metrics stored in this evaluation.
- ASSIGN_OP - Static variable in class org.tribuo.interop.tensorflow.TensorFlowUtil
-
The name of the assignment op.
- ASSIGN_PLACEHOLDER - Static variable in class org.tribuo.interop.tensorflow.TensorFlowUtil
-
The name given to the assignment operation from the placeholders.
- assignTo(Ret) - Method in class org.tribuo.util.onnx.ONNXRef
-
Convenience method that calls
ONNXContext.assignTo(ONNXRef, ONNXRef)
, using this ONNXRef as the argument toinput
. - assignTo(RHS, LHS) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates an
ONNXOperators.IDENTITY
node connectinginput
tooutput
, effectively permitting assignment of values. - ATTENTION_MASK - Static variable in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
-
Input name for the attention mask.
- attributes - Variable in enum class org.tribuo.util.onnx.ONNXOperators
-
The operator attributes.
- auc(double[], double[]) - Static method in class org.tribuo.util.Util
-
Calculates the area under the curve, bounded below by the x axis.
- AUCROC - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics
-
The area under the receiver-operator curve (ROC).
- AUCROC(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation
-
Area under the ROC curve.
- AUCROC(Label, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics
-
Area under the ROC curve.
- AUCROC(MetricTarget<Label>, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics
-
Area under the ROC curve.
- AUTO - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.TreeMethod
-
XGBoost chooses between
XGBoostTrainer.TreeMethod.EXACT
andXGBoostTrainer.TreeMethod.APPROX
depending on dataset size. - AverageAggregator - Class in org.tribuo.data.text.impl
-
A feature aggregator that averages feature values across a feature list.
- AverageAggregator() - Constructor for class org.tribuo.data.text.impl.AverageAggregator
- averageAUCROC(boolean) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation
-
Area under the ROC curve averaged across labels.
- AVERAGED_PRECISION - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics
-
The averaged precision.
- averagedExplainedVariance() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation
-
The average explained variance across all dimensions.
- averagedPrecision(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil
-
Summarises a Precision-Recall Curve by taking the weighted mean of the precisions at a given threshold, where the weight is the recall achieved at that threshold.
- averagedPrecision(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation
-
Summarises a Precision-Recall Curve by taking the weighted mean of the precisions at a given threshold, where the weight is the recall achieved at that threshold.
- averagedPrecision(Label, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics
- averagedPrecision(MetricTarget<Label>, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics
- averageMAE() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation
-
The average Mean Absolute Error across all dimensions.
- averageR2() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation
-
The average R2 across all dimensions.
- averageRMSE() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation
-
The average RMSE across all dimensions.
- AveragingCombiner - Class in org.tribuo.regression.ensemble
-
A combiner which performs a weighted or unweighted average of the predicted regressors independently across the output dimensions.
- AveragingCombiner() - Constructor for class org.tribuo.regression.ensemble.AveragingCombiner
-
Constructs an averaging combiner.
B
- b - Variable in class org.tribuo.util.infotheory.impl.CachedTriple
-
The second element.
- backRef - Variable in class org.tribuo.util.onnx.ONNXRef
-
Protobuf reference.
- BAGGING - Enum constant in enum class org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType
-
Creates a
BaggingTrainer
. - BaggingTrainer<T extends Output<T>> - Class in org.tribuo.ensemble
-
A Trainer that wraps another trainer and produces a bagged ensemble.
- BaggingTrainer() - Constructor for class org.tribuo.ensemble.BaggingTrainer
-
For the configuration system.
- BaggingTrainer(Trainer<T>, EnsembleCombiner<T>, int) - Constructor for class org.tribuo.ensemble.BaggingTrainer
-
Constructs a bagging trainer with the supplied parameters using
Trainer.DEFAULT_SEED
as the RNG seed. - BaggingTrainer(Trainer<T>, EnsembleCombiner<T>, int, long) - Constructor for class org.tribuo.ensemble.BaggingTrainer
-
Constructs a bagging trainer with the supplied parameters.
- BALANCED_ERROR_RATE - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics
-
The balanced error rate, i.e., the mean of the per class recalls.
- BALANCED_ERROR_RATE - Enum constant in enum class org.tribuo.multilabel.evaluation.MultiLabelMetrics
-
The balanced error rate, i.e., the mean of the per class recalls.
- balancedErrorRate() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
-
Returns the balanced error rate, i.e., the mean of the per label recalls.
- balancedErrorRate() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
-
Gets the balanced error rate.
- balancedErrorRate() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
- balancedErrorRate(ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
-
Calculates the balanced error rate, i.e., the mean of the recalls.
- bandwidth - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions
-
Model bandwidth in MBps.
- bandwidth - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelDeploymentConfig
-
The bandwidth for the load balancer in MBps.
- BasicPipeline - Class in org.tribuo.data.text.impl
-
An example implementation of
TextPipeline
. - BasicPipeline(Tokenizer, int) - Constructor for class org.tribuo.data.text.impl.BasicPipeline
-
Constructs a basic text pipeline which tokenizes the input and generates word n-gram features in the range 1 to
ngram
. - batchSize - Variable in class org.tribuo.interop.tensorflow.TensorFlowModel
- batchSize - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
-
Minibatch size.
- begin - Variable in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence
-
The subsequence start index.
- begin - Variable in class org.tribuo.classification.sgd.crf.Chunk
-
The starting point of this chunk.
- beliefPropagation(ChainHelper.ChainCliqueValues) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper
-
Runs belief propagation on a linear chain CRF.
- bert - Variable in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor.BERTFeatureExtractorOptions
-
BERTFeatureExtractor instance
- BERTFeatureExtractor<T extends Output<T>> - Class in org.tribuo.interop.onnx.extractors
-
Builds examples and sequence examples using features from BERT.
- BERTFeatureExtractor(OutputFactory<T>, Path, Path) - Constructor for class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
-
Constructs a BERTFeatureExtractor.
- BERTFeatureExtractor(OutputFactory<T>, Path, Path, BERTFeatureExtractor.OutputPooling, int, boolean) - Constructor for class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
-
Constructs a BERTFeatureExtractor.
- BERTFeatureExtractor.BERTFeatureExtractorOptions - Class in org.tribuo.interop.onnx.extractors
-
CLI options for running BERT.
- BERTFeatureExtractor.OutputPooling - Enum Class in org.tribuo.interop.onnx.extractors
-
The type of output pooling to perform.
- BERTFeatureExtractorOptions() - Constructor for class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor.BERTFeatureExtractorOptions
- betas - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults
-
The beta values array from backward propagation.
- BIAS_FEATURE - Static variable in class org.tribuo.Model
-
Used to denote the bias feature in a linear model.
- binarise - Enum constant in enum class org.tribuo.transform.transformations.SimpleTransform.Operation
-
Binarises the output around 1.0.
- binarise() - Static method in class org.tribuo.transform.transformations.SimpleTransform
-
Generate a SimpleTransform that sets negative and zero values to zero and positive values to one.
- BINARISED_CATEGORICAL - Enum constant in enum class org.tribuo.data.columnar.FieldProcessor.GeneratedFeatureType
-
Categoricals binarised into separate features.
- BINARY_CLASSIFICATION - Enum constant in enum class org.tribuo.interop.oci.OCIUtil.OCIModelType
-
Binary classification.
- binaryAUCROC(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil
-
Calculates the area under the receiver operator characteristic curve, i.e., the AUC of the ROC curve.
- BinaryCrossEntropy - Class in org.tribuo.multilabel.sgd.objectives
-
A multilabel version of binary cross entropy loss which expects logits.
- BinaryCrossEntropy() - Constructor for class org.tribuo.multilabel.sgd.objectives.BinaryCrossEntropy
-
Constructs a BinaryCrossEntropy objective.
- BinaryFeaturesExample<T extends Output<T>> - Class in org.tribuo.impl
-
An
Example
backed by a single array of feature names. - BinaryFeaturesExample(Example<T>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample
-
Copy constructor.
- BinaryFeaturesExample(T) - Constructor for class org.tribuo.impl.BinaryFeaturesExample
-
Constructs an example from an output.
- BinaryFeaturesExample(T, float) - Constructor for class org.tribuo.impl.BinaryFeaturesExample
-
Constructs an example from an output and a weight.
- BinaryFeaturesExample(T, float, int) - Constructor for class org.tribuo.impl.BinaryFeaturesExample
-
Constructs an example from an output and a weight, with an initial size for the feature arrays.
- BinaryFeaturesExample(T, float, Map<String, Object>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample
-
Constructs an example from an output, a weight and the metadata.
- BinaryFeaturesExample(T, String[]) - Constructor for class org.tribuo.impl.BinaryFeaturesExample
-
Constructs an example from an output and an array of names.
- BinaryFeaturesExample(T, List<? extends Feature>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample
-
Constructs an example from an output and a list of features.
- BinaryFeaturesExample(T, Map<String, Object>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample
-
Constructs an example from an output and the metadata.
- BinaryFeaturesExample(T, Example<U>, float) - Constructor for class org.tribuo.impl.BinaryFeaturesExample
-
Clones an example's features, but uses the supplied output and weight.
- BinaryResponseProcessor<T extends Output<T>> - Class in org.tribuo.data.columnar.processors.response
-
A
ResponseProcessor
that takes a single value of the field as the positive class and all other values as the negative class. - BinaryResponseProcessor(String, String, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor
-
Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
- BinaryResponseProcessor(List<String>, String, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor
-
Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
- BinaryResponseProcessor(List<String>, List<String>, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor
-
Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
- BinaryResponseProcessor(List<String>, List<String>, OutputFactory<T>, boolean) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor
-
Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
- BinaryResponseProcessor(List<String>, List<String>, OutputFactory<T>, String, String, boolean) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor
-
Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
- binarySearch(List<? extends Comparable<? super T>>, T) - Static method in class org.tribuo.util.Util
-
A binary search function.
- binarySearch(List<? extends Comparable<? super T>>, T, int, int) - Static method in class org.tribuo.util.Util
-
A binary search function.
- binarySearch(List<? extends T>, int, ToIntFunction<T>) - Static method in class org.tribuo.util.Util
-
A binary search function.
- binarySparseTrainTest() - Static method in class org.tribuo.classification.example.LabelledDataGenerator
-
Generates a pair of datasets with sparse features and unknown features in the test data.
- binarySparseTrainTest(double) - Static method in class org.tribuo.classification.example.LabelledDataGenerator
-
Generates a pair of datasets with sparse features and unknown features in the test data.
- BinningTransformation - Class in org.tribuo.transform.transformations
-
A Transformation which bins values.
- BinningTransformation.BinningTransformationProvenance - Class in org.tribuo.transform.transformations
-
Provenance for
BinningTransformation
. - BinningTransformation.BinningTransformer - Class in org.tribuo.transform.transformations
-
The implementation of a
Transformer
which splits the input into n bins. - BinningTransformation.BinningType - Enum Class in org.tribuo.transform.transformations
-
The allowed binning types.
- BinningTransformationProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.transform.transformations.BinningTransformation.BinningTransformationProvenance
-
Deserialization constructor.
- BinningTransformer(BinningTransformation.BinningType, double[], double[]) - Constructor for class org.tribuo.transform.transformations.BinningTransformation.BinningTransformer
-
Constructs a binning transformer with the supplied bin widths and bin values.
- BREAK_ITERATOR - Enum constant in enum class org.tribuo.util.tokens.options.CoreTokenizerOptions.CoreTokenizerType
-
Creates a
BreakIteratorTokenizer
. - breakIteratorOptions - Variable in class org.tribuo.util.tokens.options.CoreTokenizerOptions
-
Options for the break iterator tokenizer.
- BreakIteratorTokenizer - Class in org.tribuo.util.tokens.impl
-
A tokenizer wrapping a
BreakIterator
instance. - BreakIteratorTokenizer(Locale) - Constructor for class org.tribuo.util.tokens.impl.BreakIteratorTokenizer
-
Constructs a BreakIteratorTokenizer using the specified locale.
- BreakIteratorTokenizerOptions - Class in org.tribuo.util.tokens.options
-
CLI options for a
BreakIteratorTokenizer
. - BreakIteratorTokenizerOptions() - Constructor for class org.tribuo.util.tokens.options.BreakIteratorTokenizerOptions
- broadcastIntersectAndAddInPlace(SGDVector, boolean) - Method in class org.tribuo.math.la.DenseMatrix
-
Broadcasts the input vector and adds it to each row/column of the matrix.
- BRUTE_FORCE - Enum constant in enum class org.tribuo.math.neighbour.NeighboursQueryFactoryType
-
A factory which emits nearest neighbour query objects using a brute-force search.
- buff - Variable in class org.tribuo.util.tokens.universal.Range
-
The character buffer.
- build() - Method in class org.tribuo.interop.modelcard.UsageDetailsBuilder
-
Builds an instance of
UsageDetails
using the recorded field values or their default values. - build(Object) - Method in class org.tribuo.util.onnx.ONNXAttribute
-
Builds the attribute proto using the supplied value.
- build(ResponseProcessor<T>) - Method in class org.tribuo.data.columnar.RowProcessor.Builder
-
Construct the
RowProcessor
represented by this builder's state. - build(ONNXContext, String[], String) - Method in interface org.tribuo.util.onnx.ONNXOperator
-
Builds this node based on the supplied inputs and output.
- build(ONNXContext, String[], String[]) - Method in interface org.tribuo.util.onnx.ONNXOperator
-
Builds this node based on the supplied inputs and outputs.
- build(ONNXContext, String[], String[], Map<String, Object>) - Method in interface org.tribuo.util.onnx.ONNXOperator
-
Builds this node based on the supplied inputs and outputs.
- build(ONNXContext, String[], String, Map<String, Object>) - Method in interface org.tribuo.util.onnx.ONNXOperator
-
Builds this node based on the supplied inputs and output.
- build(ONNXContext, String, String) - Method in interface org.tribuo.util.onnx.ONNXOperator
-
Builds this node based on the supplied inputs and output.
- build(ONNXContext, String, String[]) - Method in interface org.tribuo.util.onnx.ONNXOperator
-
Builds this node based on the supplied input and outputs.
- build(ONNXContext, String, String[], Map<String, Object>) - Method in interface org.tribuo.util.onnx.ONNXOperator
-
Builds this node based on the supplied input and outputs.
- build(ONNXContext, String, String, Map<String, Object>) - Method in interface org.tribuo.util.onnx.ONNXOperator
-
Builds this node based on the supplied inputs and output.
- BUILD_TIMESTAMP - Static variable in class org.tribuo.Tribuo
-
The build timestamp.
- Builder() - Constructor for class org.tribuo.data.columnar.RowProcessor.Builder
-
Builder for
RowProcessor
, see RowProcessor constructors for argument details. - buildGraph() - Method in class org.tribuo.util.onnx.ONNXContext
-
Builds the ONNX graph represented by this context.
- buildLeNetGraph(String, int, int, int) - Static method in class org.tribuo.interop.tensorflow.example.CNNExamples
-
Builds a LeNet 5 style CNN (usually used for MNIST).
- buildMLPGraph(String, int, int[], int) - Static method in class org.tribuo.interop.tensorflow.example.MLPExamples
-
Builds an MLP which expects the supplied number of inputs, has hiddenSizes.length hidden layers, before emitting numOutput outputs.
- buildModel(ONNXContext, String, long, M) - Static method in interface org.tribuo.ONNXExportable
-
Creates an ONNX model protobuf for the supplied context.
- buildRandomTree(int[], SplittableRandom) - Method in class org.tribuo.classification.dtree.impl.ClassifierTrainingNode
-
Builds a CART tree with randomly chosen split points.
- buildRuntimeYaml(String, String) - Static method in class org.tribuo.interop.oci.OCIUtil
-
Builds the runtime.yaml String from the supplied arguments, throwing
IllegalArgumentException
if they are invalid. - buildTensorTypeNode(ONNXShape, OnnxMl.TensorProto.DataType) - Static method in class org.tribuo.util.onnx.ONNXUtils
-
Builds a type proto for the specified shape and tensor type.
- buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.classification.dtree.impl.ClassifierTrainingNode
-
Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
- buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.common.tree.AbstractTrainingNode
-
Builds next level of a tree.
- buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode
-
Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
- buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode
-
Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
- BYTE - Enum constant in enum class org.tribuo.datasource.IDXDataSource.IDXType
-
A signed byte.
C
- c - Variable in class org.tribuo.util.infotheory.impl.CachedTriple
-
The third element.
- C_SVC - Enum constant in enum class org.tribuo.classification.libsvm.SVMClassificationType.SVMMode
-
Original SVM algorithm.
- CachedPair<T1,
T2> - Class in org.tribuo.util.infotheory.impl -
A pair of things with a cached hashcode.
- CachedPair(T1, T2) - Constructor for class org.tribuo.util.infotheory.impl.CachedPair
-
Constructs a CachedPair.
- CachedTriple<T1,
T2, T3> - Class in org.tribuo.util.infotheory.impl -
A triple of things.
- CachedTriple(T1, T2, T3) - Constructor for class org.tribuo.util.infotheory.impl.CachedTriple
-
Constructs a CachedTriple.
- cacheProvenance() - Method in class org.tribuo.data.text.impl.SimpleStringDataSource
- cacheProvenance() - Method in class org.tribuo.data.text.impl.SimpleTextDataSource
-
Computes the provenance.
- calculateCountDist(List<T>) - Static method in class org.tribuo.util.infotheory.InformationTheory
-
Generate the counts for a single vector.
- calculateEntropy(DoubleStream) - Static method in class org.tribuo.util.infotheory.InformationTheory
-
Calculates the discrete Shannon entropy of a stream, assuming each element of the stream is an element of the same probability distribution.
- calculateEntropy(Stream<Double>) - Static method in class org.tribuo.util.infotheory.InformationTheory
-
Calculates the discrete Shannon entropy of a stream, assuming each element of the stream is an element of the same probability distribution.
- calculateHashCode() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
-
Overridden hashcode.
- calculateWeightedCountDist(ArrayList<T>, ArrayList<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory
-
Generate the counts for a single vector.
- CALENDAR_QUARTER - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
-
The calendar quarter of the year.
- canonicalise(FeatureMap) - Method in class org.tribuo.sequence.SequenceExample
-
Reassigns feature name Strings in each Example inside this SequenceExample to point to those in the
FeatureMap
. - canonicalize(FeatureMap) - Method in class org.tribuo.Example
-
Reassigns feature name Strings in the Example to point to those in the
FeatureMap
. - canonicalize(FeatureMap) - Method in class org.tribuo.impl.ArrayExample
- canonicalize(FeatureMap) - Method in class org.tribuo.impl.BinaryFeaturesExample
- canonicalize(FeatureMap) - Method in class org.tribuo.impl.ListExample
- CART - Enum constant in enum class org.tribuo.classification.dtree.CARTClassificationOptions.TreeType
-
Builds a CART model.
- CART - Enum constant in enum class org.tribuo.classification.experiments.AllTrainerOptions.AlgorithmType
-
Creates a
CARTClassificationTrainer
. - CART_INDEPENDENT - Enum constant in enum class org.tribuo.regression.rtree.TrainTest.TreeType
-
Creates a
CARTRegressionTrainer
which treats each regression dimension independently. - CART_JOINT - Enum constant in enum class org.tribuo.regression.rtree.TrainTest.TreeType
-
Creates a
CARTJointRegressionTrainer
which jointly minimises the impurity across all output dimensions. - CARTClassificationOptions - Class in org.tribuo.classification.dtree
-
Options for building a classification tree trainer.
- CARTClassificationOptions() - Constructor for class org.tribuo.classification.dtree.CARTClassificationOptions
- CARTClassificationOptions.ImpurityType - Enum Class in org.tribuo.classification.dtree
-
The impurity algorithm.
- CARTClassificationOptions.TreeType - Enum Class in org.tribuo.classification.dtree
-
Type of decision tree algorithm.
- CARTClassificationTrainer - Class in org.tribuo.classification.dtree
-
A
Trainer
that uses an approximation of the CART algorithm to build a decision tree. - CARTClassificationTrainer() - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer
-
Creates a CART Trainer.
- CARTClassificationTrainer(int) - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer
-
Creates a CART trainer.
- CARTClassificationTrainer(int, float, boolean, long) - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer
-
Creates a CART Trainer.
- CARTClassificationTrainer(int, float, float, float, boolean, LabelImpurity, long) - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer
-
Creates a CART Trainer.
- CARTClassificationTrainer(int, float, float, float, LabelImpurity, long) - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer
-
Creates a CART Trainer.
- CARTClassificationTrainer(int, float, long) - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer
-
Creates a CART Trainer.
- cartImpurity - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions
-
Impurity measure to use.
- CARTJointRegressionTrainer - Class in org.tribuo.regression.rtree
-
A
Trainer
that uses an approximation of the CART algorithm to build a decision tree. - CARTJointRegressionTrainer() - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer
-
Creates a CART Trainer.
- CARTJointRegressionTrainer(int) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer
-
Creates a CART Trainer.
- CARTJointRegressionTrainer(int, boolean) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer
-
Creates a CART Trainer.
- CARTJointRegressionTrainer(int, float, float, float, boolean, RegressorImpurity, boolean, long) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer
-
Creates a CART Trainer.
- CARTJointRegressionTrainer(int, float, float, float, RegressorImpurity, boolean, long) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer
-
Creates a CART Trainer.
- cartMaxDepth - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions
-
Maximum depth in the decision tree.
- cartMinChildWeight - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions
-
Minimum child weight.
- cartMinImpurityDecrease - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions
-
Minimum impurity decrease.
- cartOptions - Variable in class org.tribuo.classification.dtree.TrainTest.TrainTestOptions
-
The CART trainer options.
- cartOptions - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
-
Options for CART trainers.
- cartPrintTree - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions
-
Prints the decision tree.
- cartRandomSplit - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions
-
Whether to choose split points for features at random.
- CARTRegressionTrainer - Class in org.tribuo.regression.rtree
-
A
Trainer
that uses an approximation of the CART algorithm to build a decision tree. - CARTRegressionTrainer() - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer
-
Creates a CART trainer.
- CARTRegressionTrainer(int) - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer
-
Creates a CART trainer.
- CARTRegressionTrainer(int, float, float, float, boolean, RegressorImpurity, long) - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer
-
Creates a CART Trainer.
- CARTRegressionTrainer(int, float, float, float, RegressorImpurity, long) - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer
-
Creates a CART Trainer.
- cartSeed - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions
-
RNG seed.
- cartSplitFraction - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions
-
Fraction of features in split.
- cartTreeAlgorithm - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions
-
Tree algorithm to use (options are CART).
- CasingPreprocessor - Class in org.tribuo.data.text.impl
-
A document preprocessor which uppercases or lowercases the input.
- CasingPreprocessor(CasingPreprocessor.CasingOperation) - Constructor for class org.tribuo.data.text.impl.CasingPreprocessor
-
Construct a casing preprocessor.
- CasingPreprocessor.CasingOperation - Enum Class in org.tribuo.data.text.impl
-
The possible casing operations.
- cast(Class<?>) - Method in class org.tribuo.util.onnx.ONNXRef
-
Casts this ONNXRef to a different type using the
ONNXOperators.CAST
operation, and returning the output node of that op. - CAST - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators
-
Cast input to specified type.
- castDataset(Dataset<?>, Class<T>) - Static method in class org.tribuo.Dataset
-
Casts the dataset to the specified output type, assuming it is valid.
- castDataset(SequenceDataset<?>, Class<T>) - Static method in class org.tribuo.sequence.SequenceDataset
-
Casts the dataset to the specified output type, assuming it is valid.
- castModel(Class<U>) - Method in class org.tribuo.Model
-
Casts the model to the specified output type, assuming it is valid.
- castModel(Class<U>) - Method in class org.tribuo.sequence.SequenceModel
-
Casts the model to the specified output type, assuming it is valid.
- CATEGORICAL - Enum constant in enum class org.tribuo.data.columnar.FieldProcessor.GeneratedFeatureType
-
Unordered categorical features with the values converted into doubles.
- CategoricalIDInfo - Class in org.tribuo
-
Same as a
CategoricalInfo
, but with an additional int id field. - CategoricalIDInfo(CategoricalInfo, int) - Constructor for class org.tribuo.CategoricalIDInfo
-
Constructs a categorical id info copying the information from the supplied info, with the specified id.
- CategoricalInfo - Class in org.tribuo
-
Stores information about Categorical features.
- CategoricalInfo(String) - Constructor for class org.tribuo.CategoricalInfo
-
Constructs a new empty categorical info for the supplied feature name.
- CategoricalInfo(CategoricalInfo) - Constructor for class org.tribuo.CategoricalInfo
-
Constructs a deep copy of the supplied categorical info.
- CategoricalInfo(CategoricalInfo, String) - Constructor for class org.tribuo.CategoricalInfo
-
Constructs a deep copy of the supplied categorical info, with the new feature name.
- CC_NEGATIVE - Static variable in class org.tribuo.multilabel.baseline.ClassifierChainTrainer
-
The string used in the feature name for negative labels.
- CC_POSITIVE - Static variable in class org.tribuo.multilabel.baseline.ClassifierChainTrainer
-
The string used in the feature name for positive labels.
- CC_PREFIX - Static variable in class org.tribuo.multilabel.baseline.ClassifierChainTrainer
-
The prefix for classifier chain added features.
- CC_SEPARATOR - Static variable in class org.tribuo.multilabel.baseline.ClassifierChainTrainer
-
The joiner character for classifier chain added features.
- CCEnsembleTrainer - Class in org.tribuo.multilabel.ensemble
-
A trainer for an ensemble of randomly ordered Classifier Chains.
- CCEnsembleTrainer(Trainer<Label>, int, long) - Constructor for class org.tribuo.multilabel.ensemble.CCEnsembleTrainer
-
Constructs a classifier chain ensemble trainer.
- cdf - Variable in class org.tribuo.CategoricalInfo
-
The CDF to sample from.
- centroids - Variable in class org.tribuo.clustering.kmeans.KMeansOptions
-
Number of centroids in K-Means.
- centroids - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
-
Number of clusters to infer.
- ChainHelper - Class in org.tribuo.classification.sgd.crf
-
A collection of helper methods for performing training and inference in a CRF.
- ChainHelper.ChainBPResults - Class in org.tribuo.classification.sgd.crf
-
Belief Propagation results.
- ChainHelper.ChainCliqueValues - Class in org.tribuo.classification.sgd.crf
-
Clique scores within a chain.
- ChainHelper.ChainViterbiResults - Class in org.tribuo.classification.sgd.crf
-
Viterbi output from a linear chain.
- charAt(int) - Method in class org.tribuo.util.tokens.universal.Range
- CheckerboardDataSource - Class in org.tribuo.classification.example
-
Creates a data source using a 2d checkerboard of alternating classes.
- CheckerboardDataSource(int, long, int, double, double) - Constructor for class org.tribuo.classification.example.CheckerboardDataSource
-
Creates a checkboard with the required number of squares per dimension, where each feature value lies between min and max.
- checkIsBinary(Feature) - Static method in class org.tribuo.impl.BinaryFeaturesExample
-
Checks if the supplied feature is binary, if not throw an
IllegalArgumentException
. - CHECKPOINT - Enum constant in enum class org.tribuo.interop.tensorflow.TensorFlowTrainer.TFModelFormat
-
Saves the model state inside a TensorFlow checkpoint, emits a
TensorFlowCheckpointModel
. - checkpointPath - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
-
Path to the checkpoint base directory.
- choleskyFactorization() - Method in class org.tribuo.math.la.DenseMatrix
-
Computes the Cholesky factorization of a positive definite matrix.
- Chunk - Class in org.tribuo.classification.sgd.crf
-
Chunk class used for chunk level confidence prediction in the
CRFModel
. - Chunk(int, int[]) - Constructor for class org.tribuo.classification.sgd.crf.Chunk
-
Constructs a chunk.
- Classifiable<T extends Classifiable<T>> - Interface in org.tribuo.classification
-
A tag interface for multi-class and multi-label classification tasks.
- CLASSIFICATION_TOKEN - Static variable in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
-
Default classification token name.
- ClassificationEnsembleOptions - Class in org.tribuo.classification.ensemble
-
Options for building a classification ensemble.
- ClassificationEnsembleOptions() - Constructor for class org.tribuo.classification.ensemble.ClassificationEnsembleOptions
- ClassificationEnsembleOptions.EnsembleType - Enum Class in org.tribuo.classification.ensemble
-
The type of ensemble.
- ClassificationOptions<TRAINER extends Trainer<Label>> - Interface in org.tribuo.classification
-
An
Options
that can produce a classificationTrainer
based on the provided arguments. - ClassifierChainModel - Class in org.tribuo.multilabel.baseline
-
A Classifier Chain Model.
- ClassifierChainTrainer - Class in org.tribuo.multilabel.baseline
-
A trainer for a Classifier Chain.
- ClassifierChainTrainer(Trainer<Label>, long) - Constructor for class org.tribuo.multilabel.baseline.ClassifierChainTrainer
-
Builds a classifier chain trainer using the specified member trainer and seed.
- ClassifierChainTrainer(Trainer<Label>, List<String>) - Constructor for class org.tribuo.multilabel.baseline.ClassifierChainTrainer
-
Builds a classifier chain trainer using the specified member trainer and seed.
- ClassifierEvaluation<T extends Classifiable<T>> - Interface in org.tribuo.classification.evaluation
-
Defines methods that calculate classification performance, used for both multi-class and multi-label classification.
- ClassifierTrainingNode - Class in org.tribuo.classification.dtree.impl
-
A decision tree node used at training time.
- ClassifierTrainingNode(LabelImpurity, Dataset<Label>, AbstractTrainingNode.LeafDeterminer) - Constructor for class org.tribuo.classification.dtree.impl.ClassifierTrainingNode
-
Constructor which creates the inverted file.
- className - Variable in class org.tribuo.interop.tensorflow.TensorFlowUtil.TensorTuple
-
The tensor class name.
- className - Variable in class org.tribuo.provenance.ModelProvenance
-
The name of the host class.
- clear() - Method in class org.tribuo.anomaly.MutableAnomalyInfo
- clear() - Method in class org.tribuo.classification.MutableLabelInfo
- clear() - Method in class org.tribuo.clustering.MutableClusteringInfo
- clear() - Method in class org.tribuo.impl.ListExample
-
Clears the features from this example.
- clear() - Method in class org.tribuo.multilabel.MutableMultiLabelInfo
- clear() - Method in class org.tribuo.MutableDataset
-
Clears all the examples out of this dataset, and flushes the FeatureMap, OutputInfo, and transform provenances.
- clear() - Method in class org.tribuo.MutableFeatureMap
-
Clears all the feature observations.
- clear() - Method in interface org.tribuo.MutableOutputInfo
-
Clears the OutputInfo, removing all things it's observed.
- clear() - Method in class org.tribuo.regression.MutableRegressionInfo
- clear() - Method in class org.tribuo.sequence.MutableSequenceDataset
-
Clears all the examples out of this dataset, and flushes the FeatureMap, OutputInfo, and transform provenances.
- clear() - Method in class org.tribuo.util.infotheory.impl.RowList
-
Unsupported.
- clearUsageDetails(CommandInterpreter) - Method in class org.tribuo.interop.modelcard.ModelCardCLI
-
Creates a new instance of
UsageDetailsBuilder
to allow a newUsageDetails
to be written. - clone() - Method in class org.tribuo.Feature
- clone() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer
- clone() - Method in class org.tribuo.util.tokens.impl.NonTokenizer
- clone() - Method in class org.tribuo.util.tokens.impl.ShapeTokenizer
- clone() - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
- clone() - Method in class org.tribuo.util.tokens.impl.SplitFunctionTokenizer
- clone() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer
- clone() - Method in class org.tribuo.util.tokens.impl.WhitespaceTokenizer
- clone() - Method in class org.tribuo.util.tokens.impl.wordpiece.WordpieceBasicTokenizer
- clone() - Method in class org.tribuo.util.tokens.impl.wordpiece.WordpieceTokenizer
- clone() - Method in interface org.tribuo.util.tokens.Tokenizer
-
Clones a tokenizer with it's configuration.
- clone() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
- close() - Method in class org.tribuo.data.csv.CSVIterator
- close() - Method in class org.tribuo.data.sql.SQLDataSource
- close() - Method in class org.tribuo.interop.oci.OCIModel
- close() - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
- close() - Method in class org.tribuo.interop.onnx.ONNXExternalModel
- close() - Method in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceModel
-
Close the session and graph if they exist.
- close() - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
- close() - Method in class org.tribuo.interop.tensorflow.TensorFlowModel
- close() - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
- close() - Method in class org.tribuo.interop.tensorflow.TensorMap
- close() - Method in class org.tribuo.json.JsonFileIterator
- close(CommandInterpreter) - Method in class org.tribuo.interop.modelcard.ModelCardCLI
-
Closes the command shell
- closed - Variable in class org.tribuo.interop.tensorflow.TensorFlowModel
- closeTensorCollection(Collection<Tensor>) - Static method in class org.tribuo.interop.tensorflow.TensorFlowUtil
-
Closes a collection of
Tensor
s. - CLS - Enum constant in enum class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor.OutputPooling
-
Returns the CLS embedding.
- CLS_AND_MEAN - Enum constant in enum class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor.OutputPooling
-
Takes the average of the token embeddings and the CLS token.
- CLS_OUTPUT - Static variable in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
-
Output name for the classification token output.
- clusterCounts - Variable in class org.tribuo.clustering.ClusteringInfo
-
The cluster counts for each id.
- ClusterID - Class in org.tribuo.clustering
-
A clustering id.
- ClusterID(int) - Constructor for class org.tribuo.clustering.ClusterID
-
Creates a ClusterID with the sentinel score of
Double.NaN
. - ClusterID(int, double) - Constructor for class org.tribuo.clustering.ClusterID
-
Creates a ClusterID with the specified id number and score.
- CLUSTERING - Enum constant in enum class org.tribuo.interop.oci.OCIUtil.OCIModelType
-
Clustering, maps to Tribuo's
ClusterID
. - ClusteringDataGenerator - Class in org.tribuo.clustering.example
-
Generates three example train and test datasets, used for unit testing.
- ClusteringDataGenerator() - Constructor for class org.tribuo.clustering.example.ClusteringDataGenerator
- ClusteringEvaluation - Interface in org.tribuo.clustering.evaluation
-
An
Evaluation
for clustering tasks. - ClusteringEvaluator - Class in org.tribuo.clustering.evaluation
- ClusteringEvaluator() - Constructor for class org.tribuo.clustering.evaluation.ClusteringEvaluator
- ClusteringFactory - Class in org.tribuo.clustering
-
A factory for making ClusterID related classes.
- ClusteringFactory() - Constructor for class org.tribuo.clustering.ClusteringFactory
-
ClusteringFactory is stateless and immutable, but we need to be able to construct them via the config system.
- ClusteringFactory.ClusteringFactoryProvenance - Class in org.tribuo.clustering
-
Provenance for
ClusteringFactory
. - ClusteringFactoryProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.clustering.ClusteringFactory.ClusteringFactoryProvenance
-
Rebuilds a clustering factory provenance from the marshalled form.
- ClusteringInfo - Class in org.tribuo.clustering
-
The base class for a ClusterID OutputInfo.
- ClusteringMetric - Class in org.tribuo.clustering.evaluation
-
A metric for evaluating clustering problems.
- ClusteringMetric(MetricTarget<ClusterID>, String, BiFunction<MetricTarget<ClusterID>, ClusteringMetric.Context, Double>) - Constructor for class org.tribuo.clustering.evaluation.ClusteringMetric
-
Constructs a clustering metric using the supplied parameters.
- ClusteringMetrics - Enum Class in org.tribuo.clustering.evaluation
-
Default metrics for evaluating clusterings.
- cmi(List<T1>, List<T2>, Set<List<T3>>) - Static method in class org.tribuo.util.infotheory.InformationTheory
-
Calculates the conditional mutual information between first and second conditioned on the set.
- CMIM - Class in org.tribuo.classification.fs
-
Selects features according to the Conditional Mutual Information Maximisation algorithm.
- CMIM(int, int, int) - Constructor for class org.tribuo.classification.fs.CMIM
-
Constructs a CMIM feature selector that ranks the top
k
features. - CNNExamples - Class in org.tribuo.interop.tensorflow.example
-
Static factory methods which produce Convolutional Neural Network architectures.
- coeff - Variable in class org.tribuo.regression.libsvm.TrainTest.LibSVMOptions
-
Intercept in kernel function.
- COLUMNAR - Enum constant in enum class org.tribuo.data.DataOptions.InputFormat
-
A CSV file parsed using a configured
RowProcessor
. - ColumnarDataSource<T extends Output<T>> - Class in org.tribuo.data.columnar
-
A
ConfigurableDataSource
base class which takes columnar data (e.g., csv or DB table rows) and generatesExample
s. - ColumnarDataSource() - Constructor for class org.tribuo.data.columnar.ColumnarDataSource
-
For OLCUT.
- ColumnarDataSource(OutputFactory<T>, RowProcessor<T>, boolean) - Constructor for class org.tribuo.data.columnar.ColumnarDataSource
-
Constructs a columnar data source with the specified parameters.
- ColumnarExplainer<T extends Output<T>> - Interface in org.tribuo.classification.explanations
-
An explainer for data using Tribuo's columnar data package.
- ColumnarFeature - Class in org.tribuo.data.columnar
-
A Feature with extra bookkeeping for use inside the columnar package.
- ColumnarFeature(String, double) - Constructor for class org.tribuo.data.columnar.ColumnarFeature
-
Constructs a
ColumnarFeature
from the field name. - ColumnarFeature(String, String, double) - Constructor for class org.tribuo.data.columnar.ColumnarFeature
-
Constructs a
ColumnarFeature
from the field name, column entry and value. - ColumnarFeature(String, String, String, double) - Constructor for class org.tribuo.data.columnar.ColumnarFeature
-
Constructs a
ColumnarFeature
which is the conjunction of features from two fields. - ColumnarIterator - Class in org.tribuo.data.columnar
-
An abstract class for iterators that read data in to a columnar format, usually from a file of some kind.
- ColumnarIterator() - Constructor for class org.tribuo.data.columnar.ColumnarIterator
-
Constructs a ColumnarIterator wrapped around a buffering spliterator.
- ColumnarIterator(int, int, long) - Constructor for class org.tribuo.data.columnar.ColumnarIterator
-
Constructs a ColumnarIterator wrapped around a buffering spliterator.
- ColumnarIterator.Row - Class in org.tribuo.data.columnar
-
A representation of a row of untyped data from a columnar data source.
- columnSum() - Method in class org.tribuo.math.la.DenseMatrix
-
Returns a dense vector containing each column sum.
- columnSum(int) - Method in class org.tribuo.math.la.DenseMatrix
-
Calculates the sum of the specified column.
- combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
- combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.ensemble.VotingCombiner
- combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>, float[]) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
- combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>, float[]) - Method in class org.tribuo.classification.ensemble.VotingCombiner
- combine(ImmutableOutputInfo<MultiLabel>, List<Prediction<MultiLabel>>) - Method in class org.tribuo.multilabel.ensemble.MultiLabelVotingCombiner
- combine(ImmutableOutputInfo<MultiLabel>, List<Prediction<MultiLabel>>, float[]) - Method in class org.tribuo.multilabel.ensemble.MultiLabelVotingCombiner
- combine(ImmutableOutputInfo<Regressor>, List<Prediction<Regressor>>) - Method in class org.tribuo.regression.ensemble.AveragingCombiner
- combine(ImmutableOutputInfo<Regressor>, List<Prediction<Regressor>>, float[]) - Method in class org.tribuo.regression.ensemble.AveragingCombiner
- combine(ImmutableOutputInfo<T>, List<Prediction<T>>) - Method in interface org.tribuo.ensemble.EnsembleCombiner
-
Combine the predictions.
- combine(ImmutableOutputInfo<T>, List<Prediction<T>>, float[]) - Method in interface org.tribuo.ensemble.EnsembleCombiner
-
Combine the supplied predictions.
- combiner - Variable in class org.tribuo.ensemble.BaggingTrainer
- combiner - Variable in class org.tribuo.ensemble.WeightedEnsembleModel
-
The ensemble combination function.
- COMMA - Enum constant in enum class org.tribuo.data.DataOptions.Delimiter
-
Comma separator.
- compareTo(Feature) - Method in class org.tribuo.Feature
- compareTo(MatrixIterator) - Method in interface org.tribuo.math.la.MatrixIterator
- compareTo(VectorIterator) - Method in interface org.tribuo.math.la.VectorIterator
- compareTo(InvertedFeature) - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
- compartmentID - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions
-
Compartment ID.
- compartmentID - Variable in class org.tribuo.interop.oci.OCIUtil.OCIDSConfig
-
OCI compartment ID.
- CompletelyConfigurableTrainTest - Class in org.tribuo.data
-
Build and run a predictor for a standard dataset.
- CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions - Class in org.tribuo.data
-
Command line options.
- compute(C) - Method in interface org.tribuo.evaluation.metrics.EvaluationMetric
-
Compute the result of this metric from the input context.
- compute(AnomalyMetric.Context) - Method in class org.tribuo.anomaly.evaluation.AnomalyMetric
- compute(LabelMetric.Context) - Method in class org.tribuo.classification.evaluation.LabelMetric
- compute(ClusteringMetric.Context) - Method in class org.tribuo.clustering.evaluation.ClusteringMetric
- compute(MultiLabelMetric.Context) - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric
- compute(RegressionMetric.Context) - Method in class org.tribuo.regression.evaluation.RegressionMetric
- computeDepth(int, Node<T>) - Static method in class org.tribuo.common.tree.TreeModel
-
Computes the depth of the tree.
- computeDistance(SGDVector, SGDVector) - Method in class org.tribuo.math.distance.CosineDistance
- computeDistance(SGDVector, SGDVector) - Method in interface org.tribuo.math.distance.Distance
-
Computes the distance between the two vectors.
- computeDistance(SGDVector, SGDVector) - Method in enum class org.tribuo.math.distance.DistanceType
-
Calculates the distance between two vectors.
- computeDistance(SGDVector, SGDVector) - Method in class org.tribuo.math.distance.L1Distance
- computeDistance(SGDVector, SGDVector) - Method in class org.tribuo.math.distance.L2Distance
- computeResults(C, Set<? extends EvaluationMetric<T, C>>) - Method in class org.tribuo.evaluation.AbstractEvaluator
-
Computes each metric given the context.
- computeResults(C, Set<? extends EvaluationMetric<T, C>>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
-
Computes each metric given the context.
- CONCAT - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators
-
Concatenates tensors.
- ConcentricCirclesDataSource - Class in org.tribuo.classification.example
-
A data source for two concentric circles, one per class.
- ConcentricCirclesDataSource(int, long, double, double) - Constructor for class org.tribuo.classification.example.ConcentricCirclesDataSource
-
Constructs a data source for two concentric circles, one per class.
- condaName - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions
-
OCI DS conda environment name.
- condaName - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelArtifactConfig
-
The Conda environment name.
- condaPath - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions
-
OCI DS conda environment path in object storage.
- condaPath - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelArtifactConfig
-
The Conda environment path in object storage.
- conditionalEntropy(List<T1>, List<T2>) - Static method in class org.tribuo.util.infotheory.InformationTheory
-
Calculates the discrete Shannon conditional entropy of two arrays, using histogram probability estimators.
- conditionalMI(List<T1>, List<T2>, List<T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory
-
Calculates the discrete Shannon conditional mutual information, using histogram probability estimators.
- conditionalMI(List<T1>, List<T2>, List<T3>, List<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory
-
Calculates the discrete weighted conditional mutual information, using histogram probability estimators.
- conditionalMI(TripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory
-
Calculates the discrete Shannon conditional mutual information, using histogram probability estimators.
- conditionalMI(TripleDistribution<T1, T2, T3>, Map<?, Double>, WeightedInformationTheory.VariableSelector) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory
-
Calculates the discrete weighted conditional mutual information, using histogram probability estimators.
- conditionalMI(WeightedTripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory
-
Calculates the discrete weighted conditional mutual information, using histogram probability estimators.
- conditionalMIFlipped(TripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory
-
Calculates the discrete Shannon conditional mutual information, using histogram probability estimators.
- ConfidencePredictingSequenceModel - Class in org.tribuo.classification.sequence
-
A Sequence model which can provide confidence predictions for subsequence predictions.
- ConfidencePredictingSequenceModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>) - Constructor for class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel
-
Constructs a ConfidencePredictingSequenceModel with the supplied parameters.
- ConfidencePredictingSequenceModel.Subsequence - Class in org.tribuo.classification.sequence
-
A range class used to define a subsequence of a SequenceExample.
- ConfigurableDataSource<T extends Output<T>> - Interface in org.tribuo
-
It's a
DataSource
that's alsoConfigurable
. - ConfigurableTestOptions() - Constructor for class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
- ConfigurableTrainTest - Class in org.tribuo.classification.experiments
-
Build and run a classifier for a standard dataset.
- ConfigurableTrainTest - Class in org.tribuo.data
-
Build and run a predictor for a standard dataset.
- ConfigurableTrainTest() - Constructor for class org.tribuo.classification.experiments.ConfigurableTrainTest
- ConfigurableTrainTest.ConfigurableTrainTestOptions - Class in org.tribuo.classification.experiments
-
Command line options.
- ConfigurableTrainTest.ConfigurableTrainTestOptions - Class in org.tribuo.data
-
Command line options.
- ConfigurableTrainTestOptions() - Constructor for class org.tribuo.classification.experiments.ConfigurableTrainTest.ConfigurableTrainTestOptions
- ConfigurableTrainTestOptions() - Constructor for class org.tribuo.data.CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions
- ConfigurableTrainTestOptions() - Constructor for class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions
- configured - Variable in class org.tribuo.data.columnar.RowProcessor
-
Has this row processor been configured?
- ConfiguredDataSourceProvenance - Interface in org.tribuo.provenance
-
A tag interface for configurable data source provenance.
- confusion(Label, Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
- confusion(Label, Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
-
Note: confusion is not stored in the underlying map, so it won't show up in aggregation.
- confusion(MultiLabel, MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
- confusion(MultiLabel, MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
- confusion(T, T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
-
Returns the number of times label
truth
was predicted as labelpredicted
. - confusion(T, T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix
-
The number of times the supplied predicted label was returned for the supplied true class.
- ConfusionMatrix<T extends Classifiable<T>> - Interface in org.tribuo.classification.evaluation
-
A confusion matrix for
Classifiable
s. - ConfusionMetrics - Class in org.tribuo.classification.evaluation
-
Static functions for computing classification metrics based on a
ConfusionMatrix
. - confusionString() - Method in interface org.tribuo.anomaly.evaluation.AnomalyEvaluation
-
Returns a confusion matrix formatted String for display.
- CONJUNCTION - Static variable in class org.tribuo.data.columnar.ColumnarFeature
-
The string used as the field name of conjunction features.
- connString - Variable in class org.tribuo.data.sql.SQLToCSV.SQLToCSVOptions
-
Connection string to the SQL database
- considerations(List<String>) - Method in class org.tribuo.interop.modelcard.UsageDetailsBuilder
-
Sets the considerations of the model for which an instance of UsageDetails will be built.
- constant(String, float) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates a float scalar constant for this ONNXContext.
- constant(String, long) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates a long scalar constant for this ONNXContext.
- CONSTANT - Enum constant in enum class org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType
-
Returns the supplied label for all inputs.
- CONSTANT - Enum constant in enum class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyType
-
Returns the specified constant value.
- CONSTANT_OF_SHAPE - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators
-
Makes a constant of the size of the supplied shape containing the
value
. - CONSTANTSGD - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
-
SGD with a constant learning rate.
- CONSTRAINED_BP - Enum constant in enum class org.tribuo.classification.sgd.crf.CRFModel.ConfidenceType
-
Constrained Belief Propagation from "Confidence Estimation for Information Extraction" Culotta and McCallum 2004.
- constrainedBeliefPropagation(ChainHelper.ChainCliqueValues, int[]) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper
-
Runs constrained belief propagation on a linear chain CRF.
- constructFromLists(List<T1>, List<T2>) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution
-
Generates the counts for two vectors.
- constructFromLists(List<T1>, List<T2>, List<Double>) - Static method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution
-
Generates the counts for two vectors.
- constructFromLists(List<T1>, List<T2>, List<T3>) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution
-
Constructs a TripleDistribution from three lists of the same length.
- constructFromLists(List<T1>, List<T2>, List<T3>, List<Double>) - Static method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
-
Constructs a WeightedTripleDistribution from three lists of the same length and a list of weights of the same length.
- constructFromMap(Map<CachedPair<T1, T2>, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution
-
Constructs a distribution from a joint count.
- constructFromMap(Map<CachedPair<T1, T2>, MutableLong>, int, int) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution
-
Constructs a distribution from a joint count.
- constructFromMap(Map<CachedPair<T1, T2>, MutableLong>, Map<T1, MutableLong>, Map<T2, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution
-
Constructs a joint distribution from the counts.
- constructFromMap(Map<CachedPair<T1, T2>, WeightCountTuple>) - Static method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution
-
Generates a WeightedPairDistribution by generating the marginal distributions for the first and second elements.
- constructFromMap(Map<CachedTriple<T1, T2, T3>, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution
-
Constructs a TripleDistribution by marginalising the supplied joint distribution.
- constructFromMap(Map<CachedTriple<T1, T2, T3>, MutableLong>, int, int, int, int, int, int) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution
-
Constructs a TripleDistribution by marginalising the supplied joint distribution.
- constructFromMap(Map<CachedTriple<T1, T2, T3>, MutableLong>, Map<CachedPair<T1, T2>, MutableLong>, Map<CachedPair<T1, T3>, MutableLong>, Map<CachedPair<T2, T3>, MutableLong>, Map<T1, MutableLong>, Map<T2, MutableLong>, Map<T3, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution
-
Constructs a TripleDistribution by marginalising the supplied joint distribution.
- constructFromMap(Map<CachedTriple<T1, T2, T3>, WeightCountTuple>) - Static method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
-
Constructs a WeightedTripleDistribution by marginalising the supplied joint distribution.
- constructInfoForExternalModel(Map<Event, Integer>) - Method in class org.tribuo.anomaly.AnomalyFactory
- constructInfoForExternalModel(Map<Label, Integer>) - Method in class org.tribuo.classification.LabelFactory
- constructInfoForExternalModel(Map<ClusterID, Integer>) - Method in class org.tribuo.clustering.ClusteringFactory
-
Unlike the other info types, clustering directly uses the integer IDs as the stored value, so this mapping discards the cluster IDs and just uses the supplied integers.
- constructInfoForExternalModel(Map<MultiLabel, Integer>) - Method in class org.tribuo.multilabel.MultiLabelFactory
- constructInfoForExternalModel(Map<Regressor, Integer>) - Method in class org.tribuo.regression.RegressionFactory
- constructInfoForExternalModel(Map<T, Integer>) - Method in interface org.tribuo.OutputFactory
-
Creates an
ImmutableOutputInfo
from the supplied mapping. - contains(int) - Method in class org.tribuo.impl.IndexedArrayExample
-
Does this example contain a feature with id i.
- contains(Object) - Method in class org.tribuo.util.infotheory.impl.RowList
- contains(String) - Method in class org.tribuo.multilabel.MultiLabel
-
Does this MultiLabel contain this string?
- contains(Label) - Method in class org.tribuo.multilabel.MultiLabel
-
Does this MultiLabel contain this Label?
- containsAll(Collection<?>) - Method in class org.tribuo.util.infotheory.impl.RowList
- containsMetadata(String) - Method in class org.tribuo.Example
-
Test if the metadata contains the supplied key.
- context - Variable in class org.tribuo.util.onnx.ONNXRef
-
The ONNX context.
- Context(Model<Label>, List<Prediction<Label>>) - Constructor for class org.tribuo.classification.evaluation.LabelMetric.Context
-
Constructs a context and compute the confusion matrix using the specified model and predictions.
- Context(SequenceModel<Label>, List<Prediction<Label>>) - Constructor for class org.tribuo.classification.evaluation.LabelMetric.Context
-
Constructs a context and compute the confusion matrix using the specified model and predictions.
- convert(byte) - Static method in enum class org.tribuo.datasource.IDXDataSource.IDXType
-
Converts the byte into the enum.
- convert(List<? extends Example<?>>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.DenseFeatureConverter
- convert(List<? extends Example<?>>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.FeatureConverter
- convert(List<? extends Example<?>>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.ImageConverter
-
Transform implicitly pads unseen values with zero.
- convert(List<? extends SGDVector>) - Method in class org.tribuo.interop.tensorflow.DenseFeatureConverter
- convert(List<? extends SGDVector>) - Method in interface org.tribuo.interop.tensorflow.FeatureConverter
- convert(List<? extends SGDVector>) - Method in class org.tribuo.interop.tensorflow.ImageConverter
- convert(Example<?>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.DenseFeatureConverter
- convert(Example<?>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.FeatureConverter
- convert(Example<?>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.ImageConverter
-
Transform implicitly pads unseen values with zero.
- convert(SGDVector) - Method in class org.tribuo.interop.tensorflow.DenseFeatureConverter
- convert(SGDVector) - Method in interface org.tribuo.interop.tensorflow.FeatureConverter
- convert(SGDVector) - Method in class org.tribuo.interop.tensorflow.ImageConverter
- convert(SequenceExample<Label>, ImmutableFeatureMap, ImmutableOutputInfo<Label>) - Static method in class org.tribuo.classification.sgd.crf.CRFModel
-
Deprecated.
- convert(SequenceExample<T>, ImmutableFeatureMap) - Static method in class org.tribuo.classification.sgd.crf.CRFModel
-
Deprecated.As it's replaced with
CRFModel.convertToVector(org.tribuo.sequence.SequenceExample<T>, org.tribuo.ImmutableFeatureMap)
which is more flexible. - convertBatchOutput(ImmutableOutputInfo<Label>, List<float[][]>, int[], Example<Label>[]) - Method in class org.tribuo.classification.xgboost.XGBoostClassificationConverter
- convertBatchOutput(ImmutableOutputInfo<Regressor>, List<float[][]>, int[], Example<Regressor>[]) - Method in class org.tribuo.regression.xgboost.XGBoostRegressionConverter
- convertBatchOutput(ImmutableOutputInfo<T>, List<float[][]>, int[], Example<T>[]) - Method in interface org.tribuo.common.xgboost.XGBoostOutputConverter
-
Converts a list of float arrays from XGBoost Boosters into a Tribuo
Prediction
. - convertDataset(Dataset<T>) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
-
Converts a dataset into a DMatrix.
- convertDataset(Dataset<T>, Function<T, Float>) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
-
Converts a dataset into a DMatrix.
- convertExample(Example<T>, ImmutableFeatureMap) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
-
Converts an example into a DMatrix.
- convertExample(Example<T>, ImmutableFeatureMap, Function<T, Float>) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
-
Converts an example into a DMatrix.
- convertExamples(Iterable<Example<T>>, ImmutableFeatureMap) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
-
Converts an iterable of examples into a DMatrix.
- convertExamples(Iterable<Example<T>>, ImmutableFeatureMap, Function<T, Float>) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
-
Converts an iterable of examples into a DMatrix.
- convertFeatures(SparseVector) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel
- convertFeatures(SparseVector) - Method in class org.tribuo.interop.ExternalModel
-
Converts from a SparseVector using the external model's indices into the ingestion format for the external model.
- convertFeatures(SparseVector) - Method in class org.tribuo.interop.oci.OCIModel
- convertFeatures(SparseVector) - Method in class org.tribuo.interop.onnx.ONNXExternalModel
- convertFeatures(SparseVector) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
- convertFeatures(SparseVector) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
- convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel
- convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.interop.ExternalModel
-
Converts from a list of SparseVector using the external model's indices into the ingestion format for the external model.
- convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.interop.oci.OCIModel
- convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.interop.onnx.ONNXExternalModel
- convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
- convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
- convertOutput(float[][], int[], List<Example<T>>) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel
- convertOutput(float[][], int, Example<T>) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel
- convertOutput(List<OnnxValue>, int[], List<Example<T>>) - Method in class org.tribuo.interop.onnx.ONNXExternalModel
-
Converts a tensor into a prediction.
- convertOutput(List<OnnxValue>, int, Example<T>) - Method in class org.tribuo.interop.onnx.ONNXExternalModel
-
Converts a tensor into a prediction.
- convertOutput(Tensor, int[], List<Example<T>>) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
-
Converts a tensor into a prediction.
- convertOutput(Tensor, int, Example<T>) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
-
Converts a tensor into a prediction.
- convertOutput(ImmutableOutputInfo<Label>, List<float[]>, int, Example<Label>) - Method in class org.tribuo.classification.xgboost.XGBoostClassificationConverter
- convertOutput(ImmutableOutputInfo<Regressor>, List<float[]>, int, Example<Regressor>) - Method in class org.tribuo.regression.xgboost.XGBoostRegressionConverter
- convertOutput(ImmutableOutputInfo<T>, List<float[]>, int, Example<T>) - Method in interface org.tribuo.common.xgboost.XGBoostOutputConverter
-
Converts a list of float arrays from XGBoost Boosters into a Tribuo
Prediction
. - convertOutput(TensorMap, int[], List<Example<T>>) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
-
Converts a tensor into a prediction.
- convertOutput(TensorMap, int, Example<T>) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
-
Converts a tensor into a prediction.
- convertOutput(DenseMatrix, int[], List<Example<Label>>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.oci.OCILabelConverter
- convertOutput(DenseMatrix, int[], List<Example<MultiLabel>>, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.oci.OCIMultiLabelConverter
- convertOutput(DenseMatrix, int[], List<Example<Regressor>>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.oci.OCIRegressorConverter
- convertOutput(DenseMatrix, int[], List<Example<T>>) - Method in class org.tribuo.interop.oci.OCIModel
- convertOutput(DenseMatrix, int[], List<Example<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.oci.OCIOutputConverter
-
Converts a dense matrix into a list of predictions of the appropriate type.
- convertOutput(DenseMatrix, int, Example<T>) - Method in class org.tribuo.interop.oci.OCIModel
- convertOutput(DenseVector, int, Example<Label>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.oci.OCILabelConverter
- convertOutput(DenseVector, int, Example<MultiLabel>, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.oci.OCIMultiLabelConverter
- convertOutput(DenseVector, int, Example<Regressor>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.oci.OCIRegressorConverter
- convertOutput(DenseVector, int, Example<T>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.oci.OCIOutputConverter
-
Converts a dense vector into a single prediction of the appropriate type.
- convertOutput(V, int[], List<Example<T>>) - Method in class org.tribuo.interop.ExternalModel
-
Converts the output of the external model into a list of
Prediction
s. - convertOutput(V, int, Example<T>) - Method in class org.tribuo.interop.ExternalModel
-
Converts the output of the external model into a
Prediction
. - convertSingleExample(Example<T>, ImmutableFeatureMap, ArrayList<Float>, ArrayList<Integer>, ArrayList<Long>, long) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
-
Writes out the features from an example into the three supplied
ArrayList
s. - convertSparseVector(SparseVector) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
-
Used when predicting with an externally trained XGBoost model.
- convertSparseVectors(List<SparseVector>) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
-
Used when predicting with an externally trained XGBoost model.
- convertToBatchOutput(Tensor, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
- convertToBatchOutput(Tensor, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
- convertToBatchOutput(Tensor, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
- convertToBatchOutput(Tensor, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
-
Converts a
Tensor
containing multiple outputs into a list ofOutput
s. - convertToBatchPrediction(Tensor, ImmutableOutputInfo<Label>, int[], List<Example<Label>>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
- convertToBatchPrediction(Tensor, ImmutableOutputInfo<MultiLabel>, int[], List<Example<MultiLabel>>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
- convertToBatchPrediction(Tensor, ImmutableOutputInfo<Regressor>, int[], List<Example<Regressor>>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
- convertToBatchPrediction(Tensor, ImmutableOutputInfo<T>, int[], List<Example<T>>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
-
Converts a
Tensor
containing multiple outputs into a list ofPrediction
s. - convertToCheckpointModel(String, String) - Method in class org.tribuo.interop.tensorflow.TensorFlowNativeModel
-
Creates a
TensorFlowCheckpointModel
version of this model. - convertToDense() - Method in class org.tribuo.math.optimisers.util.ShrinkingMatrix
- convertToDense() - Method in interface org.tribuo.math.optimisers.util.ShrinkingTensor
-
Converts the tensor into a dense tensor.
- convertToDense() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
- convertToDenseVector(ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.multilabel.MultiLabel
-
Converts this MultiLabel into a DenseVector using the indices from the output info.
- convertToMap(ObjectNode) - Static method in class org.tribuo.json.JsonUtil
-
Converts a Json node into a Map from String to String for use in downstream processing by
RowProcessor
. - convertToNativeModel() - Method in class org.tribuo.interop.tensorflow.TensorFlowCheckpointModel
-
Creates a
TensorFlowNativeModel
version of this model. - convertToOutput(Tensor, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
- convertToOutput(Tensor, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
- convertToOutput(Tensor, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
- convertToOutput(Tensor, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
-
Converts a
Tensor
into the specified output type. - convertToPrediction(Tensor, ImmutableOutputInfo<Label>, int, Example<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
- convertToPrediction(Tensor, ImmutableOutputInfo<MultiLabel>, int, Example<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
- convertToPrediction(Tensor, ImmutableOutputInfo<Regressor>, int, Example<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
- convertToPrediction(Tensor, ImmutableOutputInfo<T>, int, Example<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
-
Converts a
Tensor
into aPrediction
. - convertToSparseVector(ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.multilabel.MultiLabel
-
Converts this MultiLabel into a SparseVector using the indices from the output info.
- convertToTensor(List<Example<Label>>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
- convertToTensor(List<Example<MultiLabel>>, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
- convertToTensor(List<Example<Regressor>>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
- convertToTensor(List<Example<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
-
Converts a list of
Example
into aTensor
representing all the outputs in the list. - convertToTensor(Label, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
- convertToTensor(MultiLabel, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
- convertToTensor(Regressor, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
- convertToTensor(T, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
-
Converts an
Output
into aTensor
representing it's output. - convertToVector(SequenceExample<Label>, ImmutableFeatureMap, ImmutableOutputInfo<Label>) - Static method in class org.tribuo.classification.sgd.crf.CRFModel
-
Converts a
SequenceExample
into an array ofSGDVector
s and labels suitable for CRF prediction. - convertToVector(SequenceExample<T>, ImmutableFeatureMap) - Static method in class org.tribuo.classification.sgd.crf.CRFModel
-
Converts a
SequenceExample
into an array ofSGDVector
s suitable for CRF prediction. - convertTree() - Method in class org.tribuo.classification.dtree.impl.ClassifierTrainingNode
- convertTree() - Method in class org.tribuo.common.tree.AbstractTrainingNode
-
Converts a tree from a training representation to the final inference time representation.
- convertTree() - Method in class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode
- convertTree() - Method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode
- copy() - Method in class org.tribuo.anomaly.AnomalyInfo
- copy() - Method in class org.tribuo.anomaly.Event
- copy() - Method in class org.tribuo.anomaly.ImmutableAnomalyInfo
- copy() - Method in class org.tribuo.anomaly.MutableAnomalyInfo
- copy() - Method in class org.tribuo.CategoricalIDInfo
- copy() - Method in class org.tribuo.CategoricalInfo
- copy() - Method in class org.tribuo.classification.ImmutableLabelInfo
- copy() - Method in class org.tribuo.classification.Label
- copy() - Method in class org.tribuo.classification.LabelInfo
- copy() - Method in class org.tribuo.classification.MutableLabelInfo
- copy() - Method in class org.tribuo.clustering.ClusterID
- copy() - Method in class org.tribuo.clustering.ClusteringInfo
- copy() - Method in class org.tribuo.clustering.hdbscan.HdbscanTrainer.ClusterExemplar
-
Copies this cluster exemplar.
- copy() - Method in class org.tribuo.clustering.ImmutableClusteringInfo
- copy() - Method in class org.tribuo.clustering.MutableClusteringInfo
- copy() - Method in class org.tribuo.common.sgd.FMParameters
- copy() - Method in class org.tribuo.common.tree.AbstractTrainingNode
- copy() - Method in class org.tribuo.common.tree.impl.IntArrayContainer
-
Returns a copy of the elements in use.
- copy() - Method in class org.tribuo.common.tree.LeafNode
- copy() - Method in interface org.tribuo.common.tree.Node
-
Copies the node and it's children.
- copy() - Method in class org.tribuo.common.tree.SplitNode
- copy() - Method in class org.tribuo.data.columnar.RowProcessor
-
Deprecated.In a future release this API will change, in the meantime this is the correct way to get a row processor with clean state.
When using regexMappingProcessors, RowProcessor is stateful in a way that can sometimes make it fail the second time it is used. Concretely:
RowProcessor rp; Dataset ds1 = new MutableDataset(new CSVDataSource(csvfile1, rp)); Dataset ds2 = new MutableDataset(new CSVDataSource(csvfile2, rp)); // this may fail due to state in rp
This method returns a RowProcessor with clean state and the same configuration as this row processor. - copy() - Method in class org.tribuo.Example
-
Returns a deep copy of this Example.
- copy() - Method in class org.tribuo.impl.ArrayExample
- copy() - Method in class org.tribuo.impl.BinaryFeaturesExample
- copy() - Method in class org.tribuo.impl.IndexedArrayExample
- copy() - Method in class org.tribuo.impl.ListExample
- copy() - Method in interface org.tribuo.math.FeedForwardParameters
-
Returns a copy of the parameters.
- copy() - Method in class org.tribuo.math.la.DenseMatrix
-
Copies the matrix.
- copy() - Method in class org.tribuo.math.la.DenseSparseMatrix
- copy() - Method in class org.tribuo.math.la.DenseVector
- copy() - Method in interface org.tribuo.math.la.Matrix
-
Copies the matrix.
- copy() - Method in interface org.tribuo.math.la.SGDVector
-
Returns a deep copy of this vector.
- copy() - Method in class org.tribuo.math.la.SparseVector
- copy() - Method in interface org.tribuo.math.la.Tensor
-
Returns a copy of this Tensor.
- copy() - Method in class org.tribuo.math.LinearParameters
- copy() - Method in class org.tribuo.math.optimisers.AdaDelta
- copy() - Method in class org.tribuo.math.optimisers.AdaGrad
- copy() - Method in class org.tribuo.math.optimisers.AdaGradRDA
- copy() - Method in class org.tribuo.math.optimisers.Adam
- copy() - Method in class org.tribuo.math.optimisers.ParameterAveraging
- copy() - Method in class org.tribuo.math.optimisers.Pegasos
- copy() - Method in class org.tribuo.math.optimisers.RMSProp
- copy() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
- copy() - Method in interface org.tribuo.math.StochasticGradientOptimiser
-
Copies a gradient optimiser with it's configuration.
- copy() - Method in class org.tribuo.Model
-
Copies a model, returning a deep copy of any mutable state, and a shallow copy otherwise.
- copy() - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
- copy() - Method in class org.tribuo.multilabel.MultiLabel
- copy() - Method in class org.tribuo.multilabel.MultiLabelInfo
- copy() - Method in class org.tribuo.multilabel.MutableMultiLabelInfo
- copy() - Method in interface org.tribuo.Output
-
Deep copy of the output up to its immutable state.
- copy() - Method in interface org.tribuo.OutputInfo
-
Generates a copy of this OutputInfo, including it's mutability.
- copy() - Method in class org.tribuo.RealIDInfo
- copy() - Method in class org.tribuo.RealInfo
- copy() - Method in class org.tribuo.regression.ImmutableRegressionInfo
- copy() - Method in class org.tribuo.regression.MutableRegressionInfo
- copy() - Method in class org.tribuo.regression.RegressionInfo
- copy() - Method in class org.tribuo.regression.Regressor
- copy() - Method in class org.tribuo.regression.Regressor.DimensionTuple
- copy() - Method in class org.tribuo.sequence.SequenceExample
-
Returns a deep copy of this SequenceExample.
- copy() - Method in class org.tribuo.SparseModel
- copy() - Method in interface org.tribuo.VariableInfo
-
Returns a copy of this variable info.
- copy(String) - Method in interface org.tribuo.data.columnar.FieldProcessor
-
Returns a copy of this FieldProcessor bound to the supplied newFieldName.
- copy(String) - Method in class org.tribuo.data.columnar.processors.field.DateFieldProcessor
- copy(String) - Method in class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
- copy(String) - Method in class org.tribuo.data.columnar.processors.field.IdentityProcessor
- copy(String) - Method in class org.tribuo.data.columnar.processors.field.RegexFieldProcessor
- copy(String) - Method in class org.tribuo.data.columnar.processors.field.TextFieldProcessor
-
Note: the copy shares the text pipeline with the original.
- copy(String, EnsembleModelProvenance, List<Model<T>>) - Method in class org.tribuo.ensemble.EnsembleModel
-
Copies this ensemble model.
- copy(String, EnsembleModelProvenance, List<Model<T>>) - Method in class org.tribuo.ensemble.WeightedEnsembleModel
- copy(String, ModelProvenance) - Method in class org.tribuo.anomaly.liblinear.LibLinearAnomalyModel
- copy(String, ModelProvenance) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyModel
- copy(String, ModelProvenance) - Method in class org.tribuo.classification.baseline.DummyClassifierModel
- copy(String, ModelProvenance) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationModel
- copy(String, ModelProvenance) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationModel
- copy(String, ModelProvenance) - Method in class org.tribuo.classification.mnb.MultinomialNaiveBayesModel
- copy(String, ModelProvenance) - Method in class org.tribuo.classification.sgd.fm.FMClassificationModel
- copy(String, ModelProvenance) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel
- copy(String, ModelProvenance) - Method in class org.tribuo.classification.sgd.linear.LinearSGDModel
- copy(String, ModelProvenance) - Method in class org.tribuo.clustering.hdbscan.HdbscanModel
- copy(String, ModelProvenance) - Method in class org.tribuo.clustering.kmeans.KMeansModel
- copy(String, ModelProvenance) - Method in class org.tribuo.common.nearest.KNNModel
- copy(String, ModelProvenance) - Method in class org.tribuo.common.tree.TreeModel
- copy(String, ModelProvenance) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel
- copy(String, ModelProvenance) - Method in class org.tribuo.common.xgboost.XGBoostModel
- copy(String, ModelProvenance) - Method in class org.tribuo.ensemble.EnsembleModel
- copy(String, ModelProvenance) - Method in class org.tribuo.interop.oci.OCIModel
- copy(String, ModelProvenance) - Method in class org.tribuo.interop.onnx.ONNXExternalModel
- copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowCheckpointModel
- copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
- copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowNativeModel
- copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
- copy(String, ModelProvenance) - Method in class org.tribuo.Model
-
Copies a model, replacing its provenance and name with the supplied values.
- copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.baseline.ClassifierChainModel
- copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.baseline.IndependentMultiLabelModel
- copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.sgd.fm.FMMultiLabelModel
- copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.sgd.linear.LinearSGDModel
- copy(String, ModelProvenance) - Method in class org.tribuo.regression.baseline.DummyRegressionModel
- copy(String, ModelProvenance) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionModel
- copy(String, ModelProvenance) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionModel
- copy(String, ModelProvenance) - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
- copy(String, ModelProvenance) - Method in class org.tribuo.regression.sgd.fm.FMRegressionModel
- copy(String, ModelProvenance) - Method in class org.tribuo.regression.sgd.linear.LinearSGDModel
- copy(String, ModelProvenance) - Method in class org.tribuo.regression.slm.SparseLinearModel
- copy(String, ModelProvenance) - Method in class org.tribuo.transform.TransformedModel
- copyDataset(Dataset<T>) - Static method in class org.tribuo.ImmutableDataset
-
Creates an immutable deep copy of the supplied dataset.
- copyDataset(Dataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.ImmutableDataset
-
Creates an immutable deep copy of the supplied dataset, using a different feature and output map.
- copyDataset(Dataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>, Merger) - Static method in class org.tribuo.ImmutableDataset
-
Creates an immutable deep copy of the supplied dataset.
- copyDataset(SequenceDataset<T>) - Static method in class org.tribuo.sequence.ImmutableSequenceDataset
-
Creates an immutable deep copy of the supplied dataset.
- copyDataset(SequenceDataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.sequence.ImmutableSequenceDataset
-
Creates an immutable deep copy of the supplied dataset, using a different feature and output map.
- copyDataset(SequenceDataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>, Merger) - Static method in class org.tribuo.sequence.ImmutableSequenceDataset
-
Creates an immutable deep copy of the supplied dataset.
- copyModel(Model) - Static method in class org.tribuo.common.liblinear.LibLinearModel
-
Copies the model by writing it out to a String and loading it back in.
- copyModel(svm_model) - Static method in class org.tribuo.common.libsvm.LibSVMModel
-
Copies an svm_model, as it does not provide a copy method.
- copyParameters(svm_parameter) - Static method in class org.tribuo.common.libsvm.SVMParameters
-
Deep copy of the svm_parameters including the arrays.
- copyParams(Map<String, ?>) - Method in class org.tribuo.common.xgboost.XGBoostTrainer
-
Returns a copy of the supplied parameter map which has the appropriate type for passing to XGBoost.train.
- copyResourceToTmp(String) - Static method in class org.tribuo.tests.Resources
-
Copies a classpath resource to a temporary file.
- copyValues(int) - Method in class org.tribuo.impl.ArrayExample
-
Returns a copy of the feature values array at the specific size.
- CoreTokenizerOptions - Class in org.tribuo.util.tokens.options
-
CLI Options for all the tokenizers in the core package.
- CoreTokenizerOptions() - Constructor for class org.tribuo.util.tokens.options.CoreTokenizerOptions
- CoreTokenizerOptions.CoreTokenizerType - Enum Class in org.tribuo.util.tokens.options
-
Tokenizer type.
- coreTokenizerType - Variable in class org.tribuo.util.tokens.options.CoreTokenizerOptions
-
Type of tokenizer
- CORRELATED - Enum constant in enum class org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType
-
Correlated data.
- COSINE - Enum constant in enum class org.tribuo.clustering.hdbscan.HdbscanTrainer.Distance
-
Deprecated.Cosine similarity as a distance measure.
- COSINE - Enum constant in enum class org.tribuo.clustering.kmeans.KMeansTrainer.Distance
-
Deprecated.Cosine similarity as a distance measure.
- COSINE - Enum constant in enum class org.tribuo.common.nearest.KNNTrainer.Distance
-
Deprecated.Cosine similarity used as a distance measure.
- COSINE - Enum constant in enum class org.tribuo.math.distance.DistanceType
-
Cosine similarity used as a distance measure.
- cosineDistance(SGDVector) - Method in interface org.tribuo.math.la.SGDVector
-
Calculates the cosine distance of two vectors.
- CosineDistance - Class in org.tribuo.math.distance
-
Cosine similarity used as a distance measure.
- CosineDistance() - Constructor for class org.tribuo.math.distance.CosineDistance
-
Constructs a Cosine distance function.
- cosineSimilarity(SGDVector) - Method in interface org.tribuo.math.la.SGDVector
-
Calculates the cosine similarity of two vectors.
- cost - Variable in class org.tribuo.common.liblinear.LibLinearTrainer
- cost - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
-
Cost penalty for SVM.
- count - Variable in class org.tribuo.SkeletalVariableInfo
-
How often the feature occurs in the dataset.
- count - Variable in class org.tribuo.util.infotheory.impl.PairDistribution
-
The number of samples this distribution has seen.
- count - Variable in class org.tribuo.util.infotheory.impl.TripleDistribution
-
The number of samples in this distribution.
- count - Variable in class org.tribuo.util.infotheory.impl.WeightCountTuple
-
The current count.
- count - Variable in class org.tribuo.util.infotheory.impl.WeightedPairDistribution
-
The sample count.
- count - Variable in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
-
The sample count.
- countMap - Variable in class org.tribuo.regression.RegressionInfo
-
The occurrence count per dimension.
- countNodes(Node<T>) - Method in class org.tribuo.common.tree.TreeModel
-
Counts the number of nodes in the tree rooted at the supplied node, including that node.
- CREATE_AND_DEPLOY - Enum constant in enum class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions.Mode
-
Create a Model artifact, upload it to OCI and create a Model Deployment.
- createBootstrapView(Dataset<T>, int, long) - Static method in class org.tribuo.dataset.DatasetView
-
Generates a DatasetView bootstrapped from the supplied Dataset.
- createBootstrapView(Dataset<T>, int, long, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.dataset.DatasetView
-
Generates a DatasetView bootstrapped from the supplied Dataset.
- createConstantTrainer(double) - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer
-
Creates a trainer which create models which return a fixed value.
- createConstantTrainer(String) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer
-
Creates a trainer which creates models which return a fixed label.
- createContext(Model<Event>, List<Prediction<Event>>) - Method in class org.tribuo.anomaly.evaluation.AnomalyEvaluator
- createContext(Model<Event>, List<Prediction<Event>>) - Method in class org.tribuo.anomaly.evaluation.AnomalyMetric
- createContext(Model<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.evaluation.LabelEvaluator
- createContext(Model<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.evaluation.LabelMetric
- createContext(Model<ClusterID>, List<Prediction<ClusterID>>) - Method in class org.tribuo.clustering.evaluation.ClusteringEvaluator
- createContext(Model<ClusterID>, List<Prediction<ClusterID>>) - Method in class org.tribuo.clustering.evaluation.ClusteringMetric
- createContext(Model<MultiLabel>, List<Prediction<MultiLabel>>) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluator
- createContext(Model<MultiLabel>, List<Prediction<MultiLabel>>) - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric
- createContext(Model<Regressor>, List<Prediction<Regressor>>) - Method in class org.tribuo.regression.evaluation.RegressionEvaluator
- createContext(Model<Regressor>, List<Prediction<Regressor>>) - Method in class org.tribuo.regression.evaluation.RegressionMetric
- createContext(Model<T>, List<Prediction<T>>) - Method in class org.tribuo.evaluation.AbstractEvaluator
-
Create the context needed for evaluation.
- createContext(Model<T>, List<Prediction<T>>) - Method in interface org.tribuo.evaluation.metrics.EvaluationMetric
-
Creates the context this metric uses to compute it's value.
- createContext(Model<T>, Dataset<T>) - Method in interface org.tribuo.evaluation.metrics.EvaluationMetric
-
Creates the metric context used to compute this metric's value, generating
Prediction
s for eachExample
in the supplied dataset. - createContext(SequenceModel<Label>, List<List<Prediction<Label>>>) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator
- createContext(SequenceModel<T>, List<List<Prediction<T>>>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
-
Create the context needed for evaluation.
- createDataCarrier() - Method in class org.tribuo.Model
-
Constructs the data carrier for serialization.
- createDataCarrier() - Method in class org.tribuo.sequence.SequenceModel
-
Constructs the data carrier for serialization.
- createDataCarrier(FeatureMap, OutputInfo<T>) - Method in class org.tribuo.Dataset
-
Constructs the data carrier for serialization.
- createDataCarrier(FeatureMap, OutputInfo<T>) - Method in class org.tribuo.sequence.SequenceDataset
-
Constructs the data carrier for serialization.
- createDataCarrier(FeatureMap, OutputInfo<T>, List<ObjectProvenance>) - Method in class org.tribuo.Dataset
-
Constructs the data carrier for serialization.
- createDeepCopy(Dataset<T>) - Static method in class org.tribuo.MutableDataset
-
Creates a deep copy of the supplied
Dataset
which is mutable. - createDenseMatrix(double[][]) - Static method in class org.tribuo.math.la.DenseMatrix
-
Defensively copies the values before construction.
- createDenseMatrix(SGDVector[]) - Static method in class org.tribuo.math.la.DenseMatrix
-
Constructs a new DenseMatrix copying the values from the supplied vectors.
- createDenseVector(double[]) - Static method in class org.tribuo.math.la.DenseVector
-
Defensively copies the values before construction.
- createDenseVector(Example<T>, ImmutableFeatureMap, boolean) - Static method in class org.tribuo.math.la.DenseVector
-
Builds a
DenseVector
from anExample
. - createDiagonal(SGDVector) - Static method in class org.tribuo.math.la.DenseSparseMatrix
-
Creates a diagonal matrix using the supplied values.
- createEnsembleFromExistingModels(String, List<Model<T>>, EnsembleCombiner<T>) - Static method in class org.tribuo.ensemble.WeightedEnsembleModel
-
Creates an ensemble from existing models.
- createEnsembleFromExistingModels(String, List<Model<T>>, EnsembleCombiner<T>, float[]) - Static method in class org.tribuo.ensemble.WeightedEnsembleModel
-
Creates an ensemble from existing models.
- createEvaluation(C, Map<MetricID<T>, Double>, EvaluationProvenance) - Method in class org.tribuo.evaluation.AbstractEvaluator
-
Create an evaluation for the given results
- createEvaluation(C, Map<MetricID<T>, Double>, EvaluationProvenance) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
-
Create an evaluation for the given results
- createEvaluation(AnomalyMetric.Context, Map<MetricID<Event>, Double>, EvaluationProvenance) - Method in class org.tribuo.anomaly.evaluation.AnomalyEvaluator
- createEvaluation(LabelMetric.Context, Map<MetricID<Label>, Double>, EvaluationProvenance) - Method in class org.tribuo.classification.evaluation.LabelEvaluator
- createEvaluation(LabelMetric.Context, Map<MetricID<Label>, Double>, EvaluationProvenance) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator
- createEvaluation(ClusteringMetric.Context, Map<MetricID<ClusterID>, Double>, EvaluationProvenance) - Method in class org.tribuo.clustering.evaluation.ClusteringEvaluator
- createEvaluation(MultiLabelMetric.Context, Map<MetricID<MultiLabel>, Double>, EvaluationProvenance) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluator
- createEvaluation(RegressionMetric.Context, Map<MetricID<Regressor>, Double>, EvaluationProvenance) - Method in class org.tribuo.regression.evaluation.RegressionEvaluator
- createFeatureMap(Set<String>) - Static method in class org.tribuo.interop.ExternalModel
-
Creates an immutable feature map from a set of feature names.
- createFeatures(Example<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionModel
-
Creates the feature vector.
- createFeatures(Example<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel
-
Creates the feature vector.
- createFeatures(Example<Regressor>) - Method in class org.tribuo.regression.slm.SparseLinearModel
-
Creates the feature vector.
- createFromPairList(List<Pair<String, Boolean>>) - Static method in class org.tribuo.multilabel.MultiLabel
-
Creates a MultiLabel from a list of dimensions.
- createFromPairList(List<Pair<String, Double>>) - Static method in class org.tribuo.regression.Regressor
-
Creates a Regressor from a list of dimension tuples.
- createFromSparseVectors(SparseVector[]) - Static method in class org.tribuo.math.la.DenseSparseMatrix
-
Defensively copies the values.
- createGaussianTrainer(long) - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer
-
Creates a trainer which create models which sample the output from a gaussian distribution fit to the training data.
- createIdentity(int) - Static method in class org.tribuo.math.la.DenseSparseMatrix
-
Creates an identity matrix of the specified size.
- createIDXData(IDXDataSource.IDXType, int[], double[]) - Static method in class org.tribuo.datasource.IDXDataSource.IDXData
-
Constructs an IDXData, validating the input and defensively copying it.
- createLabel(Label) - Method in class org.tribuo.multilabel.MultiLabel
-
Creates a binary label from this multilabel.
- createMeanTrainer() - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer
-
Creates a trainer which create models which return the mean of the training data.
- createMedianTrainer() - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer
-
Creates a trainer which create models which return the median of the training data.
- createMetrics(Model<Event>) - Method in class org.tribuo.anomaly.evaluation.AnomalyEvaluator
- createMetrics(Model<Label>) - Method in class org.tribuo.classification.evaluation.LabelEvaluator
- createMetrics(Model<ClusterID>) - Method in class org.tribuo.clustering.evaluation.ClusteringEvaluator
- createMetrics(Model<MultiLabel>) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluator
- createMetrics(Model<Regressor>) - Method in class org.tribuo.regression.evaluation.RegressionEvaluator
- createMetrics(Model<T>) - Method in class org.tribuo.evaluation.AbstractEvaluator
-
Creates the appropriate set of metrics for this model, by querying for it's
OutputInfo
. - createMetrics(SequenceModel<Label>) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator
- createMetrics(SequenceModel<T>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
-
Creates the appropriate set of metrics for this model, by querying for it's
OutputInfo
. - createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, FMParameters) - Method in class org.tribuo.classification.sgd.fm.FMClassificationTrainer
- createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, LinearParameters) - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer
- createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<MultiLabel>, FMParameters) - Method in class org.tribuo.multilabel.sgd.fm.FMMultiLabelTrainer
- createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<MultiLabel>, LinearParameters) - Method in class org.tribuo.multilabel.sgd.linear.LinearSGDTrainer
- createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, FMParameters) - Method in class org.tribuo.regression.sgd.fm.FMRegressionTrainer
- createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, LinearParameters) - Method in class org.tribuo.regression.sgd.linear.LinearSGDTrainer
- createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<Booster>, XGBoostOutputConverter<T>) - Method in class org.tribuo.common.xgboost.XGBoostTrainer
-
Creates an XGBoost model from the booster list.
- createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, X) - Method in class org.tribuo.common.sgd.AbstractSGDTrainer
-
Creates the appropriate model subclass for this subclass of AbstractSGDTrainer.
- createModel(Path, ModelProvenance, OCIUtil.OCIModelType, DataScienceClient, ObjectMapper, OCIUtil.OCIModelArtifactConfig) - Static method in class org.tribuo.interop.oci.OCIUtil
-
Creates an OCI DS model and uploads the model artifact.
- createModel(Map<String, T>, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer
-
Constructs the appropriate subclass of
SkeletalIndependentRegressionModel
for this trainer. - createModel(Map<String, T>, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionTrainer
-
Constructs the appropriate subclass of
SkeletalIndependentRegressionModel
for this trainer. - createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Event>, List<Model>) - Method in class org.tribuo.anomaly.liblinear.LibLinearAnomalyTrainer
- createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Event>, List<svm_model>) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer
- createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, List<Model>) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationTrainer
- createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, List<svm_model>) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationTrainer
- createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, List<Model>) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionTrainer
- createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, List<svm_model>) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionTrainer
- createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<Model>) - Method in class org.tribuo.common.liblinear.LibLinearTrainer
-
Construct the appropriate subtype of LibLinearModel for the prediction task.
- createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<svm_model>) - Method in class org.tribuo.common.libsvm.LibSVMTrainer
-
Construct the appropriate subtype of LibSVMModel for the prediction task.
- createModel(U, DataScienceClient, ObjectMapper, OCIUtil.OCIModelArtifactConfig) - Static method in class org.tribuo.interop.oci.OCIUtil
-
Creates an OCI DS model and uploads the model artifact.
- createModelArtifact(Path, OCIUtil.OCIModelArtifactConfig) - Static method in class org.tribuo.interop.oci.OCIUtil
-
Creates the OCI DS model artifact zip file.
- createMostFrequentTrainer() - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer
-
Creates a trainer which creates models which return a fixed label, the one which was most frequent in the training data.
- createNeighboursQuery(SGDVector[]) - Method in class org.tribuo.math.neighbour.bruteforce.NeighboursBruteForceFactory
-
Constructs a brute-force nearest neighbor query object using the supplied array of
SGDVector
. - createNeighboursQuery(SGDVector[]) - Method in class org.tribuo.math.neighbour.kdtree.KDTreeFactory
-
Constructs a k-d tree nearest neighbor query object using the supplied array of
SGDVector
. - createNeighboursQuery(SGDVector[]) - Method in interface org.tribuo.math.neighbour.NeighboursQueryFactory
-
Constructs a nearest neighbour query object using the supplied array of
SGDVector
. - createObjectMapper() - Static method in class org.tribuo.interop.oci.OCIUtil
-
Creates an ObjectMapper capable of parsing the OCI DS json.
- createOCIModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, Path, String, String, OCIOutputConverter<T>) - Static method in class org.tribuo.interop.oci.OCIModel
-
Creates an
OCIModel
by wrapping an OCI DS Model Deployment endpoint. - createOCIModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, Path, String, OCIOutputConverter<T>) - Static method in class org.tribuo.interop.oci.OCIModel
-
Creates an
OCIModel
by wrapping an OCI DS Model Deployment endpoint. - createOnlineEvaluator(Model<T>, DataProvenance) - Method in interface org.tribuo.evaluation.Evaluator
-
Creates an online evaluator that maintains a list of all the predictions it has seen and can evaluate them upon request.
- createOnnxModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, ExampleTransformer, OutputTransformer<T>, OrtSession.SessionOptions, String, String) - Static method in class org.tribuo.interop.onnx.ONNXExternalModel
-
Creates an
ONNXExternalModel
by loading the model from disk. - createOnnxModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, ExampleTransformer, OutputTransformer<T>, OrtSession.SessionOptions, Path, String) - Static method in class org.tribuo.interop.onnx.ONNXExternalModel
-
Creates an
ONNXExternalModel
by loading the model from disk. - createOutputInfo(OutputFactory<T>, Map<T, Integer>) - Static method in class org.tribuo.interop.ExternalModel
-
Creates an output info from a set of outputs.
- createParameters(int, int, SplittableRandom) - Method in class org.tribuo.common.sgd.AbstractFMTrainer
-
Constructs the trainable parameters object, in this case a
FMParameters
containing a weight matrix for the feature weights and a series of weight matrices for the factorized feature representation. - createParameters(int, int, SplittableRandom) - Method in class org.tribuo.common.sgd.AbstractLinearSGDTrainer
-
Constructs the trainable parameters object, in this case a
LinearParameters
containing a single weight matrix. - createParameters(int, int, SplittableRandom) - Method in class org.tribuo.common.sgd.AbstractSGDTrainer
-
Constructs the trainable parameters object.
- createQuartileTrainer(double) - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer
-
Creates a trainer which create models which return the value at the specified fraction of the sorted training data.
- createSparseVector(int, int[], double[]) - Static method in class org.tribuo.math.la.SparseVector
-
Defensively copies the input, and checks that the indices are sorted.
- createSparseVector(int, Map<Integer, Double>) - Static method in class org.tribuo.math.la.SparseVector
-
Builds a SparseVector from a map.
- createSparseVector(Example<T>, ImmutableFeatureMap, boolean) - Static method in class org.tribuo.math.la.SparseVector
-
Builds a
SparseVector
from anExample
. - createSplitFunction(boolean) - Static method in class org.tribuo.util.tokens.impl.wordpiece.WordpieceBasicTokenizer
-
Creates a
SplitFunctionTokenizer.SplitFunction
that is used by the super classSplitFunctionTokenizer
to determine how and where the tokenizer splits the input. - createSplitNode() - Method in class org.tribuo.common.tree.AbstractTrainingNode
-
Transforms an
AbstractTrainingNode
into aSplitNode
- createStats() - Method in interface org.tribuo.transform.Transformation
-
Creates the statistics object for this Transformation.
- createStats() - Method in class org.tribuo.transform.transformations.BinningTransformation
- createStats() - Method in class org.tribuo.transform.transformations.IDFTransformation
- createStats() - Method in class org.tribuo.transform.transformations.LinearScalingTransformation
- createStats() - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation
- createStats() - Method in class org.tribuo.transform.transformations.SimpleTransform
-
Returns itself.
- createStratifiedTrainer(long) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer
-
Creates a trainer which creates models which return random labels sampled from the training label distribution.
- createSupplier(Tokenizer) - Static method in interface org.tribuo.util.tokens.Tokenizer
-
Creates a supplier from the specified tokenizer by cloning it.
- createTensorflowModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, String, FeatureConverter, OutputConverter<T>, String) - Static method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
-
Creates a TensorflowFrozenExternalModel by loading in a frozen graph.
- createTensorflowModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, String, FeatureConverter, OutputConverter<T>, String) - Static method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
-
Creates a TensorflowSavedModelExternalModel by loading in a
SavedModelBundle
. - createThreadLocal(Tokenizer) - Static method in interface org.tribuo.util.tokens.Tokenizer
-
Creates a thread local source of tokenizers by making a Tokenizer supplier using
Tokenizer.createSupplier(Tokenizer)
. - createTransformers(TransformationMap) - Method in class org.tribuo.Dataset
-
Takes a
TransformationMap
and converts it into aTransformerMap
by observing all the values in this dataset. - createTransformers(TransformationMap, boolean) - Method in class org.tribuo.Dataset
-
Takes a
TransformationMap
and converts it into aTransformerMap
by observing all the values in this dataset. - createUniformTrainer(long) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer
-
Creates a trainer which creates models which return random labels sampled uniformly from the labels seen at training time.
- createView(Dataset<T>, Predicate<Example<T>>, String) - Static method in class org.tribuo.dataset.DatasetView
-
Creates a view from the supplied dataset, using the specified predicate to test if each example should be in this view.
- createWeightedBootstrapView(Dataset<T>, int, long, float[]) - Static method in class org.tribuo.dataset.DatasetView
-
Generates a DatasetView bootstrapped from the supplied Dataset using the supplied example weights.
- createWeightedBootstrapView(Dataset<T>, int, long, float[], ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.dataset.DatasetView
-
Generates a DatasetView bootstrapped from the supplied Dataset using the supplied example weights.
- createWhitespaceTokenizer() - Static method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
-
Creates a tokenizer that splits on whitespace.
- createWithEmptyOutputs(List<? extends List<? extends Feature>>, OutputFactory<T>) - Static method in class org.tribuo.sequence.SequenceExample
-
Creates a SequenceExample using
OutputFactory.getUnknownOutput()
as the output for each sequence element. - createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, String) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel
-
Creates an
XGBoostExternalModel
from the supplied model on disk. - createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, Path) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel
-
Creates an
XGBoostExternalModel
from the supplied model on disk. - createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, Booster, URL) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel
-
Deprecated.As the URL argument must always be valid. To wrap an in-memory booster use
XGBoostExternalModel.createXGBoostModel(OutputFactory, Map, Map, XGBoostOutputConverter, Booster, Map)
. - createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, Booster, Map<String, Provenance>) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel
-
Creates an
XGBoostExternalModel
from the supplied in-memory XGBoostBooster
. - CREATION_TIME - Static variable in class org.tribuo.provenance.impl.TimestampedTrainerProvenance
-
The name of the provenance field storing the model creation time.
- CRFModel - Class in org.tribuo.classification.sgd.crf
-
An inference time model for a linear chain CRF trained using SGD.
- CRFModel.ConfidenceType - Enum Class in org.tribuo.classification.sgd.crf
-
The type of subsequence level confidence to predict.
- CRFOptions - Class in org.tribuo.classification.sgd.crf
-
CLI options for training a linear chain CRF model.
- CRFOptions() - Constructor for class org.tribuo.classification.sgd.crf.CRFOptions
- CRFOptions() - Constructor for class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
- CRFParameters - Class in org.tribuo.classification.sgd.crf
-
A
Parameters
for training a CRF using SGD. - CRFTrainer - Class in org.tribuo.classification.sgd.crf
-
A trainer for CRFs using SGD.
- CRFTrainer(StochasticGradientOptimiser, int, int, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer
-
Creates a CRFTrainer which uses SGD to learn the parameters.
- CRFTrainer(StochasticGradientOptimiser, int, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer
-
Sets the minibatch size to 1.
- CRFTrainer(StochasticGradientOptimiser, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer
-
Sets the minibatch size to 1 and the logging interval to 100.
- crossValidation - Variable in class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions
-
Cross-validate the output metrics.
- CrossValidation<T extends Output<T>,
E extends Evaluation<T>> - Class in org.tribuo.evaluation -
A class that does k-fold cross-validation.
- CrossValidation(Trainer<T>, Dataset<T>, Evaluator<T, E>, int) - Constructor for class org.tribuo.evaluation.CrossValidation
-
Builds a k-fold cross-validation loop.
- CrossValidation(Trainer<T>, Dataset<T>, Evaluator<T, E>, int, long) - Constructor for class org.tribuo.evaluation.CrossValidation
-
Builds a k-fold cross-validation loop.
- CSV - Enum constant in enum class org.tribuo.data.DataOptions.InputFormat
-
Simple numeric CSV file.
- CSVDataSource<T extends Output<T>> - Class in org.tribuo.data.csv
-
A
DataSource
for loading separable data from a text file (e.g., CSV, TSV) and applyingFieldProcessor
s to it. - CSVDataSource(URI, RowProcessor<T>, boolean) - Constructor for class org.tribuo.data.csv.CSVDataSource
-
Creates a CSVDataSource using the specified RowProcessor to process the data.
- CSVDataSource(URI, RowProcessor<T>, boolean, char) - Constructor for class org.tribuo.data.csv.CSVDataSource
-
Creates a CSVDataSource using the specified RowProcessor to process the data.
- CSVDataSource(URI, RowProcessor<T>, boolean, char, char) - Constructor for class org.tribuo.data.csv.CSVDataSource
-
Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.
- CSVDataSource(URI, RowProcessor<T>, boolean, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVDataSource
-
Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.
- CSVDataSource(Path, RowProcessor<T>, boolean) - Constructor for class org.tribuo.data.csv.CSVDataSource
-
Creates a CSVDataSource using the specified RowProcessor to process the data.
- CSVDataSource(Path, RowProcessor<T>, boolean, char) - Constructor for class org.tribuo.data.csv.CSVDataSource
-
Creates a CSVDataSource using the specified RowProcessor to process the data.
- CSVDataSource(Path, RowProcessor<T>, boolean, char, char) - Constructor for class org.tribuo.data.csv.CSVDataSource
-
Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.
- CSVDataSource(Path, RowProcessor<T>, boolean, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVDataSource
-
Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.
- CSVDataSource.CSVDataSourceProvenance - Class in org.tribuo.data.csv
-
Provenance for
CSVDataSource
. - CSVDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance
-
Deserialization constructor.
- CSVIterator - Class in org.tribuo.data.csv
-
An iterator over a CSV file.
- CSVIterator(Reader) - Constructor for class org.tribuo.data.csv.CSVIterator
-
Builds a CSVIterator for the supplied Reader.
- CSVIterator(Reader, char, char) - Constructor for class org.tribuo.data.csv.CSVIterator
-
Builds a CSVIterator for the supplied Reader.
- CSVIterator(Reader, char, char, String[]) - Constructor for class org.tribuo.data.csv.CSVIterator
-
Builds a CSVIterator for the supplied Reader.
- CSVIterator(Reader, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVIterator
-
Builds a CSVIterator for the supplied Reader.
- CSVIterator(URI) - Constructor for class org.tribuo.data.csv.CSVIterator
-
Builds a CSVIterator for the supplied URI.
- CSVIterator(URI, char, char) - Constructor for class org.tribuo.data.csv.CSVIterator
-
Builds a CSVIterator for the supplied URI.
- CSVIterator(URI, char, char, String[]) - Constructor for class org.tribuo.data.csv.CSVIterator
-
Builds a CSVIterator for the supplied URI.
- CSVIterator(URI, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVIterator
-
Builds a CSVIterator for the supplied URI.
- CSVLoader<T extends Output<T>> - Class in org.tribuo.data.csv
-
Load a DataSource/Dataset from a CSV file.
- CSVLoader(char, char, OutputFactory<T>) - Constructor for class org.tribuo.data.csv.CSVLoader
-
Creates a CSVLoader using the supplied separator, quote and output factory.
- CSVLoader(char, OutputFactory<T>) - Constructor for class org.tribuo.data.csv.CSVLoader
-
Creates a CSVLoader using the supplied separator and output factory.
- CSVLoader(OutputFactory<T>) - Constructor for class org.tribuo.data.csv.CSVLoader
-
Creates a CSVLoader using the supplied output factory.
- CSVLoader.CSVLoaderProvenance - Class in org.tribuo.data.csv
-
Deprecated.Deprecated in 4.2 as CSVLoader now returns a
CSVDataSource
. This provenance is kept so older models can still load correctly. - CSVLoaderProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.csv.CSVLoader.CSVLoaderProvenance
-
Deprecated.Deserialization constructor.
- csvQuoteChar - Variable in class org.tribuo.data.DataOptions
-
Quote character in the CSV file.
- csvResponseName - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
-
Response name in the csv file.
- csvResponseName - Variable in class org.tribuo.data.DataOptions
-
Response name in the csv file.
- CSVSaver - Class in org.tribuo.data.csv
-
Saves a Dataset in CSV format suitable for loading by
CSVLoader
. - CSVSaver() - Constructor for class org.tribuo.data.csv.CSVSaver
-
Builds a CSV saver using the default separator and quote from
CSVIterator
. - CSVSaver(char, char) - Constructor for class org.tribuo.data.csv.CSVSaver
-
Builds a CSV saver using the supplied separator and quote.
- cumulativeSum(boolean[]) - Static method in class org.tribuo.util.Util
-
Produces a cumulative sum array.
- cumulativeSum(double[]) - Static method in class org.tribuo.util.Util
-
Produces a cumulative sum array.
- CURRENT_VERSION - Static variable in class org.tribuo.anomaly.libsvm.LibSVMAnomalyModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.CategoricalIDInfo
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.CategoricalInfo
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.baseline.DummyClassifierModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.ensemble.VotingCombiner
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.libsvm.LibSVMClassificationModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.mnb.MultinomialNaiveBayesModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.sequence.viterbi.ViterbiModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.sgd.crf.CRFModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.sgd.crf.CRFParameters
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.sgd.fm.FMClassificationModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.sgd.kernel.KernelSVMModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.sgd.linear.LinearSGDModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.classification.xgboost.XGBoostClassificationConverter
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.clustering.hdbscan.HdbscanModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.clustering.kmeans.KMeansModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.common.liblinear.LibLinearModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.common.nearest.KNNModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.common.sgd.FMParameters
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.common.tree.LeafNode
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.common.tree.SplitNode
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.common.tree.TreeModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.common.xgboost.XGBoostExternalModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.common.xgboost.XGBoostModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.dataset.DatasetView
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.dataset.MinimumCardinalityDataset
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.dataset.SelectedFeatureDataset
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.ensemble.WeightedEnsembleModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.hash.HashCodeHasher
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.hash.HashedFeatureMap
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.hash.MessageDigestHasher
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.hash.ModHashCodeHasher
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.ImmutableDataset
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.ImmutableFeatureMap
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.impl.ArrayExample
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.impl.BinaryFeaturesExample
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.impl.IndexedArrayExample
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.impl.ListExample
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.oci.OCILabelConverter
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.oci.OCIModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.oci.OCIMultiLabelConverter
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.oci.OCIRegressorConverter
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.onnx.DenseTransformer
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.onnx.ImageTransformer
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.onnx.LabelOneVOneTransformer
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.onnx.LabelTransformer
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.onnx.MultiLabelTransformer
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.onnx.ONNXExternalModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.onnx.RegressorTransformer
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.tensorflow.DenseFeatureConverter
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.tensorflow.ImageConverter
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.tensorflow.LabelConverter
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.tensorflow.MultiLabelConverter
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.tensorflow.RegressorConverter
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.tensorflow.TensorFlowCheckpointModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.tensorflow.TensorFlowNativeModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.distance.CosineDistance
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.distance.L1Distance
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.distance.L2Distance
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.kernel.Linear
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.kernel.Polynomial
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.kernel.RBF
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.kernel.Sigmoid
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.la.DenseMatrix
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.la.DenseSparseMatrix
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.la.DenseVector
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.la.SparseVector
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.LinearParameters
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.neighbour.bruteforce.NeighboursBruteForceFactory
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.neighbour.kdtree.KDTreeFactory
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.util.ExpNormalizer
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.util.HeapMerger
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.util.MatrixHeapMerger
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.util.NoopNormalizer
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.util.Normalizer
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.math.util.SigmoidNormalizer
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.multilabel.baseline.ClassifierChainModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.multilabel.baseline.IndependentMultiLabelModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.multilabel.ensemble.MultiLabelVotingCombiner
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.multilabel.sgd.linear.LinearSGDModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.MutableDataset
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.MutableFeatureMap
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.Prediction
-
The current protobuf serialization version of this class.
- CURRENT_VERSION - Static variable in class org.tribuo.RealIDInfo
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.RealInfo
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.regression.baseline.DummyRegressionModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.regression.ensemble.AveragingCombiner
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.regression.libsvm.LibSVMRegressionModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.regression.sgd.fm.FMRegressionModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.regression.sgd.linear.LinearSGDModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.regression.slm.SparseLinearModel
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.regression.xgboost.XGBoostRegressionConverter
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.SelectedFeatureSet
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.sequence.ImmutableSequenceDataset
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.sequence.MinimumCardinalitySequenceDataset
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.sequence.MutableSequenceDataset
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.sequence.SequenceExample
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.transform.transformations.BinningTransformation.BinningTransformer
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.transform.transformations.SimpleTransform
-
Protobuf serialization version.
- CURRENT_VERSION - Static variable in class org.tribuo.transform.TransformerMap
-
Protobuf serialization version.
- currentRow - Variable in class org.tribuo.data.columnar.ColumnarIterator
-
The current row.
D
- DART - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.BoosterType
-
A gradient boosted decision tree using dropout.
- data - Variable in class org.tribuo.common.xgboost.XGBoostTrainer.DMatrixTuple
-
The data matrix.
- data - Variable in class org.tribuo.data.text.TextDataSource
-
The actual data read out of the text file.
- data - Variable in class org.tribuo.Dataset
-
The data in this data set.
- data - Variable in class org.tribuo.interop.tensorflow.TensorFlowUtil.TensorTuple
-
The tensor data.
- data - Variable in class org.tribuo.sequence.SequenceDataset
-
The data in this data set.
- DataOptions - Class in org.tribuo.data
-
Options for working with training and test data in a CLI.
- DataOptions() - Constructor for class org.tribuo.data.DataOptions
- DataOptions.Delimiter - Enum Class in org.tribuo.data
-
The delimiters supported by CSV files in this options object.
- DataOptions.InputFormat - Enum Class in org.tribuo.data
-
The input formats supported by this options object.
- DataProvenance - Interface in org.tribuo.provenance
-
Tag interface for data sources provenances.
- Dataset<T extends Output<T>> - Class in org.tribuo
-
A class for sets of data, which are used to train and evaluate classifiers.
- Dataset(DataSource<T>) - Constructor for class org.tribuo.Dataset
-
Creates a dataset.
- Dataset(DataProvenance, OutputFactory<T>) - Constructor for class org.tribuo.Dataset
-
Creates a dataset.
- Dataset(DataProvenance, OutputFactory<T>, String) - Constructor for class org.tribuo.Dataset
-
Creates a dataset.
- DATASET - Enum constant in enum class org.tribuo.json.StripProvenance.ProvenanceTypes
-
Select the dataset provenance.
- DATASET - Static variable in class org.tribuo.provenance.ModelProvenance
-
The name of the dataset provenance field.
- DatasetDataCarrier<T extends Output<T>> - Class in org.tribuo.impl
-
Serialization carrier for common fields in Dataset.
- DatasetDataCarrier(DataProvenance, FeatureMap, OutputInfo<T>, OutputFactory<T>, List<ObjectProvenance>, String) - Constructor for class org.tribuo.impl.DatasetDataCarrier
-
Constructs a new DatasetDataCarrier.
- DatasetExplorer - Class in org.tribuo.data
-
A CLI for exploring a serialised
Dataset
. - DatasetExplorer() - Constructor for class org.tribuo.data.DatasetExplorer
-
Constructs a dataset explorer.
- DatasetExplorer.DatasetExplorerOptions - Class in org.tribuo.data
-
Command line options.
- DatasetExplorerOptions() - Constructor for class org.tribuo.data.DatasetExplorer.DatasetExplorerOptions
- datasetName - Variable in class org.tribuo.classification.sequence.SeqTrainTest.SeqTrainTestOptions
-
Name of the example dataset, options are {gorilla}.
- datasetName - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
-
Name of the example dataset, options are {gorilla}.
- datasetPath - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions
-
Path to the serialized dataset to score.
- datasetProtobuf - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions
-
Is the serialized dataset in protobuf format?
- datasetProvenance - Variable in class org.tribuo.provenance.ModelProvenance
-
The dataset provenance.
- DatasetProvenance - Class in org.tribuo.provenance
-
Base class for dataset provenance.
- DatasetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.DatasetProvenance
-
Deserialization constructor.
- DatasetProvenance(DataProvenance, ListProvenance<ObjectProvenance>, String, boolean, boolean, int, int, int) - Constructor for class org.tribuo.provenance.DatasetProvenance
-
Constructs a dataset provenance using the supplied information.
- DatasetProvenance(DataProvenance, ListProvenance<ObjectProvenance>, Dataset<T>) - Constructor for class org.tribuo.provenance.DatasetProvenance
-
Creates a dataset provenance from the supplied dataset.
- DatasetProvenance(DataProvenance, ListProvenance<ObjectProvenance>, SequenceDataset<T>) - Constructor for class org.tribuo.provenance.DatasetProvenance
-
Creates a dataset provenance from the supplied sequence dataset.
- DatasetView<T extends Output<T>> - Class in org.tribuo.dataset
-
DatasetView provides an immutable view on another
Dataset
that only exposes selected examples. - DatasetView(Dataset<T>, int[], String) - Constructor for class org.tribuo.dataset.DatasetView
-
Creates a DatasetView which includes the supplied indices from the dataset.
- DatasetView(Dataset<T>, int[], ImmutableFeatureMap, ImmutableOutputInfo<T>, String) - Constructor for class org.tribuo.dataset.DatasetView
-
Creates a DatasetView which includes the supplied indices from the dataset.
- DatasetView.DatasetViewProvenance - Class in org.tribuo.dataset
-
Provenance for the
DatasetView
. - DatasetViewProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.dataset.DatasetView.DatasetViewProvenance
-
Deserialization constructor.
- dataSource - Variable in class org.tribuo.data.PreprocessAndSerialize.PreprocessAndSerializeOptions
-
Datasource to load from a config file
- DataSource<T extends Output<T>> - Interface in org.tribuo
-
A interface for things that can be given to a Dataset's constructor.
- DATASOURCE_CREATION_TIME - Static variable in interface org.tribuo.provenance.DataSourceProvenance
-
The name of the provenance field for the datasource timestamp.
- DataSourceProvenance - Interface in org.tribuo.provenance
-
Data source provenance.
- DateExtractor - Class in org.tribuo.data.columnar.extractors
-
Extracts the field value and translates it to a
LocalDate
based on the specifiedDateTimeFormatter
. - DateExtractor(String, String, String) - Constructor for class org.tribuo.data.columnar.extractors.DateExtractor
-
Constructs a date extractor that emits a LocalDate by applying the supplied format to the specified field.
- DateExtractor(String, String, String, String, String) - Constructor for class org.tribuo.data.columnar.extractors.DateExtractor
-
Constructs a date extractor that emits a LocalDate by applying the supplied format to the specified field.
- DateExtractor(String, String, DateTimeFormatter) - Constructor for class org.tribuo.data.columnar.extractors.DateExtractor
-
Deprecated.
- DateFieldProcessor - Class in org.tribuo.data.columnar.processors.field
-
Processes a column that contains a date value.
- DateFieldProcessor(String, EnumSet<DateFieldProcessor.DateFeatureType>, String) - Constructor for class org.tribuo.data.columnar.processors.field.DateFieldProcessor
-
Constructs a field processor which parses a date from the specified field name using the supplied format string then extracts date features according to the supplied
EnumSet
. - DateFieldProcessor(String, EnumSet<DateFieldProcessor.DateFeatureType>, String, String, String) - Constructor for class org.tribuo.data.columnar.processors.field.DateFieldProcessor
-
Constructs a field processor which parses a date from the specified field name using the supplied format string then extracts date features according to the supplied
EnumSet
. - DateFieldProcessor.DateFeatureType - Enum Class in org.tribuo.data.columnar.processors.field
-
The types of date features which can be extracted.
- DAY - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
-
The day.
- DAY_OF_QUARTER - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
-
The day of the quarter.
- DAY_OF_WEEK - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
-
The day of the week in ISO 8601.
- DAY_OF_YEAR - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
-
The day of the year.
- dbConfig - Variable in class org.tribuo.data.sql.SQLToCSV.SQLToCSVOptions
-
Name of the DBConfig to use
- DEBUG - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.LoggingVerbosity
-
All the logging.
- DecisionTreeTrainer<T extends Output<T>> - Interface in org.tribuo.common.tree
-
A tag interface for a
Trainer
so the random forests trainer can check if it's actually a tree. - decode(Tensor, List<SequenceExample<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputConverter
-
Decode graph output tensors corresponding to a batch of input sequences.
- decode(Tensor, SequenceExample<T>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputConverter
-
Decode a tensor of graph output into a list of predictions for the input sequence.
- deepCopy() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
-
Copies this inverted feature.
- deepCopy() - Method in class org.tribuo.regression.rtree.impl.TreeFeature
-
Returns a deep copy of this tree feature.
- DEFAULT - Enum constant in enum class org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions.ViterbiLabelFeatures
-
The default label features.
- DEFAULT_BATCH_SIZE - Static variable in class org.tribuo.interop.ExternalModel
-
Default batch size for external model batch predictions.
- DEFAULT_COST - Static variable in class org.tribuo.regression.sgd.objectives.Huber
-
The default cost beyond which the function is linear.
- DEFAULT_FIELD_NAME - Static variable in annotation interface org.tribuo.protos.ProtoSerializableField
-
The default field name, used to signify it should use the field name rather than a supplied value.
- DEFAULT_HASH_SEED - Static variable in class org.tribuo.data.text.impl.FeatureHasher
-
Default value for the hash function seed.
- DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.impl.TripleDistribution
-
The default map size to initialise the marginalised count maps with.
- DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
-
The default map size.
- DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.InformationTheory
-
The initial size of the various maps.
- DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory
-
The initial size of the various maps.
- DEFAULT_METADATA_SIZE - Static variable in class org.tribuo.Example
-
The default initial size of the metadata map.
- DEFAULT_NAME - Static variable in class org.tribuo.regression.Regressor
-
Default name used for dimensions which are unnamed when parsed from Strings.
- DEFAULT_RESPONSE - Static variable in class org.tribuo.data.csv.CSVSaver
-
The default response column name.
- DEFAULT_SCORE - Static variable in class org.tribuo.anomaly.Event
-
The default score of events.
- DEFAULT_SEED - Static variable in interface org.tribuo.Trainer
-
Default seed used to initialise RNGs.
- DEFAULT_SIZE - Static variable in class org.tribuo.common.tree.AbstractTrainingNode
-
Default buffer size used in the split operation.
- DEFAULT_SIZE - Static variable in class org.tribuo.impl.ArrayExample
-
Default initial size of the backing arrays.
- DEFAULT_SIZE - Static variable in class org.tribuo.impl.BinaryFeaturesExample
-
Default initial size of the backing arrays.
- DEFAULT_SPLIT_CHAR - Static variable in class org.tribuo.regression.RegressionFactory
-
The default character to split the string form of a multidimensional regressor.
- DEFAULT_SPLIT_CHARACTERS - Static variable in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
-
The default split characters.
- DEFAULT_SPLIT_EXCEPTING_IN_DIGITS_CHARACTERS - Static variable in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
-
The default characters which don't cause splits inside digits.
- DEFAULT_THRESHOLD - Static variable in class org.tribuo.interop.oci.OCIMultiLabelConverter
-
The default threshold for conversion into a label.
- DEFAULT_THRESHOLD - Static variable in class org.tribuo.interop.onnx.MultiLabelTransformer
-
The default threshold for conversion into a label.
- DEFAULT_UNKNOWN_TOKEN - Static variable in class org.tribuo.util.tokens.impl.wordpiece.Wordpiece
-
The default unknown token string.
- DEFAULT_VALUE_HASH_SEED - Static variable in class org.tribuo.data.text.impl.FeatureHasher
-
Default value for the value hash function seed.
- DEFAULT_WEIGHT - Static variable in class org.tribuo.Example
-
The default weight.
- DEFAULT_WEIGHT - Static variable in class org.tribuo.sequence.SequenceExample
-
The default sequence example weight.
- DefaultFeatureExtractor - Class in org.tribuo.classification.sequence.viterbi
-
A label feature extractor that produces several kinds of label-based features.
- DefaultFeatureExtractor() - Constructor for class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
-
Constructs a default feature extractor for bigrams and trigrams using the past 3 outcomes.
- DefaultFeatureExtractor(int, int, boolean, boolean, boolean) - Constructor for class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
-
Constructs a default feature extractor using the supplied parameters.
- degree - Variable in class org.tribuo.regression.libsvm.TrainTest.LibSVMOptions
-
Degree in polynomial kernel.
- delimiter - Variable in class org.tribuo.data.DataOptions
-
Delimiter
- DELTA - Static variable in class org.tribuo.math.la.VectorTuple
-
The tolerance for equality in value comparisons.
- DemoLabelDataSource - Class in org.tribuo.classification.example
-
The base class for the 2d binary classification data sources in
org.tribuo.classification.example
. - DemoLabelDataSource.DemoLabelDataSourceProvenance - Class in org.tribuo.classification.example
-
Provenance for
DemoLabelDataSource
. - DemoLabelDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.classification.example.DemoLabelDataSource.DemoLabelDataSourceProvenance
-
Constructs a provenance from the marshalled form.
- DemoOptions() - Constructor for class org.tribuo.util.infotheory.example.InformationTheoryDemo.DemoOptions
- dense - Variable in class org.tribuo.MutableDataset
-
Denotes if this dataset contains implicit zeros or not.
- dense - Variable in class org.tribuo.sequence.MutableSequenceDataset
-
Does this dataset have a dense feature space.
- DENSE - Enum constant in enum class org.tribuo.interop.tensorflow.TrainTest.InputType
-
Dense feature extractor.
- DenseFeatureConverter - Class in org.tribuo.interop.tensorflow
-
Converts a sparse example into a dense float vector, then wraps it in a
TFloat32
. - DenseFeatureConverter(String) - Constructor for class org.tribuo.interop.tensorflow.DenseFeatureConverter
-
Builds a DenseFeatureConverter, setting the input name.
- DenseMatrix - Class in org.tribuo.math.la
-
A dense matrix, backed by a primitive array.
- DenseMatrix(int, int) - Constructor for class org.tribuo.math.la.DenseMatrix
-
Creates a dense matrix full of zeros.
- DenseMatrix(DenseMatrix) - Constructor for class org.tribuo.math.la.DenseMatrix
-
Copies the supplied matrix.
- DenseMatrix(Matrix) - Constructor for class org.tribuo.math.la.DenseMatrix
-
Copies the supplied matrix, densifying it if it's sparse.
- DenseMatrix.CholeskyFactorization - Class in org.tribuo.math.la
-
The output of a successful Cholesky factorization.
- DenseMatrix.EigenDecomposition - Class in org.tribuo.math.la
-
The output of a successful eigen decomposition.
- DenseMatrix.LUFactorization - Class in org.tribuo.math.la
-
The output of a successful LU factorization.
- DenseSparseMatrix - Class in org.tribuo.math.la
-
A matrix which is dense in the first dimension and sparse in the second.
- DenseSparseMatrix(int, int) - Constructor for class org.tribuo.math.la.DenseSparseMatrix
-
Creates a DenseSparseMatrix with no values or indices.
- DenseSparseMatrix(List<SparseVector>) - Constructor for class org.tribuo.math.la.DenseSparseMatrix
-
Constructs a DenseSparseMatrix out of the supplied sparse vector list.
- DenseSparseMatrix(DenseSparseMatrix) - Constructor for class org.tribuo.math.la.DenseSparseMatrix
-
Creates a new DenseSparseMatrix by deep copying the supplied DenseSparseMatrix.
- denseTrainTest() - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator
-
Makes a simple dataset for training and testing.
- denseTrainTest() - Static method in class org.tribuo.classification.example.LabelledDataGenerator
-
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 classes, {Foo,Bar,Baz,Quux}.
- denseTrainTest() - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator
-
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
- denseTrainTest() - Static method in class org.tribuo.regression.example.RegressionDataGenerator
-
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
- denseTrainTest(double) - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator
-
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
- denseTrainTest(double) - Static method in class org.tribuo.classification.example.LabelledDataGenerator
-
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 classes, {Foo,Bar,Baz,Quux}.
- denseTrainTest(double) - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator
-
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
- denseTrainTest(double) - Static method in class org.tribuo.regression.example.RegressionDataGenerator
-
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
- DenseTransformer - Class in org.tribuo.interop.onnx
-
Converts a sparse Tribuo example into a dense float vector, then wraps it in an
OnnxTensor
. - DenseTransformer() - Constructor for class org.tribuo.interop.onnx.DenseTransformer
-
Construct a transformer which converts Tribuo sparse vectors into a dense tensor.
- DenseVector - Class in org.tribuo.math.la
-
A dense vector, backed by a double array.
- DenseVector(double[]) - Constructor for class org.tribuo.math.la.DenseVector
-
Does not defensively copy the input, used internally.
- DenseVector(int) - Constructor for class org.tribuo.math.la.DenseVector
-
Creates an empty dense vector of the specified size.
- DenseVector(int, double) - Constructor for class org.tribuo.math.la.DenseVector
-
Creates a dense vector of the specified size where each element is initialised to the specified value.
- DenseVector(DenseVector) - Constructor for class org.tribuo.math.la.DenseVector
-
Copy constructor.
- densify() - Method in class org.tribuo.math.la.SparseVector
-
Returns a dense vector copying this sparse vector.
- densify() - Method in class org.tribuo.MutableDataset
-
Iterates through the examples, converting implicit zeros into explicit zeros.
- densify() - Method in class org.tribuo.sequence.MutableSequenceDataset
-
Iterates through the examples, converting implicit zeros into explicit zeros.
- densify(List<String>) - Method in class org.tribuo.Example
-
Adds zero valued features for each feature name in
featureNames
. - densify(List<String>) - Method in class org.tribuo.impl.ArrayExample
-
Adds zero valued features for each feature name in
featureList
. - densify(List<String>) - Method in class org.tribuo.impl.BinaryFeaturesExample
- densify(List<String>) - Method in class org.tribuo.impl.IndexedArrayExample
-
Unlike
ArrayExample.densify(List)
this method will throwIllegalArgumentException
if one of the feature names is not present in this example'sImmutableFeatureMap
. - densify(List<String>) - Method in class org.tribuo.impl.ListExample
-
Adds zero valued features for each feature name in
featureNames
. - densify(FeatureMap) - Method in class org.tribuo.Example
-
Converts all implicit zeros into explicit zeros based on the supplied feature map.
- densify(FeatureMap) - Method in class org.tribuo.impl.BinaryFeaturesExample
- densify(FeatureMap) - Method in class org.tribuo.sequence.SequenceExample
-
Converts all implicit zeros into explicit zeros based on the supplied feature map.
- deploy(OCIUtil.OCIModelDeploymentConfig, DataScienceClient, ObjectMapper) - Static method in class org.tribuo.interop.oci.OCIUtil
-
Creates a Model deployment from an uploaded model.
- DEPLOY - Enum constant in enum class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions.Mode
-
Create a Model deployment.
- deploymentName - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelDeploymentConfig
-
The deployment name.
- depth - Variable in class org.tribuo.common.tree.AbstractTrainingNode
- depth - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions
-
Maximum depth in the decision tree.
- depth - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
-
Max tree depth (default 6, range (0,inf]).
- depth - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
-
Max tree depth (default 6, range (0,inf]).
- DESCRIPTION - Static variable in class org.tribuo.provenance.SimpleDataSourceProvenance
-
The description field in the provenance.
- DescriptiveStats - Class in org.tribuo.evaluation
-
Descriptive statistics calculated across a list of doubles.
- DescriptiveStats() - Constructor for class org.tribuo.evaluation.DescriptiveStats
-
Create an empty DescriptiveStats.
- DescriptiveStats(List<Double>) - Constructor for class org.tribuo.evaluation.DescriptiveStats
-
Create a DescriptiveStats initialized with the supplied values.
- DESERIALIZATION_METHOD_NAME - Static variable in interface org.tribuo.protos.ProtoSerializable
-
The name of the static deserialization method for
ProtoSerializable
classes. - deserialize(LabelFeatureExtractorProto) - Static method in interface org.tribuo.classification.sequence.viterbi.LabelFeatureExtractor
-
Deserializes the LabelFeatureExtractor from the supplied protobuf.
- deserialize(KernelProto) - Static method in interface org.tribuo.math.kernel.Kernel
-
Deserializes the kernel from the supplied protobuf.
- deserialize(NeighbourFactoryProto) - Static method in interface org.tribuo.math.neighbour.NeighboursQueryFactory
-
Deserialization helper for NeighboursQueryFactories.
- deserialize(NormalizerProto) - Static method in interface org.tribuo.math.util.VectorNormalizer
-
Deserializes the vector normalizer from the supplied protobuf.
- deserialize(ParametersProto) - Static method in interface org.tribuo.math.Parameters
-
Deserializes the parameters from the supplied protobuf.
- deserialize(TensorProto) - Static method in interface org.tribuo.math.la.Tensor
-
Deserialize a tensor proto into a Tensor.
- deserialize(DatasetDataProto) - Static method in class org.tribuo.impl.DatasetDataCarrier
-
Deserializes a
DatasetDataProto
into aDatasetDataCarrier
. - deserialize(DatasetProto) - Static method in class org.tribuo.Dataset
-
Deserializes a dataset proto into a dataset.
- deserialize(EnsembleCombinerProto) - Static method in interface org.tribuo.ensemble.EnsembleCombiner
-
Deserialization helper for EnsembleCombiner.
- deserialize(ExampleProto) - Static method in class org.tribuo.Example
-
Deserializes an example proto into an example.
- deserialize(FeatureDomainProto) - Static method in class org.tribuo.FeatureMap
-
Deserializes a
FeatureDomainProto
into aFeatureMap
subclass. - deserialize(ModelDataProto) - Static method in class org.tribuo.impl.ModelDataCarrier
-
Deserializes a
ModelDataProto
into aModelDataCarrier
. - deserialize(ModelProto) - Static method in class org.tribuo.Model
-
Deserializes the model from the supplied protobuf.
- deserialize(OutputDomainProto) - Static method in interface org.tribuo.OutputInfo
-
Deserializes a
OutputDomainProto
into aOutputInfo
subclass. - deserialize(OutputFactoryProto) - Static method in interface org.tribuo.OutputFactory
-
Deserializes a
OutputFactoryProto
into aOutputFactory
subclass. - deserialize(OutputProto) - Static method in interface org.tribuo.Output
-
Deserializes a
OutputProto
into aOutput
subclass. - deserialize(SequenceDatasetProto) - Static method in class org.tribuo.sequence.SequenceDataset
-
Deserializes a sequence dataset proto into a sequence dataset.
- deserialize(SequenceExampleProto) - Static method in class org.tribuo.sequence.SequenceExample
-
Deserialization shortcut, used to firm up the types.
- deserialize(SequenceModelProto) - Static method in class org.tribuo.sequence.SequenceModel
-
Deserializes the model from the supplied protobuf.
- deserialize(TransformerMapProto) - Static method in class org.tribuo.transform.TransformerMap
-
Deserializes a
TransformerMapProto
into aTransformerMap
. - deserialize(TransformerProto) - Static method in interface org.tribuo.transform.Transformer
-
Deserializes a
TransformerProto
into aTransformer
subclass. - deserialize(SERIALIZED) - Static method in class org.tribuo.protos.ProtoUtil
-
Instantiates the class from the supplied protobuf fields.
- deserializeExamples(List<ExampleProto>, Class<?>, FeatureMap) - Static method in class org.tribuo.Dataset
-
Deserializes a list of example protos into a list of examples.
- deserializeExamples(List<SequenceExampleProto>, Class<?>, FeatureMap) - Static method in class org.tribuo.sequence.SequenceDataset
-
Deserializes a list of sequence example protos into a list of sequence examples.
- deserializeFromFile(Path) - Static method in class org.tribuo.Dataset
-
Reads an instance of
DatasetProto
from the supplied path and deserializes it. - deserializeFromFile(Path) - Static method in class org.tribuo.Model
-
Reads an instance of
ModelProto
from the supplied path and deserializes it. - deserializeFromFile(Path) - Static method in class org.tribuo.sequence.SequenceDataset
-
Reads an instance of
SequenceDatasetProto
from the supplied path and deserializes it. - deserializeFromFile(Path) - Static method in class org.tribuo.sequence.SequenceModel
-
Reads an instance of
SequenceModelProto
from the supplied path and deserializes it. - deserializeFromJson(JsonNode) - Static method in class org.tribuo.interop.modelcard.ModelCard
-
Reads the Json content corresponding to a ModelCard and instantiates it.
- deserializeFromJson(Path) - Static method in class org.tribuo.interop.modelcard.ModelCard
-
Reads the Json content corresponding to a ModelCard from file and instantiates it.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.anomaly.AnomalyFactory
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.anomaly.Event
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.anomaly.ImmutableAnomalyInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.anomaly.liblinear.LibLinearAnomalyModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.anomaly.MutableAnomalyInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.CategoricalIDInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.CategoricalInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.baseline.DummyClassifierModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.ensemble.VotingCombiner
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.ImmutableLabelInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.Label
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.LabelFactory
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.liblinear.LibLinearClassificationModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.libsvm.LibSVMClassificationModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.mnb.MultinomialNaiveBayesModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.MutableLabelInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sgd.crf.CRFModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sgd.crf.CRFParameters
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sgd.fm.FMClassificationModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sgd.kernel.KernelSVMModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sgd.linear.LinearSGDModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.xgboost.XGBoostClassificationConverter
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.clustering.ClusterID
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.clustering.ClusteringFactory
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.clustering.hdbscan.HdbscanModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.clustering.ImmutableClusteringInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.clustering.kmeans.KMeansModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.clustering.MutableClusteringInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.common.nearest.KNNModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.common.sgd.FMParameters
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.common.tree.TreeModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.common.xgboost.XGBoostModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.dataset.DatasetView
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.dataset.MinimumCardinalityDataset
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.dataset.SelectedFeatureDataset
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.ensemble.WeightedEnsembleModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.hash.HashCodeHasher
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.hash.HashedFeatureMap
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.hash.MessageDigestHasher
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.hash.ModHashCodeHasher
-
Deserialization constructor.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.ImmutableDataset
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.ImmutableFeatureMap
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.impl.ArrayExample
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.impl.BinaryFeaturesExample
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.impl.IndexedArrayExample
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.impl.ListExample
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.oci.OCILabelConverter
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.oci.OCIModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.oci.OCIMultiLabelConverter
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.oci.OCIRegressorConverter
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.onnx.DenseTransformer
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.onnx.ImageTransformer
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.onnx.LabelOneVOneTransformer
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.onnx.LabelTransformer
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.onnx.MultiLabelTransformer
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.onnx.ONNXExternalModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.onnx.RegressorTransformer
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.tensorflow.DenseFeatureConverter
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.tensorflow.ImageConverter
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.tensorflow.LabelConverter
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.tensorflow.MultiLabelConverter
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.tensorflow.RegressorConverter
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.tensorflow.TensorFlowCheckpointModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.tensorflow.TensorFlowNativeModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.distance.CosineDistance
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.distance.L1Distance
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.distance.L2Distance
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.kernel.Linear
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.kernel.Polynomial
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.kernel.RBF
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.kernel.Sigmoid
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.la.DenseMatrix
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.la.DenseSparseMatrix
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.la.DenseVector
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.la.SparseVector
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.LinearParameters
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.neighbour.bruteforce.NeighboursBruteForceFactory
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.neighbour.kdtree.KDTreeFactory
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.optimisers.util.ShrinkingMatrix
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.optimisers.util.ShrinkingVector
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.util.ExpNormalizer
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.util.HeapMerger
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.util.MatrixHeapMerger
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.util.NoopNormalizer
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.util.Normalizer
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.math.util.SigmoidNormalizer
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.multilabel.baseline.ClassifierChainModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.multilabel.baseline.IndependentMultiLabelModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.multilabel.ensemble.MultiLabelVotingCombiner
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.multilabel.MultiLabel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.multilabel.MultiLabelFactory
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.multilabel.MutableMultiLabelInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.multilabel.sgd.fm.FMMultiLabelModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.multilabel.sgd.linear.LinearSGDModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.MutableDataset
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.MutableFeatureMap
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.Prediction
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.RealIDInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.RealInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.baseline.DummyRegressionModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.ensemble.AveragingCombiner
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.ImmutableRegressionInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.liblinear.LibLinearRegressionModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.libsvm.LibSVMRegressionModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.MutableRegressionInfo
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.RegressionFactory
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.Regressor
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.Regressor.DimensionTuple
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.sgd.fm.FMRegressionModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.sgd.linear.LinearSGDModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.slm.SparseLinearModel
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.regression.xgboost.XGBoostRegressionConverter
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.SelectedFeatureSet
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.sequence.ImmutableSequenceDataset
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.sequence.MutableSequenceDataset
-
Deserialization factory.
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.sequence.SequenceExample
-
Deserialization factory.
- deserializeFromProtos(List<TreeNodeProto>, Class<U>) - Static method in class org.tribuo.common.tree.TreeModel
-
We will start off with a list of node builders that we will replace item-by-item with the nodes that they built.
- deserializeFromStream(InputStream) - Static method in class org.tribuo.Dataset
-
Reads an instance of
DatasetProto
from the supplied input stream and deserializes it. - deserializeFromStream(InputStream) - Static method in class org.tribuo.Model
-
Reads an instance of
ModelProto
from the supplied input stream and deserializes it. - deserializeFromStream(InputStream) - Static method in class org.tribuo.sequence.SequenceDataset
-
Reads an instance of
SequenceDatasetProto
from the supplied input stream and deserializes it. - deserializeFromStream(InputStream) - Static method in class org.tribuo.sequence.SequenceModel
-
Reads an instance of
SequenceModelProto
from the supplied input stream and deserializes it. - deserializeModel(SVMModelProto) - Static method in class org.tribuo.common.libsvm.LibSVMModel
-
Deserializes a LibSVM svm_model from a protobuf.
- deserializeParameters(SVMParameterProto) - Static method in class org.tribuo.common.libsvm.LibSVMModel
-
Deserializes svm_parameter from a protobuf.
- determinant() - Method in class org.tribuo.math.la.DenseMatrix.CholeskyFactorization
-
Compute the matrix determinant of the factorized matrix.
- determinant() - Method in class org.tribuo.math.la.DenseMatrix.EigenDecomposition
-
Computes the determinant of the matrix which was decomposed.
- determinant() - Method in class org.tribuo.math.la.DenseMatrix.LUFactorization
-
Compute the matrix determinant of the factorized matrix.
- determinant() - Method in interface org.tribuo.math.la.Matrix.Factorization
-
Compute the matrix determinant of the factorized matrix.
- diagonal() - Method in class org.tribuo.math.la.DenseMatrix.EigenDecomposition
-
The diagonal vector of the tridiagonal form.
- difference(SparseVector) - Method in class org.tribuo.math.la.SparseVector
-
Generates an array of the indices that are active in this vector but are not present in
other
. - differencesIndices(double[]) - Static method in class org.tribuo.util.Util
-
Returns an array containing the indices where values are different.
- differencesIndices(double[], double) - Static method in class org.tribuo.util.Util
-
Returns an array containing the indices where values are different.
- diffProvenance(ModelProvenance, ModelProvenance) - Static method in class org.tribuo.reproducibility.ReproUtil
-
Creates a JSON String diff of two
ModelProvenance
objects. - dim1 - Variable in class org.tribuo.math.la.DenseMatrix
-
The number of rows.
- dim1() - Method in class org.tribuo.math.la.DenseMatrix.CholeskyFactorization
- dim1() - Method in class org.tribuo.math.la.DenseMatrix.EigenDecomposition
- dim1() - Method in class org.tribuo.math.la.DenseMatrix.LUFactorization
- dim1() - Method in interface org.tribuo.math.la.Matrix.Factorization
-
First dimension of the factorized matrix.
- dim2 - Variable in class org.tribuo.math.la.DenseMatrix
-
The number of columns.
- dim2() - Method in class org.tribuo.math.la.DenseMatrix.CholeskyFactorization
- dim2() - Method in class org.tribuo.math.la.DenseMatrix.EigenDecomposition
- dim2() - Method in class org.tribuo.math.la.DenseMatrix.LUFactorization
- dim2() - Method in interface org.tribuo.math.la.Matrix.Factorization
-
Second dimension of the factorized matrix.
- DIMENSIONALITY_REDUCTION - Enum constant in enum class org.tribuo.interop.oci.OCIUtil.OCIModelType
-
Dimensionality reduction, no Tribuo mapping.
- dimensions - Variable in class org.tribuo.regression.impl.SkeletalIndependentRegressionModel
-
The output dimension names.
- dimensions - Variable in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel
-
The output dimension names.
- DimensionTuple(String, double) - Constructor for class org.tribuo.regression.Regressor.DimensionTuple
-
Creates a dimension tuple from the supplied name and value.
- DimensionTuple(String, double, double) - Constructor for class org.tribuo.regression.Regressor.DimensionTuple
-
Creates a dimension tuple from the supplied name, value and variance.
- directory - Variable in class org.tribuo.classification.experiments.RunAll.RunAllOptions
-
Directory to write out the models and test reports.
- DirectoryFileSource<T extends Output<T>> - Class in org.tribuo.data.text
-
A data source for a somewhat-common format for text classification datasets: a top level directory that contains a number of subdirectories.
- DirectoryFileSource() - Constructor for class org.tribuo.data.text.DirectoryFileSource
-
for olcut
- DirectoryFileSource(Path, OutputFactory<T>, TextFeatureExtractor<T>, DocumentPreprocessor...) - Constructor for class org.tribuo.data.text.DirectoryFileSource
-
Creates a data source that will use the given feature extractor and document preprocessors on the data read from the files in the directories representing classes.
- DirectoryFileSource.DirectoryFileSourceProvenance - Class in org.tribuo.data.text
-
Provenance for
DirectoryFileSource
. - DirectoryFileSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.text.DirectoryFileSource.DirectoryFileSourceProvenance
-
Deserialization constructor.
- distance(SGDVector, DoubleUnaryOperator, DoubleUnaryOperator) - Method in class org.tribuo.math.la.SparseVector
-
Computes the distance between this vector and the other vector.
- Distance - Interface in org.tribuo.math.distance
-
Interface for distance functions.
- DISTANCE_DELTA - Static variable in class org.tribuo.classification.explanations.lime.LIMEBase
-
Delta to consider two distances equal.
- DistanceType - Enum Class in org.tribuo.math.distance
-
The built-in distance functions.
- distributionEquals(Prediction<T>) - Method in class org.tribuo.Prediction
-
Checks that the other prediction has the same distribution as this prediction, using the
Output.fullEquals(T)
method. - distributionEquals(Prediction<T>, double) - Method in class org.tribuo.Prediction
-
Checks that the other prediction has the same distribution as this prediction, using the
Output.fullEquals(T)
method. - distType - Variable in class org.tribuo.clustering.hdbscan.HdbscanOptions
-
Distance function in HDBSCAN*.
- distType - Variable in class org.tribuo.clustering.kmeans.KMeansOptions
-
Distance function in K-Means.
- distType - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
-
Distance function to use in the e step.
- distType - Variable in class org.tribuo.common.nearest.KNNClassifierOptions
-
Distance metric to use.
- div - Enum constant in enum class org.tribuo.transform.transformations.SimpleTransform.Operation
-
Divides by the specified constant.
- div(double) - Static method in class org.tribuo.transform.transformations.SimpleTransform
-
Generate a SimpleTransform that divides each value by the operand.
- DIV - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators
-
Element-wise division with broadcasting.
- DMatrixTuple(DMatrix, int[], Example<T>[]) - Constructor for class org.tribuo.common.xgboost.XGBoostTrainer.DMatrixTuple
-
Constructs a tuple containing the data and some Tribuo metadata.
- DocumentPreprocessor - Interface in org.tribuo.data.text
-
An interface for things that can pre-process documents before they are broken into features.
- domain - Variable in enum class org.tribuo.util.onnx.ONNXOperators
-
The operator domain (used for the ML operators).
- domainAndIDEquals(ImmutableOutputInfo<Event>) - Method in class org.tribuo.anomaly.ImmutableAnomalyInfo
- domainAndIDEquals(ImmutableOutputInfo<Label>) - Method in class org.tribuo.classification.ImmutableLabelInfo
- domainAndIDEquals(ImmutableOutputInfo<ClusterID>) - Method in class org.tribuo.clustering.ImmutableClusteringInfo
- domainAndIDEquals(ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
- domainAndIDEquals(ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.regression.ImmutableRegressionInfo
- domainAndIDEquals(ImmutableOutputInfo<T>) - Method in interface org.tribuo.ImmutableOutputInfo
-
Checks if the domain is the same as the other output info's domain, and that each element is mapped to the same id number.
- domainEquals(FeatureMap) - Method in class org.tribuo.FeatureMap
-
Check if this feature map contains the same features as the supplied one.
- dot(SGDVector) - Method in class org.tribuo.math.la.DenseVector
- dot(SGDVector) - Method in interface org.tribuo.math.la.SGDVector
-
Calculates the dot product between this vector and
other
. - dot(SGDVector) - Method in class org.tribuo.math.la.SparseVector
- dot(SGDVector) - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
- DOUBLE - Enum constant in enum class org.tribuo.datasource.IDXDataSource.IDXType
-
A 64-bit float.
- DoubleExtractor - Class in org.tribuo.data.columnar.extractors
-
Extracts the field value and converts it to a double.
- DoubleExtractor(String) - Constructor for class org.tribuo.data.columnar.extractors.DoubleExtractor
-
Extracts a double value from the supplied field name.
- DoubleExtractor(String, String) - Constructor for class org.tribuo.data.columnar.extractors.DoubleExtractor
-
Extracts a double value from the supplied field name.
- DoubleFieldProcessor - Class in org.tribuo.data.columnar.processors.field
-
Processes a column that contains a real value.
- DoubleFieldProcessor(String) - Constructor for class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
-
Constructs a field processor which extracts a single double valued feature from the specified field name.
- DoubleFieldProcessor(String, boolean) - Constructor for class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
-
Constructs a field processor which extracts a single double valued feature from the specified field name.
- DoubleFieldProcessor(String, boolean, boolean) - Constructor for class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
-
Constructs a field processor which extracts a single double valued feature from the specified field name.
- doubleTensorBuilder(ONNXContext, String, List<Integer>, Consumer<DoubleBuffer>) - Static method in class org.tribuo.util.onnx.ONNXUtils
-
Generic method to create double
OnnxMl.TensorProto
instances. - dropInvalidExamples - Variable in class org.tribuo.ImmutableDataset
-
If true, instead of throwing an exception when an invalid
Example
is encountered, this Dataset will log a warning and drop it. - dsConfig - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelArtifactConfig
-
The OCI Data Science config.
- dsConfig - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelDeploymentConfig
-
The OCI Data Science config.
- DTYPE - Static variable in class org.tribuo.interop.tensorflow.TensorFlowUtil
-
The name of the data type.
- DummyClassifierModel - Class in org.tribuo.classification.baseline
-
A model which performs dummy classifications (e.g., constant output, uniform sampled labels, stratified sampled labels).
- DummyClassifierTrainer - Class in org.tribuo.classification.baseline
-
A trainer for simple baseline classifiers.
- DummyClassifierTrainer.DummyType - Enum Class in org.tribuo.classification.baseline
-
Types of dummy classifier.
- DummyRegressionModel - Class in org.tribuo.regression.baseline
-
A model which performs dummy regressions (e.g., constant output, gaussian sampled output, mean value, median, quartile).
- DummyRegressionTrainer - Class in org.tribuo.regression.baseline
-
A trainer for simple baseline regressors.
- DummyRegressionTrainer.DummyRegressionTrainerProvenance - Class in org.tribuo.regression.baseline
-
Deprecated.
- DummyRegressionTrainer.DummyType - Enum Class in org.tribuo.regression.baseline
-
Types of dummy regression model.
- DummyRegressionTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance
-
Deprecated.Constructs a provenance from the marshalled form.
- DummyRegressionTrainerProvenance(DummyRegressionTrainer) - Constructor for class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance
-
Deprecated.Constructs a provenance from the host.
E
- eigenDecomposition() - Method in class org.tribuo.math.la.DenseMatrix
-
Eigen decomposition of a symmetric matrix.
- eigenvalues() - Method in class org.tribuo.math.la.DenseMatrix.EigenDecomposition
-
The vector of eigenvalues, in descending order.
- eigenvectors() - Method in class org.tribuo.math.la.DenseMatrix.EigenDecomposition
-
The eigenvectors for each eigenvalue, stored in the columns of the matrix.
- ELASTICNET - Enum constant in enum class org.tribuo.regression.slm.TrainTest.SLMType
-
Creates an
ElasticNetCDTrainer
. - ElasticNetCDTrainer - Class in org.tribuo.regression.slm
-
An ElasticNet trainer that uses co-ordinate descent.
- ElasticNetCDTrainer(double, double) - Constructor for class org.tribuo.regression.slm.ElasticNetCDTrainer
-
Constructs an elastic net trainer using the supplied parameters, with a tolerance of 1e-4, max iterations of 500, randomising the feature choice and using
Trainer.DEFAULT_SEED
as the RNG seed. - ElasticNetCDTrainer(double, double, double, int, boolean, long) - Constructor for class org.tribuo.regression.slm.ElasticNetCDTrainer
-
Constructs an elastic net trainer using the supplied parameters.
- ElasticNetCDTrainer(double, double, long) - Constructor for class org.tribuo.regression.slm.ElasticNetCDTrainer
-
Constructs an elastic net trainer using the supplied parameters, with a tolerance of 1e-4, max iterations of 500, and randomising the feature choice.
- elements - Variable in class org.tribuo.math.la.DenseVector
-
The value array.
- EmptyDatasetProvenance - Class in org.tribuo.provenance.impl
-
An empty DatasetProvenance, should not be used except by the provenance removal system.
- EmptyDatasetProvenance() - Constructor for class org.tribuo.provenance.impl.EmptyDatasetProvenance
-
An empty dataset provenance.
- EmptyDatasetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.impl.EmptyDatasetProvenance
-
Deserialization constructor.
- EmptyDataSourceProvenance - Class in org.tribuo.provenance.impl
-
An empty DataSourceProvenance, should not be used except by the provenance removal system.
- EmptyDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.impl.EmptyDataSourceProvenance
-
Deserialization constructor.
- emptyExample() - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator
-
Generates an example with no features.
- emptyExample() - Static method in class org.tribuo.classification.example.LabelledDataGenerator
-
Generates an example with no features.
- emptyExample() - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator
-
Generates an example with no features.
- emptyExample() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator
-
Generates an example with no features.
- emptyExample() - Static method in class org.tribuo.regression.example.RegressionDataGenerator
-
Generates an example with no features.
- emptyMultiDimExample() - Static method in class org.tribuo.regression.example.RegressionDataGenerator
-
Generates an example with no features.
- EmptyResponseProcessor<T extends Output<T>> - Class in org.tribuo.data.columnar.processors.response
-
A
ResponseProcessor
that always emits an empty optional. - EmptyResponseProcessor(OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.EmptyResponseProcessor
-
Constructs a response processor which never emits a response.
- EmptyTrainerProvenance - Class in org.tribuo.provenance.impl
-
An empty TrainerProvenance, should not be used except by the provenance removal system.
- EmptyTrainerProvenance() - Constructor for class org.tribuo.provenance.impl.EmptyTrainerProvenance
-
Constructs an empty trainer provenance.
- EmptyTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.impl.EmptyTrainerProvenance
-
Deserialization constructor.
- encode(List<? extends SequenceExample<?>>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceFeatureConverter
-
Encodes a batch of examples as a feed dict.
- encode(List<SequenceExample<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputConverter
-
Encodes a batch of labels as a feed dict.
- encode(SequenceExample<?>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceFeatureConverter
-
Encodes an example as a feed dict.
- encode(SequenceExample<T>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputConverter
-
Encodes an example's label as a feed dict.
- end - Variable in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence
-
The subsequence end index.
- end - Variable in class org.tribuo.util.tokens.Token
-
The end index.
- end - Variable in class org.tribuo.util.tokens.universal.Range
-
The end index.
- endpointDomain - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions
-
The OCI endpoint domain.
- ensemble - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
-
Options for classifier ensembles.
- EnsembleCombiner<T extends Output<T>> - Interface in org.tribuo.ensemble
-
An interface for combining predictions.
- EnsembleExcuse<T extends Output<T>> - Class in org.tribuo.ensemble
-
An
Excuse
which has a List of excuses for each of the ensemble members. - EnsembleExcuse(Example<T>, Prediction<T>, Map<String, List<Pair<String, Double>>>, List<Excuse<T>>) - Constructor for class org.tribuo.ensemble.EnsembleExcuse
-
Constructs an ensemble excuse, comprising the excuses from each ensemble member, along with the feature weights.
- EnsembleModel<T extends Output<T>> - Class in org.tribuo.ensemble
-
A model which contains a list of other
Model
s. - EnsembleModel(String, EnsembleModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<Model<T>>) - Constructor for class org.tribuo.ensemble.EnsembleModel
-
Builds an EnsembleModel from the supplied model list.
- EnsembleModelProvenance - Class in org.tribuo.provenance
-
Model provenance for ensemble models.
- EnsembleModelProvenance(String, OffsetDateTime, DatasetProvenance, TrainerProvenance, ListProvenance<? extends ModelProvenance>) - Constructor for class org.tribuo.provenance.EnsembleModelProvenance
-
Creates a provenance for an ensemble model tracking the class name, creation time, dataset provenance and trainer provenance along with the individual model provenances for each ensemble member.
- EnsembleModelProvenance(String, OffsetDateTime, DatasetProvenance, TrainerProvenance, Map<String, Provenance>, boolean, ListProvenance<? extends ModelProvenance>) - Constructor for class org.tribuo.provenance.EnsembleModelProvenance
-
Creates a provenance for an ensemble model tracking the class name, creation time, dataset provenance, trainer provenance and any instance specific provenance along with the individual model provenances for each ensemble member.
- EnsembleModelProvenance(String, OffsetDateTime, DatasetProvenance, TrainerProvenance, Map<String, Provenance>, ListProvenance<? extends ModelProvenance>) - Constructor for class org.tribuo.provenance.EnsembleModelProvenance
-
Creates a provenance for an ensemble model tracking the class name, creation time, dataset provenance, trainer provenance and any instance specific provenance along with the individual model provenances for each ensemble member.
- EnsembleModelProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.EnsembleModelProvenance
-
Used by the provenance unmarshalling system.
- ensembleName() - Method in class org.tribuo.common.tree.ExtraTreesTrainer
- ensembleName() - Method in class org.tribuo.common.tree.RandomForestTrainer
- ensembleName() - Method in class org.tribuo.ensemble.BaggingTrainer
-
Default name of the generated ensemble.
- ensembleOptions - Variable in class org.tribuo.classification.liblinear.TrainTest.TrainTestOptions
-
The ensemble options.
- ensembleOptions - Variable in class org.tribuo.classification.libsvm.TrainTest.TrainTestOptions
-
The ensemble options.
- ensembleOptions - Variable in class org.tribuo.classification.mnb.TrainTest.TrainTestOptions
-
The ensemble options.
- ensembleOptions - Variable in class org.tribuo.classification.sgd.fm.TrainTest.TrainTestOptions
-
The ensemble options.
- ensembleOptions - Variable in class org.tribuo.classification.sgd.TrainTest.TrainTestOptions
-
The ensemble options.
- ensembleSize - Variable in class org.tribuo.classification.ensemble.ClassificationEnsembleOptions
-
Number of base learners in the ensemble.
- ensembleSize - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
-
Number of trees in the ensemble.
- ensembleSize - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
-
Number of trees in the ensemble.
- entropy(List<T>) - Static method in class org.tribuo.util.infotheory.InformationTheory
-
Calculates the discrete Shannon entropy, using histogram probability estimators.
- Entropy - Class in org.tribuo.classification.dtree.impurity
-
A log_e entropy impurity measure.
- Entropy() - Constructor for class org.tribuo.classification.dtree.impurity.Entropy
- ENTROPY - Enum constant in enum class org.tribuo.classification.dtree.CARTClassificationOptions.ImpurityType
-
Uses
Entropy
. - entrySet() - Method in class org.tribuo.transform.TransformerMap
-
Get the feature names and associated list of transformers.
- epochs - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
-
Number of SGD epochs.
- epochs - Variable in class org.tribuo.common.sgd.AbstractSGDTrainer
- epochs - Variable in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceTrainer
- epochs - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
-
Number of gradient descent epochs.
- epochs - Variable in class org.tribuo.regression.sgd.fm.TrainTest.FMRegressionOptions
-
Number of SGD epochs.
- epochs - Variable in class org.tribuo.regression.sgd.TrainTest.SGDOptions
-
Number of SGD epochs.
- epsilon - Variable in class org.tribuo.common.liblinear.LibLinearTrainer
- epsilon - Variable in class org.tribuo.math.optimisers.GradientOptimiserOptions
-
Epsilon for AdaDelta, AdaGrad, AdaGradRDA, Adam.
- epsilon - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
-
Regression value insensitivity for margin.
- EPSILON - Static variable in class org.tribuo.transform.transformations.SimpleTransform
-
Epsilon for determining when two double values are the same.
- EPSILON_SVR - Enum constant in enum class org.tribuo.regression.libsvm.SVMRegressionType.SVMMode
-
epsilon-insensitive SVR.
- EQUAL_FREQUENCY - Enum constant in enum class org.tribuo.transform.transformations.BinningTransformation.BinningType
-
Creates bins of equal frequency (i.e., equal numbers of data points).
- EQUAL_WIDTH - Enum constant in enum class org.tribuo.transform.transformations.BinningTransformation.BinningType
-
Creates bins of equal width over the data range.
- equalFrequency(int) - Static method in class org.tribuo.transform.transformations.BinningTransformation
-
Returns a BinningTransformation which generates bins which contain the same amount of training data that is, each bin has an equal probability of occurrence in the training data.
- equals(Object) - Method in class org.tribuo.anomaly.AnomalyFactory.AnomalyFactoryProvenance
- equals(Object) - Method in class org.tribuo.anomaly.AnomalyFactory
- equals(Object) - Method in class org.tribuo.anomaly.AnomalyInfo
- equals(Object) - Method in class org.tribuo.anomaly.Event
- equals(Object) - Method in class org.tribuo.CategoricalIDInfo
- equals(Object) - Method in class org.tribuo.CategoricalInfo
- equals(Object) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
- equals(Object) - Method in class org.tribuo.classification.ensemble.VotingCombiner
- equals(Object) - Method in class org.tribuo.classification.evaluation.LabelMetric
- equals(Object) - Method in class org.tribuo.classification.ImmutableLabelInfo
- equals(Object) - Method in class org.tribuo.classification.Label
- equals(Object) - Method in class org.tribuo.classification.LabelFactory
- equals(Object) - Method in class org.tribuo.classification.LabelFactory.LabelFactoryProvenance
- equals(Object) - Method in class org.tribuo.classification.MutableLabelInfo
- equals(Object) - Method in class org.tribuo.clustering.ClusterID
- equals(Object) - Method in class org.tribuo.clustering.ClusteringFactory.ClusteringFactoryProvenance
- equals(Object) - Method in class org.tribuo.clustering.ClusteringFactory
- equals(Object) - Method in class org.tribuo.clustering.ClusteringInfo
- equals(Object) - Method in class org.tribuo.clustering.hdbscan.HdbscanTrainer.ClusterExemplar
- equals(Object) - Method in class org.tribuo.common.tree.LeafNode
- equals(Object) - Method in class org.tribuo.common.tree.SplitNode
- equals(Object) - Method in class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance
- equals(Object) - Method in class org.tribuo.data.csv.CSVLoader.CSVLoaderProvenance
-
Deprecated.
- equals(Object) - Method in class org.tribuo.data.sql.SQLDataSource.SQLDataSourceProvenance
- equals(Object) - Method in class org.tribuo.data.text.DirectoryFileSource.DirectoryFileSourceProvenance
- equals(Object) - Method in class org.tribuo.data.text.impl.SimpleStringDataSource.SimpleStringDataSourceProvenance
- equals(Object) - Method in class org.tribuo.data.text.impl.SimpleTextDataSource.SimpleTextDataSourceProvenance
- equals(Object) - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance
- equals(Object) - Method in class org.tribuo.dataset.MinimumCardinalityDataset.MinimumCardinalityDatasetProvenance
- equals(Object) - Method in class org.tribuo.dataset.SelectedFeatureDataset.SelectedFeatureDatasetProvenance
- equals(Object) - Method in class org.tribuo.datasource.AggregateDataSource.AggregateDataSourceProvenance
- equals(Object) - Method in class org.tribuo.datasource.LibSVMDataSource.LibSVMDataSourceProvenance
- equals(Object) - Method in class org.tribuo.evaluation.DescriptiveStats
- equals(Object) - Method in class org.tribuo.evaluation.metrics.MetricTarget
- equals(Object) - Method in class org.tribuo.evaluation.TrainTestSplitter.SplitDataSourceProvenance
- equals(Object) - Method in class org.tribuo.Feature
- equals(Object) - Method in class org.tribuo.FeatureMap
- equals(Object) - Method in class org.tribuo.hash.HashCodeHasher
- equals(Object) - Method in class org.tribuo.hash.HashCodeHasher.HashCodeHasherProvenance
- equals(Object) - Method in class org.tribuo.hash.MessageDigestHasher
- equals(Object) - Method in class org.tribuo.hash.MessageDigestHasher.MessageDigestHasherProvenance
- equals(Object) - Method in class org.tribuo.hash.ModHashCodeHasher
- equals(Object) - Method in class org.tribuo.hash.ModHashCodeHasher.ModHashCodeHasherProvenance
- equals(Object) - Method in class org.tribuo.impl.ArrayExample
- equals(Object) - Method in class org.tribuo.impl.BinaryFeaturesExample
- equals(Object) - Method in class org.tribuo.impl.DatasetDataCarrier
- equals(Object) - Method in class org.tribuo.impl.IndexedArrayExample
- equals(Object) - Method in class org.tribuo.impl.ListExample
- equals(Object) - Method in class org.tribuo.impl.ModelDataCarrier
- equals(Object) - Method in class org.tribuo.interop.ExternalTrainerProvenance
- equals(Object) - Method in class org.tribuo.interop.modelcard.ModelCard
- equals(Object) - Method in class org.tribuo.interop.modelcard.ModelDetails
- equals(Object) - Method in class org.tribuo.interop.modelcard.TestingDetails
- equals(Object) - Method in class org.tribuo.interop.modelcard.TrainingDetails
- equals(Object) - Method in class org.tribuo.interop.modelcard.UsageDetails
- equals(Object) - Method in class org.tribuo.interop.modelcard.UsageDetailsBuilder
- equals(Object) - Method in class org.tribuo.interop.oci.OCILabelConverter
- equals(Object) - Method in class org.tribuo.interop.oci.OCIMultiLabelConverter
- equals(Object) - Method in class org.tribuo.interop.oci.OCIRegressorConverter
- equals(Object) - Method in class org.tribuo.interop.onnx.DenseTransformer
- equals(Object) - Method in class org.tribuo.interop.onnx.ImageTransformer
- equals(Object) - Method in class org.tribuo.interop.onnx.LabelTransformer
- equals(Object) - Method in class org.tribuo.interop.onnx.MultiLabelTransformer
- equals(Object) - Method in class org.tribuo.interop.onnx.RegressorTransformer
- equals(Object) - Method in class org.tribuo.interop.tensorflow.TensorFlowTrainer.TensorFlowTrainerProvenance
- equals(Object) - Method in class org.tribuo.json.JsonDataSource.JsonDataSourceProvenance
- equals(Object) - Method in class org.tribuo.math.distance.CosineDistance
- equals(Object) - Method in class org.tribuo.math.distance.L1Distance
- equals(Object) - Method in class org.tribuo.math.distance.L2Distance
- equals(Object) - Method in class org.tribuo.math.kernel.Linear
- equals(Object) - Method in class org.tribuo.math.kernel.Polynomial
- equals(Object) - Method in class org.tribuo.math.kernel.RBF
- equals(Object) - Method in class org.tribuo.math.kernel.Sigmoid
- equals(Object) - Method in class org.tribuo.math.la.DenseMatrix
- equals(Object) - Method in class org.tribuo.math.la.DenseSparseMatrix
- equals(Object) - Method in class org.tribuo.math.la.DenseVector
-
Equals is defined mathematically, that is two SGDVectors are equal iff they have the same indices and the same values at those indices.
- equals(Object) - Method in class org.tribuo.math.la.MatrixTuple
- equals(Object) - Method in class org.tribuo.math.la.SparseVector
-
Equals is defined mathematically, that is two SGDVectors are equal iff they have the same indices and the same values at those indices.
- equals(Object) - Method in class org.tribuo.math.la.VectorTuple
- equals(Object) - Method in class org.tribuo.math.LinearParameters
- equals(Object) - Method in class org.tribuo.math.neighbour.bruteforce.NeighboursBruteForceFactory
- equals(Object) - Method in class org.tribuo.math.neighbour.kdtree.KDTreeFactory
- equals(Object) - Method in class org.tribuo.math.util.ExpNormalizer
- equals(Object) - Method in class org.tribuo.math.util.HeapMerger
- equals(Object) - Method in class org.tribuo.math.util.MatrixHeapMerger
- equals(Object) - Method in class org.tribuo.math.util.NoopNormalizer
- equals(Object) - Method in class org.tribuo.math.util.Normalizer
- equals(Object) - Method in class org.tribuo.math.util.SigmoidNormalizer
- equals(Object) - Method in class org.tribuo.multilabel.ensemble.MultiLabelVotingCombiner
- equals(Object) - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric
- equals(Object) - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
- equals(Object) - Method in class org.tribuo.multilabel.MultiLabel
- equals(Object) - Method in class org.tribuo.multilabel.MultiLabelFactory
- equals(Object) - Method in class org.tribuo.multilabel.MultiLabelFactory.MultiLabelFactoryProvenance
- equals(Object) - Method in class org.tribuo.multilabel.MultiLabelInfo
- equals(Object) - Method in class org.tribuo.MutableFeatureMap
- equals(Object) - Method in class org.tribuo.provenance.DatasetProvenance
- equals(Object) - Method in class org.tribuo.provenance.EnsembleModelProvenance
- equals(Object) - Method in class org.tribuo.provenance.EvaluationProvenance
- equals(Object) - Method in class org.tribuo.provenance.FeatureSetProvenance
- equals(Object) - Method in class org.tribuo.provenance.impl.EmptyDataSourceProvenance
- equals(Object) - Method in class org.tribuo.provenance.impl.EmptyTrainerProvenance
- equals(Object) - Method in class org.tribuo.provenance.impl.TimestampedTrainerProvenance
- equals(Object) - Method in class org.tribuo.provenance.ModelProvenance
- equals(Object) - Method in class org.tribuo.provenance.SimpleDataSourceProvenance
- equals(Object) - Method in class org.tribuo.provenance.SkeletalTrainerProvenance
- equals(Object) - Method in class org.tribuo.RealIDInfo
- equals(Object) - Method in class org.tribuo.RealInfo
- equals(Object) - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance
-
Deprecated.
- equals(Object) - Method in class org.tribuo.regression.ensemble.AveragingCombiner
- equals(Object) - Method in class org.tribuo.regression.ImmutableRegressionInfo
- equals(Object) - Method in class org.tribuo.regression.MutableRegressionInfo
- equals(Object) - Method in class org.tribuo.regression.RegressionFactory
- equals(Object) - Method in class org.tribuo.regression.RegressionFactory.RegressionFactoryProvenance
- equals(Object) - Method in class org.tribuo.regression.Regressor.DimensionTuple
- equals(Object) - Method in class org.tribuo.regression.Regressor
-
Regressors are equal if they have the same number of dimensions and equal dimension names.
- equals(Object) - Method in record class org.tribuo.reproducibility.ReproUtil.FeatureDiff
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in record class org.tribuo.reproducibility.ReproUtil.ModelReproduction
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in record class org.tribuo.reproducibility.ReproUtil.OutputDiff
-
Indicates whether some other object is "equal to" this one.
- equals(Object) - Method in class org.tribuo.SelectedFeatureSet
-
Checks if this
SelectedFeatureSet
is equal to the supplied object. - equals(Object) - Method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset.MinimumCardinalitySequenceDatasetProvenance
- equals(Object) - Method in class org.tribuo.sequence.SequenceExample
- equals(Object) - Method in class org.tribuo.SkeletalVariableInfo
- equals(Object) - Method in class org.tribuo.transform.TransformationMap.TransformationList
- equals(Object) - Method in class org.tribuo.transform.transformations.BinningTransformation.BinningTransformationProvenance
- equals(Object) - Method in class org.tribuo.transform.transformations.BinningTransformation.BinningTransformer
- equals(Object) - Method in class org.tribuo.transform.transformations.LinearScalingTransformation.LinearScalingTransformationProvenance
- equals(Object) - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation.MeanStdDevTransformationProvenance
- equals(Object) - Method in class org.tribuo.transform.transformations.SimpleTransform
- equals(Object) - Method in class org.tribuo.transform.transformations.SimpleTransform.SimpleTransformProvenance
- equals(Object) - Method in class org.tribuo.transform.TransformerMap
- equals(Object) - Method in class org.tribuo.transform.TransformerMap.TransformerMapProvenance
- equals(Object) - Method in class org.tribuo.util.infotheory.impl.CachedPair
- equals(Object) - Method in class org.tribuo.util.infotheory.impl.CachedTriple
- equals(Object) - Method in class org.tribuo.util.infotheory.impl.Row
- equals(Object) - Method in class org.tribuo.util.infotheory.impl.WeightCountTuple
- equals(Object) - Method in class org.tribuo.util.IntDoublePair
- equals(Object) - Method in class org.tribuo.util.MeanVarianceAccumulator
- equalWidth(int) - Static method in class org.tribuo.transform.transformations.BinningTransformation
-
Returns a BinningTransformation which generates fixed equal width bins between the observed min and max values.
- eta - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
-
Step size shrinkage parameter (default 0.3, range [0,1]).
- eta - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
-
Step size shrinkage parameter (default 0.3, range [0,1]).
- EUCLIDEAN - Enum constant in enum class org.tribuo.clustering.hdbscan.HdbscanTrainer.Distance
-
Deprecated.Euclidean (or l2) distance.
- EUCLIDEAN - Enum constant in enum class org.tribuo.clustering.kmeans.KMeansTrainer.Distance
-
Deprecated.Euclidean (or l2) distance.
- euclideanDistance(SGDVector) - Method in class org.tribuo.math.la.DenseVector
-
The l2 or euclidean distance between this vector and the other vector.
- euclideanDistance(SGDVector) - Method in interface org.tribuo.math.la.SGDVector
-
The l2 or euclidean distance between this vector and the other vector.
- euclideanDistance(SGDVector) - Method in class org.tribuo.math.la.SparseVector
- EV - Enum constant in enum class org.tribuo.regression.evaluation.RegressionMetrics
-
Calculates the Explained Variance of the predictions.
- evaluate() - Method in class org.tribuo.evaluation.CrossValidation
-
Performs k fold cross validation, returning the k evaluations.
- evaluate() - Method in class org.tribuo.evaluation.OnlineEvaluator
-
Creates an
Evaluation
containing all the current predictions. - evaluate(Model<T>, List<Prediction<T>>, List<T>, DataProvenance) - Method in interface org.tribuo.evaluation.Evaluator
-
Evaluates the model performance using the supplied predictions, returning an immutable
Evaluation
of the appropriate type. - evaluate(Model<T>, List<Prediction<T>>, DataProvenance) - Method in class org.tribuo.evaluation.AbstractEvaluator
-
Produces an evaluation for the supplied model and predictions by aggregating the appropriate statistics.
- evaluate(Model<T>, List<Prediction<T>>, DataProvenance) - Method in interface org.tribuo.evaluation.Evaluator
-
Evaluates the model performance using the supplied predictions, returning an immutable
Evaluation
of the appropriate type. - evaluate(Model<T>, Dataset<T>) - Method in class org.tribuo.evaluation.AbstractEvaluator
-
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. - evaluate(Model<T>, Dataset<T>) - Method in interface org.tribuo.evaluation.Evaluator
-
Evaluates the dataset using the supplied model, returning an immutable
Evaluation
of the appropriate type. - evaluate(Model<T>, DataSource<T>) - Method in class org.tribuo.evaluation.AbstractEvaluator
-
Produces an evaluation for the supplied model and datasource, by calling
Model.predict(org.tribuo.Example<T>)
to create the predictions, then aggregating the appropriate statistics. - evaluate(Model<T>, DataSource<T>) - Method in interface org.tribuo.evaluation.Evaluator
-
Evaluates the dataset using the supplied model, returning an immutable
Evaluation
of the appropriate type. - evaluate(SequenceModel<T>, List<List<Prediction<T>>>, DataProvenance) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
-
Produces an evaluation for the supplied model and predictions by aggregating the appropriate statistics.
- evaluate(SequenceModel<T>, List<List<Prediction<T>>>, DataProvenance) - Method in interface org.tribuo.sequence.SequenceEvaluator
-
Evaluates the supplied model and predictions by aggregating the appropriate statistics.
- evaluate(SequenceModel<T>, SequenceDataset<T>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
-
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. - evaluate(SequenceModel<T>, SequenceDataset<T>) - Method in interface org.tribuo.sequence.SequenceEvaluator
-
Evaluates the dataset using the supplied model, returning an immutable evaluation.
- evaluate(SequenceModel<T>, SequenceDataSource<T>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
-
Produces an evaluation for the supplied model and datasource, by calling
SequenceModel.predict(org.tribuo.sequence.SequenceExample<T>)
to create the predictions, then aggregating the appropriate statistics. - evaluate(SequenceModel<T>, SequenceDataSource<T>) - Method in interface org.tribuo.sequence.SequenceEvaluator
-
Evaluates the datasource using the supplied model, returning an immutable evaluation.
- Evaluation<T extends Output<T>> - Interface in org.tribuo.evaluation
-
An immutable evaluation of a specific model and dataset.
- EvaluationAggregator - Class in org.tribuo.evaluation
-
Aggregates metrics from a list of evaluations, or a list of models and datasets.
- EvaluationMetric<T extends Output<T>,
C extends MetricContext<T>> - Interface in org.tribuo.evaluation.metrics -
A metric that can be calculated for the specified output type.
- EvaluationMetric.Average - Enum Class in org.tribuo.evaluation.metrics
-
Specifies what form of average to use for a
EvaluationMetric
. - EvaluationProvenance - Class in org.tribuo.provenance
-
Provenance for evaluations.
- EvaluationProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.EvaluationProvenance
-
Deserialization constructor.
- EvaluationProvenance(ModelProvenance, DataProvenance) - Constructor for class org.tribuo.provenance.EvaluationProvenance
-
Constructs an evaluation provenance from the supplied provenances.
- EvaluationRenderer<T extends Output<T>,
E extends Evaluation<T>> - Interface in org.tribuo.evaluation -
Renders an
Evaluation
into a String. - evaluator - Static variable in class org.tribuo.classification.explanations.lime.LIMEBase
- Evaluator<T extends Output<T>,
E extends Evaluation<T>> - Interface in org.tribuo.evaluation -
An evaluation factory which produces immutable
Evaluation
s of a givenDataset
using the givenModel
. - EVEN_OR_ODD_DAY - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
-
The parity of the day of the year.
- EVEN_OR_ODD_MONTH - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
-
The parity of the month.
- EVEN_OR_ODD_WEEK - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
-
The parity of the week of the year as defined by ISO 8601.
- EVEN_OR_ODD_YEAR - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
-
The parity of the year.
- Event - Class in org.tribuo.anomaly
- Event(Event.EventType) - Constructor for class org.tribuo.anomaly.Event
-
Constructs a new event of the specified type with the default score of
Event.DEFAULT_SCORE
. - Event(Event.EventType, double) - Constructor for class org.tribuo.anomaly.Event
-
Constructs a new event of the specified type and score.
- Event.EventType - Enum Class in org.tribuo.anomaly
-
The type of event.
- EXACT - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.TreeMethod
-
Exact greedy algorithm, enumerates all split candidates.
- Example<T extends Output<T>> - Class in org.tribuo
-
An example used for training and evaluation.
- Example(Example<T>) - Constructor for class org.tribuo.Example
-
Copies the output, weight and metadata into this example.
- Example(T) - Constructor for class org.tribuo.Example
-
Construct an empty example using the supplied output and
Example.DEFAULT_WEIGHT
as the weight. - Example(T, float) - Constructor for class org.tribuo.Example
-
Construct an empty example using the supplied output and weight.
- Example(T, float, Map<String, Object>) - Constructor for class org.tribuo.Example
-
Construct an empty example using the supplied output, weight and metadata.
- Example(T, Map<String, Object>) - Constructor for class org.tribuo.Example
-
Construct an empty example using the supplied output, metadata and
Example.DEFAULT_WEIGHT
as the weight. - ExampleArray(SparseVector[], int[], double[]) - Constructor for class org.tribuo.classification.sgd.Util.ExampleArray
-
Constructs an example array.
- examples - Variable in class org.tribuo.classification.example.DemoLabelDataSource
- examples - Variable in class org.tribuo.common.xgboost.XGBoostTrainer.DMatrixTuple
-
The examples.
- exampleToNodes(Example<T>, ImmutableFeatureMap, List<FeatureNode>) - Static method in class org.tribuo.common.liblinear.LibLinearTrainer
-
Converts a Tribuo
Example
into a liblinearFeatureNode
array, including a bias feature. - exampleToNodes(Example<T>, ImmutableFeatureMap, List<svm_node>) - Static method in class org.tribuo.common.libsvm.LibSVMTrainer
-
Convert the example into an array of svm_node which represents a sparse feature vector.
- ExampleTransformer - Interface in org.tribuo.interop.onnx
-
Transforms a
SparseVector
, extracting the features from it as aOnnxTensor
. - excuse(String) - Method in class org.tribuo.Excuse
-
Returns the features involved in this excuse.
- Excuse<T extends Output<T>> - Class in org.tribuo
-
Holds an
Example
, aPrediction
and a Map from String to List of Pairs that contains the per output explanation. - Excuse(Example<T>, Prediction<T>, Map<String, List<Pair<String, Double>>>) - Constructor for class org.tribuo.Excuse
-
Constructs an excuse for the prediction of the supplied example, using the feature weights.
- exp - Enum constant in enum class org.tribuo.transform.transformations.SimpleTransform.Operation
-
Exponentiates the inputs
- exp() - Static method in class org.tribuo.transform.transformations.SimpleTransform
-
Generate a SimpleTransform that applies
Math.exp(double)
. - expandRegexMapping(Collection<String>) - Method in class org.tribuo.data.columnar.RowProcessor
-
Uses similar logic to
TransformationMap.validateTransformations(org.tribuo.FeatureMap)
to check the regexes against the supplied list of field names. - expandRegexMapping(ImmutableFeatureMap) - Method in class org.tribuo.data.columnar.RowProcessor
-
Uses similar logic to
TransformationMap.validateTransformations(org.tribuo.FeatureMap)
to check the regexes against the supplied feature map. - expandRegexMapping(Model<T>) - Method in class org.tribuo.data.columnar.RowProcessor
-
Uses similar logic to
TransformationMap.validateTransformations(org.tribuo.FeatureMap)
to check the regexes against theImmutableFeatureMap
contained in the suppliedModel
. - EXPECTED - Enum constant in enum class org.tribuo.anomaly.Event.EventType
-
An expected event, with id 0.
- EXPECTED_EVENT - Static variable in class org.tribuo.anomaly.AnomalyFactory
-
The expected event.
- expectedCount - Variable in class org.tribuo.anomaly.AnomalyInfo
-
The number of expected events observed.
- expectedMI(List<T>, List<T>) - Static method in class org.tribuo.util.infotheory.InformationTheory
-
Compute the expected mutual information assuming randomized inputs.
- explain(CommandInterpreter, String[]) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
-
Explains a text classification.
- explain(String) - Method in class org.tribuo.classification.explanations.lime.LIMEText
- explain(String) - Method in interface org.tribuo.classification.explanations.TextExplainer
- explain(Map<String, String>) - Method in interface org.tribuo.classification.explanations.ColumnarExplainer
-
Explains the supplied data.
- explain(Map<String, String>) - Method in class org.tribuo.classification.explanations.lime.LIMEColumnar
- explain(Example<Label>) - Method in class org.tribuo.classification.explanations.lime.LIMEBase
- explain(Example<Label>) - Method in interface org.tribuo.classification.explanations.TabularExplainer
-
Explain why the supplied
Example
is classified a certain way. - explainedVariance() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation
-
Calculatest the explained variance for all dimensions.
- explainedVariance(MetricTarget<Regressor>, RegressionSufficientStatistics) - Static method in enum class org.tribuo.regression.evaluation.RegressionMetrics
-
Calculates the explained variance based on the supplied statistics.
- explainedVariance(Regressor) - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation
-
Calculates the explained variance of the ground truth using the predictions for the supplied dimension.
- explainedVariance(Regressor, RegressionSufficientStatistics) - Static method in enum class org.tribuo.regression.evaluation.RegressionMetrics
-
Calculates the explained variance based on the supplied statistics for a single dimension.
- explainWithSamples(Map<String, String>) - Method in class org.tribuo.classification.explanations.lime.LIMEColumnar
- explainWithSamples(Example<Label>) - Method in class org.tribuo.classification.explanations.lime.LIMEBase
- Explanation<T extends Output<T>> - Interface in org.tribuo.classification.explanations
-
An explanation knows what features are used, what the explaining Model is and what the original Model's prediction is.
- explanationTrainer - Variable in class org.tribuo.classification.explanations.lime.LIMEBase
- expNormalize(double) - Method in class org.tribuo.math.la.DenseVector
-
An optimisation for the exponential normalizer when you already know the normalization constant.
- ExpNormalizer - Class in org.tribuo.math.util
-
Normalizes the exponential values of the input array.
- ExpNormalizer() - Constructor for class org.tribuo.math.util.ExpNormalizer
-
Constructs an ExpNormalizer.
- exportCombiner(ONNXNode) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
-
Exports this voting combiner to ONNX.
- exportCombiner(ONNXNode) - Method in class org.tribuo.classification.ensemble.VotingCombiner
-
Exports this voting combiner to ONNX.
- exportCombiner(ONNXNode) - Method in interface org.tribuo.ensemble.EnsembleCombiner
-
Exports this ensemble combiner into the ONNX context of its input.
- exportCombiner(ONNXNode) - Method in class org.tribuo.multilabel.ensemble.MultiLabelVotingCombiner
-
Exports this voting combiner to ONNX.
- exportCombiner(ONNXNode) - Method in class org.tribuo.regression.ensemble.AveragingCombiner
-
Exports this averaging combiner, writing constructed nodes into the
ONNXContext
governinginput
and returning the leaf node of the combiner. - exportCombiner(ONNXNode, T) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
-
Exports this voting combiner to ONNX.
- exportCombiner(ONNXNode, T) - Method in class org.tribuo.classification.ensemble.VotingCombiner
-
Exports this voting combiner to ONNX
- exportCombiner(ONNXNode, T) - Method in class org.tribuo.multilabel.ensemble.MultiLabelVotingCombiner
-
Exports this voting combiner to ONNX
- exportCombiner(ONNXNode, T) - Method in class org.tribuo.regression.ensemble.AveragingCombiner
-
Exports this averaging combiner, writing constructed nodes into the
ONNXContext
governinginput
and returning the leaf node of the combiner. - exportCombiner(ONNXNode, U) - Method in interface org.tribuo.ensemble.EnsembleCombiner
-
Exports this ensemble combiner into the ONNX context of its input.
- exportModel(String) - Method in class org.tribuo.interop.tensorflow.TensorFlowModel
-
Exports this model as a
SavedModelBundle
, writing to the supplied directory. - exportNormalizer(ONNXNode) - Method in class org.tribuo.math.util.ExpNormalizer
-
Returns the ONNX softmax node over the 2nd dimension.
- exportNormalizer(ONNXNode) - Method in class org.tribuo.math.util.NoopNormalizer
-
Returns its input.
- exportNormalizer(ONNXNode) - Method in class org.tribuo.math.util.Normalizer
-
Applies ONNX ReduceMin, Sub, ReduceSum, and Div operations to input.
- exportNormalizer(ONNXNode) - Method in class org.tribuo.math.util.SigmoidNormalizer
-
Returns the ONNX sigmoid node, operating independently over each element.
- exportNormalizer(ONNXNode) - Method in interface org.tribuo.math.util.VectorNormalizer
-
Exports this normalizer to ONNX, returning the leaf of the appended graph and writing the nodes needed for normalization into the
ONNXContext
thatinput
belongs to. - exportONNXModel(String, long) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationModel
- exportONNXModel(String, long) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationModel
- exportONNXModel(String, long) - Method in class org.tribuo.common.sgd.AbstractFMModel
-
Exports this
Model
as an ONNX protobuf. - exportONNXModel(String, long) - Method in class org.tribuo.common.sgd.AbstractLinearSGDModel
-
Exports this
Model
as an ONNX protobuf. - exportONNXModel(String, long) - Method in class org.tribuo.ensemble.WeightedEnsembleModel
-
Exports this
EnsembleModel
as an ONNX model. - exportONNXModel(String, long) - Method in interface org.tribuo.ONNXExportable
-
Exports this
Model
as an ONNX protobuf. - exportONNXModel(String, long) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionModel
- exportONNXModel(String, long) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionModel
- exportONNXModel(String, long) - Method in class org.tribuo.regression.slm.SparseLinearModel
- ExternalDatasetProvenance - Class in org.tribuo.interop
-
A dummy provenance used to describe the dataset of external models.
- ExternalDatasetProvenance(String, OutputFactory<T>, boolean, int, int) - Constructor for class org.tribuo.interop.ExternalDatasetProvenance
-
An empty provenance used as a placeholder for externally trained models.
- ExternalDatasetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.interop.ExternalDatasetProvenance
-
Deserialization constructor.
- ExternalModel<T extends Output<T>,
U, V> - Class in org.tribuo.interop -
This is the base class for third party models which are trained externally and loaded into Tribuo for prediction.
- ExternalModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, boolean, Map<String, Integer>) - Constructor for class org.tribuo.interop.ExternalModel
-
Constructs an external model from a model trained outside of Tribuo.
- ExternalModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, int[], int[], boolean) - Constructor for class org.tribuo.interop.ExternalModel
-
Constructs an external model from a model trained outside of Tribuo.
- externalPrediction(OnnxTensor) - Method in class org.tribuo.interop.onnx.ONNXExternalModel
-
Runs the session to make a prediction.
- externalPrediction(DMatrix) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel
- externalPrediction(TensorMap) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
-
Runs the session to make a prediction.
- externalPrediction(TensorMap) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
-
Runs the session to make a prediction.
- externalPrediction(DenseMatrix) - Method in class org.tribuo.interop.oci.OCIModel
- externalPrediction(U) - Method in class org.tribuo.interop.ExternalModel
-
Runs the external model's prediction function.
- ExternalTrainerProvenance - Class in org.tribuo.interop
-
A dummy provenance for a model trained outside Tribuo.
- ExternalTrainerProvenance(byte[]) - Constructor for class org.tribuo.interop.ExternalTrainerProvenance
-
Creates an external trainer provenance, computing the hash from the byte array and storing this instant as the timestamp, and the current working directory as the location.
- ExternalTrainerProvenance(URL) - Constructor for class org.tribuo.interop.ExternalTrainerProvenance
-
Creates an external trainer provenance, storing the location and pulling in the timestamp and file hash.
- ExternalTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.interop.ExternalTrainerProvenance
-
Used by the provenance serialization system.
- EXTRA_TREES - Enum constant in enum class org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType
-
Creates an
ExtraTreesTrainer
. - extract(LocalDate) - Method in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
-
Applies this enum's extraction function to the supplied date.
- extract(ColumnarIterator.Row) - Method in class org.tribuo.data.columnar.extractors.IndexExtractor
- extract(ColumnarIterator.Row) - Method in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor
- extract(ColumnarIterator.Row) - Method in interface org.tribuo.data.columnar.FieldExtractor
-
Returns Optional which is filled if extraction succeeded.
- extract(T, String) - Method in class org.tribuo.data.text.impl.TextFeatureExtractorImpl
- extract(T, String) - Method in interface org.tribuo.data.text.TextFeatureExtractor
-
Extracts an example from the supplied input text and output object.
- extract(T, String) - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
-
Tokenizes the input using the loaded tokenizer, truncates the token list if it's longer than
maxLength
- 2 (to account for [CLS] and [SEP] tokens), and then passes the token list toBERTFeatureExtractor.extractExample(java.util.List<java.lang.String>)
. - extractData(Dataset<Event>, ImmutableOutputInfo<Event>, ImmutableFeatureMap) - Method in class org.tribuo.anomaly.liblinear.LibLinearAnomalyTrainer
- extractData(Dataset<Event>, ImmutableOutputInfo<Event>, ImmutableFeatureMap) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer
- extractData(Dataset<Label>, ImmutableOutputInfo<Label>, ImmutableFeatureMap) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationTrainer
- extractData(Dataset<Label>, ImmutableOutputInfo<Label>, ImmutableFeatureMap) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationTrainer
- extractData(Dataset<Regressor>, ImmutableOutputInfo<Regressor>, ImmutableFeatureMap) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionTrainer
- extractData(Dataset<Regressor>, ImmutableOutputInfo<Regressor>, ImmutableFeatureMap) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionTrainer
- extractData(Dataset<T>, ImmutableOutputInfo<T>, ImmutableFeatureMap) - Method in class org.tribuo.common.liblinear.LibLinearTrainer
-
Extracts the features and
Output
s in LibLinear's format. - extractData(Dataset<T>, ImmutableOutputInfo<T>, ImmutableFeatureMap) - Method in class org.tribuo.common.libsvm.LibSVMTrainer
-
Extracts the features and
Output
s in LibSVM's format. - extractExample(List<String>) - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
-
Passes the tokens through BERT, replacing any unknown tokens with the [UNK] token.
- extractExample(List<String>, T) - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
-
Passes the tokens through BERT, replacing any unknown tokens with the [UNK] token.
- extractFeatures(List<Label>, double) - Method in class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
- extractFeatures(List<Label>, double) - Method in interface org.tribuo.classification.sequence.viterbi.LabelFeatureExtractor
-
Generates features based on the previously produced labels.
- extractFeatures(List<Label>, double) - Method in class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor
- extractField(String) - Method in class org.tribuo.data.columnar.extractors.DateExtractor
- extractField(String) - Method in class org.tribuo.data.columnar.extractors.DoubleExtractor
- extractField(String) - Method in class org.tribuo.data.columnar.extractors.FloatExtractor
- extractField(String) - Method in class org.tribuo.data.columnar.extractors.IdentityExtractor
- extractField(String) - Method in class org.tribuo.data.columnar.extractors.IntExtractor
- extractField(String) - Method in class org.tribuo.data.columnar.extractors.OffsetDateTimeExtractor
- extractField(String) - Method in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor
-
Extracts the field value, or returns
Optional.empty()
if it failed to parse. - extractMarshalledVariables(Graph, Session) - Static method in class org.tribuo.interop.tensorflow.TensorFlowUtil
-
Extracts a Map containing the name of each Tensorflow VariableV2 and the associated parameter array.
- extractNames(OutputInfo<Regressor>) - Static method in class org.tribuo.regression.Regressor
-
Extracts the names from the supplied Regressor domain in their canonical order.
- extractor - Variable in class org.tribuo.data.text.DirectoryFileSource
-
The extractor that we'll use to turn text into examples.
- extractor - Variable in class org.tribuo.data.text.TextDataSource
-
The extractor that we'll use to turn text into examples.
- extractOutput(Event) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer
-
Converts an output into a double for use in training.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.anomaly.example.GaussianAnomalyDataSource.GaussianAnomalyDataSourceProvenance
-
Extracts the relevant provenance information fields for this class.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.clustering.example.GaussianClusterDataSource.GaussianClusterDataSourceProvenance
-
Extracts the relevant provenance information fields for this class.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance
-
Separates this class's non-configurable fields from the configurable fields.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.sql.SQLDataSource.SQLDataSourceProvenance
-
Separates out the configured and non-configured provenance values.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.text.DirectoryFileSource.DirectoryFileSourceProvenance
-
Splits the provenance into configured and non-configured values.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.text.impl.SimpleStringDataSource.SimpleStringDataSourceProvenance
-
Separates out the configured and non-configured provenance values.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.text.impl.SimpleTextDataSource.SimpleTextDataSourceProvenance
-
Separates out the configured and non-configured provenance values.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.datasource.AggregateConfigurableDataSource.AggregateConfigurableDataSourceProvenance
-
Extracts the class name and host type fields from the provenance map.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
-
Separates out the configured and non-configured provenance values.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.datasource.LibSVMDataSource.LibSVMDataSourceProvenance
-
Separates out the configured and non-configured provenance values.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.json.JsonDataSource.JsonDataSourceProvenance
-
Splits the provenance into configurable and non-configurable provenances.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.multilabel.example.MultiLabelGaussianDataSource.MultiLabelGaussianDataSourceProvenance
-
Extracts the relevant provenance information fields for this class.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.provenance.impl.FeatureSelectorProvenanceImpl
-
Extracts the class name and host short name provenances.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.provenance.SkeletalTrainerProvenance
-
Extracts the provenance information from the supplied map, splitting it into configuration and instance information.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.regression.example.GaussianDataSource.GaussianDataSourceProvenance
-
Extracts the relevant provenance information fields for this class.
- extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.regression.example.NonlinearGaussianDataSource.NonlinearGaussianDataSourceProvenance
-
Extracts the relevant provenance information fields for this class.
- extractSequenceExample(List<String>, boolean) - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
-
Passes the tokens through BERT, replacing any unknown tokens with the [UNK] token.
- extractSequenceExample(List<String>, List<T>, boolean) - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
-
Passes the tokens through BERT, replacing any unknown tokens with the [UNK] token.
- extractTFProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceTrainer.TensorFlowSequenceTrainerProvenance
- extractTFProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.interop.tensorflow.TensorFlowTrainer.TensorFlowTrainerProvenance
- ExtraTreesTrainer<T extends Output<T>> - Class in org.tribuo.common.tree
-
A trainer which produces an Extremely Randomized Tree Ensemble.
- ExtraTreesTrainer(DecisionTreeTrainer<T>, EnsembleCombiner<T>, int) - Constructor for class org.tribuo.common.tree.ExtraTreesTrainer
-
Constructs an ExtraTreesTrainer with the default seed
Trainer.DEFAULT_SEED
. - ExtraTreesTrainer(DecisionTreeTrainer<T>, EnsembleCombiner<T>, int, long) - Constructor for class org.tribuo.common.tree.ExtraTreesTrainer
-
Constructs an ExtraTreesTrainer with the supplied seed, trainer, combining function and number of members.
F
- f1(double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
-
Computes the F_1 score.
- f1(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
-
The F1 for this label.
- f1(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
-
Computes the F_1 score.
- f1(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
- f1(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
-
Returns the F_1 score, i.e., the harmonic mean of the precision and recall.
- F1 - Enum constant in enum class org.tribuo.anomaly.evaluation.AnomalyMetrics
-
The F_1 score, i.e., the harmonic mean of the precision and the recall.
- F1 - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics
-
The F_1 score, i.e., the harmonic mean of the precision and the recall.
- F1 - Enum constant in enum class org.tribuo.multilabel.evaluation.MultiLabelMetrics
-
The F_1 score, i.e., the harmonic mean of the precision and the recall.
- FACTORIZATION_TOLERANCE - Static variable in class org.tribuo.math.la.DenseMatrix
-
Tolerance for non-zero diagonal values in the factorizations.
- factorizedDimSize - Variable in class org.tribuo.common.sgd.AbstractFMTrainer
- factors(List<String>) - Method in class org.tribuo.interop.modelcard.UsageDetailsBuilder
-
Sets the relevant factors of the model for which an instance of UsageDetails will be built.
- factorSize - Variable in class org.tribuo.regression.sgd.fm.TrainTest.FMRegressionOptions
-
Factor size.
- factory - Static variable in class org.tribuo.classification.example.DemoLabelDataSource
- Feature - Class in org.tribuo
-
A class for features.
- Feature(String, double) - Constructor for class org.tribuo.Feature
-
Creates an immutable feature.
- FEATURE_TYPE - Static variable in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
-
The name of the provenance field for the idx feature type.
- FEATURE_VALUE - Static variable in class org.tribuo.data.columnar.processors.field.IdentityProcessor
-
The value of the emitted features.
- FeatureAggregator - Interface in org.tribuo.data.text
-
An interface for aggregating feature values into other values.
- featureBackwardMapping - Variable in class org.tribuo.interop.ExternalModel
-
The backward mapping from the external indices to Tribuo's indices.
- featureConverter - Variable in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceModel
- featureConverter - Variable in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceTrainer
- featureConverter - Variable in class org.tribuo.interop.tensorflow.TensorFlowModel
- FeatureConverter - Interface in org.tribuo.interop.tensorflow
- featureDiff() - Method in record class org.tribuo.reproducibility.ReproUtil.ModelReproduction
-
Returns the value of the
featureDiff
record component. - FeatureDiff(Set<String>, Set<String>) - Constructor for record class org.tribuo.reproducibility.ReproUtil.FeatureDiff
-
Creates an instance of a
FeatureDiff
record class. - featureDomain() - Method in class org.tribuo.impl.DatasetDataCarrier
-
Get the feature domain.
- featureDomain() - Method in class org.tribuo.impl.ModelDataCarrier
-
The feature domain.
- featureForwardMapping - Variable in class org.tribuo.interop.ExternalModel
-
The forward mapping from Tribuo's indices to the external indices.
- FeatureHasher - Class in org.tribuo.data.text.impl
-
Hashes the feature names to reduce the dimensionality.
- FeatureHasher(int) - Constructor for class org.tribuo.data.text.impl.FeatureHasher
-
Constructs a feature hasher using the supplied hash dimension.
- FeatureHasher(int, boolean) - Constructor for class org.tribuo.data.text.impl.FeatureHasher
-
Constructs a feature hasher using the supplied hash dimension.
- FeatureHasher(int, int, int, boolean) - Constructor for class org.tribuo.data.text.impl.FeatureHasher
-
Constructs a feature hasher using the supplied hash dimension and seed values.
- featureIDMap - Variable in class org.tribuo.ImmutableDataset
-
A map from feature names to IDs for the features found in this dataset.
- featureIDMap - Variable in class org.tribuo.Model
-
The features this model knows about.
- featureIDMap - Variable in class org.tribuo.sequence.ImmutableSequenceDataset
-
A map from feature names to IDs for the features found in this dataset.
- featureIDMap - Variable in class org.tribuo.sequence.SequenceModel
-
The feature domain.
- featureIDs - Variable in class org.tribuo.impl.IndexedArrayExample
-
Feature id numbers from the internal featureMap.
- featureInfo(CommandInterpreter, String) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
-
Shows information on a particular feature.
- featureInfo(CommandInterpreter, String) - Method in class org.tribuo.data.DatasetExplorer
-
Shows information on a particular feature.
- featureInfo(CommandInterpreter, String) - Method in class org.tribuo.ModelExplorer
-
Shows a specific feature's information.
- featureInfo(CommandInterpreter, String) - Method in class org.tribuo.sequence.SequenceModelExplorer
-
Shows information on a particular feature.
- featureIterator() - Method in class org.tribuo.sequence.SequenceExample
-
Creates an iterator over every feature in this sequence.
- featureMap - Variable in class org.tribuo.MutableDataset
-
A map from feature names to feature info objects.
- featureMap - Variable in class org.tribuo.sequence.MutableSequenceDataset
-
A map from feature names to IDs for the features found in this dataset.
- FeatureMap - Class in org.tribuo
-
A map from Strings to
VariableInfo
objects storing information about a feature. - FeatureMap() - Constructor for class org.tribuo.FeatureMap
-
Constructs an empty feature map.
- FeatureMap(Map<String, ? extends VariableInfo>) - Constructor for class org.tribuo.FeatureMap
-
Constructs a feature map wrapping the supplied map.
- FeatureMap(FeatureMap) - Constructor for class org.tribuo.FeatureMap
-
Constructs a deep copy of the supplied feature map.
- featureNameComparator() - Static method in class org.tribuo.Feature
-
A comparator using the lexicographic ordering of feature names.
- featureNames - Variable in class org.tribuo.impl.ArrayExample
-
Feature names array.
- featureNames - Variable in class org.tribuo.impl.BinaryFeaturesExample
-
Feature names array.
- featureNames() - Method in class org.tribuo.SelectedFeatureSet
-
The selected feature names in a possibly ordered list.
- FeatureProcessor - Interface in org.tribuo.data.columnar
-
Takes a list of columnar features and adds new features or removes existing features.
- features - Variable in class org.tribuo.classification.sgd.Util.ExampleArray
-
The examples encoded as sparse vectors.
- features - Variable in class org.tribuo.classification.sgd.Util.SequenceExampleArray
-
The array of sequence example features.
- FEATURES_FILE_MODIFIED_TIME - Static variable in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
-
The name of the features file modified time provenance field.
- FEATURES_RESOURCE_HASH - Static variable in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
-
The name of the provenance field for the feature file hash.
- featureScores() - Method in class org.tribuo.SelectedFeatureSet
-
The selected feature scores in a possibly ordered list.
- FeatureSelector<T extends Output<T>> - Interface in org.tribuo
-
An interface for feature selection algorithms.
- FeatureSelectorProvenance - Interface in org.tribuo.provenance
-
A tag interface for feature selection algorithms.
- FeatureSelectorProvenanceImpl - Class in org.tribuo.provenance.impl
-
An implementation of
FeatureSelectorProvenance
which delegates everything toSkeletalConfiguredObjectProvenance
. - FeatureSelectorProvenanceImpl(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.impl.FeatureSelectorProvenanceImpl
-
Construct a FeatureSelectorProvenanceImpl by extracting the necessary fields from the supplied map.
- FeatureSelectorProvenanceImpl(T) - Constructor for class org.tribuo.provenance.impl.FeatureSelectorProvenanceImpl
-
Creates a FeatureSelectorProvenanceImpl by reading the configured fields from the host object.
- FeatureSetProvenance - Class in org.tribuo.provenance
-
Contains provenance information for an instance of a
SelectedFeatureSet
. - FeatureSetProvenance(String, DatasetProvenance, FeatureSelectorProvenance) - Constructor for class org.tribuo.provenance.FeatureSetProvenance
-
Creates a feature set provenance tracking the dataset provenance and feature selector provenance.
- FeatureSetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.FeatureSetProvenance
-
Used by the provenance unmarshalling system.
- FeatureTransformer - Interface in org.tribuo.data.text
-
A feature transformer maps a list of features to a new list of features.
- FeatureTuple() - Constructor for class org.tribuo.impl.IndexedArrayExample.FeatureTuple
-
Constructs an empty feature tuple.
- FeatureTuple(String, int, double) - Constructor for class org.tribuo.impl.IndexedArrayExample.FeatureTuple
-
Constructs a feature tuple using the specified values.
- featureValues - Variable in class org.tribuo.impl.ArrayExample
-
Feature values array.
- FeedForwardParameters - Interface in org.tribuo.math
-
A Parameters for models which make a single prediction like logistic regressions and neural networks.
- feedInto(Session.Runner) - Method in class org.tribuo.interop.tensorflow.TensorMap
-
Feeds the tensors in this FeedDict into the runner.
- FIELD_NAME - Static variable in class org.tribuo.data.columnar.processors.response.EmptyResponseProcessor
-
The field name this response processor looks for, which is ignored anyway as this processor always returns
Optional.empty()
. - FieldExtractor<T> - Interface in org.tribuo.data.columnar
-
Extracts a value from a field to be placed in an
Example
's metadata field. - fieldName - Variable in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor
-
The field name to read.
- FieldProcessor - Interface in org.tribuo.data.columnar
-
An interface for things that process the columns in a data set.
- FieldProcessor.GeneratedFeatureType - Enum Class in org.tribuo.data.columnar
-
The types of generated features.
- fieldProcessorMap - Variable in class org.tribuo.data.columnar.RowProcessor
-
The map of field processors.
- FieldResponseProcessor<T extends Output<T>> - Class in org.tribuo.data.columnar.processors.response
-
A response processor that returns the value(s) in a given (set of) fields.
- FieldResponseProcessor(String, String, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
-
Constructs a response processor which passes the field value through the output factory.
- FieldResponseProcessor(List<String>, String, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
-
Constructs a response processor which passes the field value through the output factory.
- FieldResponseProcessor(List<String>, List<String>, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
-
Constructs a response processor which passes the field value through the output factory.
- FieldResponseProcessor(List<String>, List<String>, OutputFactory<T>, boolean) - Constructor for class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
-
Constructs a response processor which passes the field value through the output factory.
- FieldResponseProcessor(List<String>, List<String>, OutputFactory<T>, boolean, boolean) - Constructor for class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
-
Constructs a response processor which passes the field value through the output factory.
- fields - Variable in class org.tribuo.data.columnar.ColumnarIterator
-
The column headers for this iterator.
- FILE_MODIFIED_TIME - Static variable in interface org.tribuo.provenance.DataSourceProvenance
-
The name of the provenance field for the file timestamp.
- fileCompleter() - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
-
Completers for filenames.
- fileCompleter() - Method in class org.tribuo.data.DatasetExplorer
-
The filename completer.
- fileCompleter() - Method in class org.tribuo.ModelExplorer
-
Completers for files.
- fileCompleter() - Method in class org.tribuo.sequence.SequenceModelExplorer
-
Completers for files.
- fill(double) - Method in class org.tribuo.math.la.DenseVector
-
Fills this
DenseVector
withvalue
. - fill(int[]) - Method in class org.tribuo.common.tree.impl.IntArrayContainer
-
Overwrites values from the supplied array into this array.
- fill(IntArrayContainer) - Method in class org.tribuo.common.tree.impl.IntArrayContainer
-
Overwrites values in this array with the supplied array.
- finalise() - Method in class org.tribuo.math.optimisers.AdaGradRDA
- finalise() - Method in class org.tribuo.math.optimisers.ParameterAveraging
-
This sets the parameters to their average value.
- finalise() - Method in class org.tribuo.math.optimisers.Pegasos
- finalise() - Method in interface org.tribuo.math.StochasticGradientOptimiser
-
Finalises the gradient optimisation, setting the parameters to their correct values.
- FIRST - Enum constant in enum class org.tribuo.data.columnar.processors.feature.UniqueProcessor.UniqueType
-
Select the first feature value in the list.
- FIRST - Enum constant in enum class org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector
-
The first variable is weighted.
- FIRST_CLASS - Static variable in class org.tribuo.classification.example.DemoLabelDataSource
-
The first class.
- firstCount - Variable in class org.tribuo.util.infotheory.impl.PairDistribution
-
The first marginal distribution.
- firstDimensionName - Static variable in class org.tribuo.regression.example.RegressionDataGenerator
-
Name of the first output dimension.
- fixSize() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
-
Fixes the size of the backing array.
- fixSize() - Method in class org.tribuo.regression.rtree.impl.TreeFeature
-
Fixes the size of each
InvertedFeature
's inner arrays. - FLOAT - Enum constant in enum class org.tribuo.datasource.IDXDataSource.IDXType
-
A 32-bit float.
- FloatExtractor - Class in org.tribuo.data.columnar.extractors
-
Extracts the field value and converts it to a float.
- FloatExtractor(String) - Constructor for class org.tribuo.data.columnar.extractors.FloatExtractor
-
Extracts a float value from the supplied field name.
- FloatExtractor(String, String) - Constructor for class org.tribuo.data.columnar.extractors.FloatExtractor
-
Extracts a float value from the supplied field name.
- floatInput(int) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates an input node for this ONNXContext, with the name "input", of dimension [batch_size,
featureDimension
], and of type float32. - floatInput(String, int) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates an input node for this ONNXContext, with the given name, of dimension [batch_size,
featureDimension
], and of type float32. - floatMatrix(ONNXContext, String, Matrix, boolean) - Static method in class org.tribuo.math.onnx.ONNXMathUtils
-
Builds a
ONNXInitializer
containing theMatrix
. - floatOutput(int) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates an output node for this ONNXContext, with the name "output", of dimension [batch_size,
outputDimension
], and of type float32. - floatOutput(String, int) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates an output node for this ONNXContext, with the given name, of dimension [batch_size,
outputDimension
], and of type float32. - floatTensor(String, List<Integer>, Consumer<FloatBuffer>) - Method in class org.tribuo.util.onnx.ONNXContext
-
Creates a tensor for this ONNXContext, populated as
ONNXUtils.floatTensorBuilder(ONNXContext, String, List, Consumer)
. - floatTensorBuilder(ONNXContext, String, List<Integer>, Consumer<FloatBuffer>) - Static method in class org.tribuo.util.onnx.ONNXUtils
-
Generic method to create float
OnnxMl.TensorProto
instances. - floatVector(ONNXContext, String, SGDVector) - Static method in class org.tribuo.math.onnx.ONNXMathUtils
-
Builds a
ONNXInitializer
containing theSGDVector
. - FMClassificationModel - Class in org.tribuo.classification.sgd.fm
-
The inference time version of a factorization machine trained using SGD.
- FMClassificationOptions - Class in org.tribuo.classification.sgd.fm
-
CLI options for training a factorization machine classifier.
- FMClassificationOptions() - Constructor for class org.tribuo.classification.sgd.fm.FMClassificationOptions
- FMClassificationOptions.LossEnum - Enum Class in org.tribuo.classification.sgd.fm
-
Available loss types.
- FMClassificationTrainer - Class in org.tribuo.classification.sgd.fm
-
A trainer for a classification factorization machine using SGD.
- FMClassificationTrainer(LabelObjective, StochasticGradientOptimiser, int, int, int, long, int, double) - Constructor for class org.tribuo.classification.sgd.fm.FMClassificationTrainer
-
Constructs an SGD trainer for a factorization machine.
- FMClassificationTrainer(LabelObjective, StochasticGradientOptimiser, int, int, long, int, double) - Constructor for class org.tribuo.classification.sgd.fm.FMClassificationTrainer
-
Constructs an SGD trainer for a factorization machine.
- FMClassificationTrainer(LabelObjective, StochasticGradientOptimiser, int, long, int, double) - Constructor for class org.tribuo.classification.sgd.fm.FMClassificationTrainer
-
Constructs an SGD trainer for a factorization machine.
- fmEpochs - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
-
Number of SGD epochs.
- fmEpochs - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
-
Number of SGD epochs.
- fmFactorSize - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
-
Factor size.
- fmFactorSize - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
-
Factor size.
- fmix32(int) - Static method in class org.tribuo.util.MurmurHash3
-
32-bit mixing function.
- fmix64(long) - Static method in class org.tribuo.util.MurmurHash3
-
64-bit mixing function.
- fmLoggingInterval - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
-
Log the objective after n examples.
- fmLoggingInterval - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
-
Log the objective after n examples.
- fmMinibatchSize - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
-
Minibatch size.
- fmMinibatchSize - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
-
Minibatch size.
- FMMultiLabelModel - Class in org.tribuo.multilabel.sgd.fm
-
The inference time version of a multi-label factorization machine trained using SGD.
- FMMultiLabelOptions - Class in org.tribuo.multilabel.sgd.fm
-
CLI options for training a linear classifier.
- FMMultiLabelOptions() - Constructor for class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
- FMMultiLabelOptions.LossEnum - Enum Class in org.tribuo.multilabel.sgd.fm
-
Available loss types.
- FMMultiLabelTrainer - Class in org.tribuo.multilabel.sgd.fm
-
A trainer for a multi-label classification factorization machine using SGD.
- FMMultiLabelTrainer(MultiLabelObjective, StochasticGradientOptimiser, int, int, int, long, int, double) - Constructor for class org.tribuo.multilabel.sgd.fm.FMMultiLabelTrainer
-
Constructs an SGD trainer for a multi-label factorization machine.
- FMMultiLabelTrainer(MultiLabelObjective, StochasticGradientOptimiser, int, int, long, int, double) - Constructor for class org.tribuo.multilabel.sgd.fm.FMMultiLabelTrainer
-
Constructs an SGD trainer for a multi-label factorization machine.
- FMMultiLabelTrainer(MultiLabelObjective, StochasticGradientOptimiser, int, long, int, double) - Constructor for class org.tribuo.multilabel.sgd.fm.FMMultiLabelTrainer
-
Constructs an SGD trainer for a multi-label factorization machine.
- fmObjective - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
-
Loss function.
- fmObjective - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
-
Loss function.
- FMParameters - Class in org.tribuo.common.sgd
-
A
Parameters
for factorization machines. - FMParameters(SplittableRandom, int, int, int, double) - Constructor for class org.tribuo.common.sgd.FMParameters
-
Constructor.
- FMRegressionModel - Class in org.tribuo.regression.sgd.fm
-
The inference time model of a regression factorization machine trained using SGD.
- FMRegressionOptions() - Constructor for class org.tribuo.regression.sgd.fm.TrainTest.FMRegressionOptions
- FMRegressionTrainer - Class in org.tribuo.regression.sgd.fm
-
A trainer for a regression factorization machine using SGD.
- FMRegressionTrainer(RegressionObjective, StochasticGradientOptimiser, int, int, int, long, int, double, boolean) - Constructor for class org.tribuo.regression.sgd.fm.FMRegressionTrainer
-
Constructs an SGD trainer for a factorization machine.
- FMRegressionTrainer(RegressionObjective, StochasticGradientOptimiser, int, int, long, int, double, boolean) - Constructor for class org.tribuo.regression.sgd.fm.FMRegressionTrainer
-
Constructs an SGD trainer for a factorization machine.
- FMRegressionTrainer(RegressionObjective, StochasticGradientOptimiser, int, long, int, double, boolean) - Constructor for class org.tribuo.regression.sgd.fm.FMRegressionTrainer
-
Constructs an SGD trainer for a factorization machine.
- fmVariance - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
-
Variance of the initialization gaussian.
- fmVariance - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
-
Variance of the initialization gaussian.
- fn() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
-
Returns the micro averaged number of false negatives.
- fn() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix
-
The total number of false negatives.
- fn() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
-
Gets the micro averaged false negative count.
- fn() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
- fn(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
- fn(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
-
The false negative count for this label.
- fn(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
-
Returns the number of false negatives, possibly averaged depending on the metric target.
- fn(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
- fn(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
- fn(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
-
Returns the number of false negatives, i.e., the number of times the true label was incorrectly predicted as another label.
- fn(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix
-
The number of false negatives for the supplied label.
- FN - Enum constant in enum class org.tribuo.anomaly.evaluation.AnomalyMetrics
-
The number of false negatives.
- FN - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics
-
The number of false negatives.
- FN - Enum constant in enum class org.tribuo.multilabel.evaluation.MultiLabelMetrics
-
The number of false negatives.
- foreachIndexedInPlace(ToDoubleBiFunction<Integer, Double>) - Method in class org.tribuo.math.la.DenseVector
- foreachIndexedInPlace(ToDoubleBiFunction<Integer, Double>) - Method in interface org.tribuo.math.la.SGDVector
-
Applies a
ToDoubleBiFunction
elementwise to thisSGDVector
. - foreachIndexedInPlace(ToDoubleBiFunction<Integer, Double>) - Method in class org.tribuo.math.la.SparseVector
-
Applies a
ToDoubleBiFunction
elementwise to thisSGDVector
. - foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseMatrix
- foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseSparseMatrix
- foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseVector
- foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.SparseVector
-
Applies a
DoubleUnaryOperator
elementwise to thisSGDVector
. - foreachInPlace(DoubleUnaryOperator) - Method in interface org.tribuo.math.la.Tensor
-
Applies a
DoubleUnaryOperator
elementwise to thisTensor
. - forEachRemaining(Consumer<? super ColumnarIterator.Row>) - Method in class org.tribuo.data.columnar.ColumnarIterator
- formatDuration(long, long) - Static method in class org.tribuo.util.Util
-
Formats a duration given two times in milliseconds.
- forTarget(MetricTarget<Label>) - Method in enum class org.tribuo.classification.evaluation.LabelMetrics
-
Gets the LabelMetric wrapped around the supplied MetricTarget.
- forTarget(MetricTarget<ClusterID>) - Method in enum class org.tribuo.clustering.evaluation.ClusteringMetrics
-
Constructs the metric for the specified metric target.
- forTarget(MetricTarget<MultiLabel>) - Method in enum class org.tribuo.multilabel.evaluation.MultiLabelMetrics
-
Get the metric for the supplied target.
- fp() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
-
Returns the micro average of the number of false positives across all the labels, i.e., the total number of false positives.
- fp() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix
-
The total number of false positives.
- fp() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
-
Gets the micro averaged false positive count.
- fp() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
- fp(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
- fp(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
-
The false positive count for this label.
- fp(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
-
Returns the number of false positives, possibly averaged depending on the metric target.
- fp(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
- fp(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
- fp(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
-
Returns the number of false positives, i.e., the number of times this label was predicted but it was not the true label..
- fp(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix
-
The number of false positives for the supplied label.
- FP - Enum constant in enum class org.tribuo.anomaly.evaluation.AnomalyMetrics
-
The number of false positives.
- FP - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics
-
The number of false positives.
- FP - Enum constant in enum class org.tribuo.multilabel.evaluation.MultiLabelMetrics
-
The number of false positives.
- fpr - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.ROC
-
The false positive rate at the corresponding threshold.
- fraction - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions
-
Fraction of features in split.
- fractionFeaturesInSplit - Variable in class org.tribuo.common.tree.AbstractCARTTrainer
-
Number of features to sample per split.
- frequencyBasedSample(Random, long) - Method in class org.tribuo.CategoricalInfo
-
Samples a value from this feature according to the frequency of observation.
- frequencyBasedSample(SplittableRandom, long) - Method in class org.tribuo.CategoricalInfo
-
Samples a value from this feature according to the frequency of observation.
- fscore(double, double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
-
Computes the Fscore.
- fscore(MetricTarget<T>, ConfusionMatrix<T>, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
-
Computes the Fscore.
- FTRL - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser
-
The FTRL optimiser.
- fullEquals(Event) - Method in class org.tribuo.anomaly.Event
- fullEquals(Event, double) - Method in class org.tribuo.anomaly.Event
- fullEquals(Label) - Method in class org.tribuo.classification.Label
- fullEquals(Label, double) - Method in class org.tribuo.classification.Label
- fullEquals(ClusterID) - Method in class org.tribuo.clustering.ClusterID
- fullEquals(MultiLabel) - Method in class org.tribuo.multilabel.MultiLabel
- fullEquals(MultiLabel, double) - Method in class org.tribuo.multilabel.MultiLabel
- fullEquals(Regressor) - Method in class org.tribuo.regression.Regressor.DimensionTuple
- fullEquals(Regressor) - Method in class org.tribuo.regression.Regressor
- fullEquals(Regressor, double) - Method in class org.tribuo.regression.Regressor
- fullEquals(T) - Method in interface org.tribuo.Output
-
Compares other to this output.
- fullEquals(T, double) - Method in interface org.tribuo.Output
-
Compares other to this output.
- FULLY_WEIGHTED_VOTING - Enum constant in enum class org.tribuo.common.nearest.KNNClassifierOptions.EnsembleCombinerType
-
Use a
FullyWeightedVotingCombiner
. - FullyWeightedVotingCombiner - Class in org.tribuo.classification.ensemble
-
A combiner which performs a weighted or unweighted vote across the predicted labels.
- FullyWeightedVotingCombiner() - Constructor for class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
-
Constructs a weighted voting combiner.
G
- gamma - Variable in class org.tribuo.regression.libsvm.TrainTest.LibSVMOptions
-
Gamma value in kernel function.
- gamma - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
-
Minimum loss reduction to make a split (default 0, range [0,inf]).
- gamma - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
-
Minimum loss reduction to make a split (default 0, range [0,inf]).
- gamma(double) - Static method in class org.tribuo.util.infotheory.Gamma
-
Function to calculate the value of a Gamma function.
- Gamma - Class in org.tribuo.util.infotheory
-
Static functions for computing the Gamma and log Gamma functions on real valued inputs.
- GAMMA - Enum constant in enum class org.tribuo.regression.xgboost.XGBoostRegressionTrainer.RegressionType
-
Gamma loss function.
- GATHER - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators
-
Gathers elements from the first argument (of rank r) indexed by the second argument (of rank q) producing a tensor of rank
q + r - 1
. - gatherAcrossDim1(int[]) - Method in class org.tribuo.math.la.DenseMatrix
-
Constructs a dense vector by gathering values across dimension 1.
- gatherAcrossDim2(int[]) - Method in class org.tribuo.math.la.DenseMatrix
-
Constructs a dense vector by gathering values across dimension 2.
- GAUSSIAN - Enum constant in enum class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyType
-
Samples from a Gaussian using the means and variances from the training data.
- gaussianAnomaly() - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator
-
Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
- gaussianAnomaly(long, double) - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator
-
Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
- GaussianAnomalyDataSource - Class in org.tribuo.anomaly.example
-
Generates an anomaly detection dataset sampling each feature uniformly from a univariate Gaussian.
- GaussianAnomalyDataSource(int, double[], double[], double[], double[], float, long) - Constructor for class org.tribuo.anomaly.example.GaussianAnomalyDataSource
-
Generates anomaly detection examples sampling each feature uniformly from a univariate Gaussian.
- GaussianAnomalyDataSource(int, float, long) - Constructor for class org.tribuo.anomaly.example.GaussianAnomalyDataSource
-
Generates anomaly detection examples sampling each feature uniformly from a univariate Gaussian.
- GaussianAnomalyDataSource.GaussianAnomalyDataSourceProvenance - Class in org.tribuo.anomaly.example
-
Provenance for
GaussianAnomalyDataSource
. - GaussianAnomalyDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.anomaly.example.GaussianAnomalyDataSource.GaussianAnomalyDataSourceProvenance
-
Constructs a provenance from the marshalled form.
- GaussianClusterDataSource - Class in org.tribuo.clustering.example
-
Generates a clustering dataset drawn from a mixture of 5 Gaussians.
- GaussianClusterDataSource(int, double[], double[], double[], double[], double[], double[], double[], double[], double[], double[], double[], long) - Constructor for class org.tribuo.clustering.example.GaussianClusterDataSource
-
Generates a clustering dataset drawn from a mixture of 5 Gaussians.
- GaussianClusterDataSource(int, long) - Constructor for class org.tribuo.clustering.example.GaussianClusterDataSource
-
Generates a clustering dataset drawn from a mixture of 5 Gaussians.
- GaussianClusterDataSource.GaussianClusterDataSourceProvenance - Class in org.tribuo.clustering.example
-
Provenance for
GaussianClusterDataSource
. - GaussianClusterDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.clustering.example.GaussianClusterDataSource.GaussianClusterDataSourceProvenance
-
Constructs a provenance from the marshalled form.
- gaussianClusters(long, long) - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator
-
Generates a dataset drawn from a mixture of 5 2d gaussians.
- GaussianDataSource - Class in org.tribuo.regression.example
-
Generates a single dimensional output drawn from N(slope*x + intercept,variance).
- GaussianDataSource(int, float, float, float, float, float, long) - Constructor for class org.tribuo.regression.example.GaussianDataSource
-
Generates a single dimensional output drawn from N(slope*x + intercept,variance).
- GaussianDataSource.GaussianDataSourceProvenance - Class in org.tribuo.regression.example
-
Provenance for
GaussianDataSource
. - GaussianDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.regression.example.GaussianDataSource.GaussianDataSourceProvenance
-
Constructs a provenance from the marshalled form.
- GaussianLabelDataSource - Class in org.tribuo.classification.example
-
A data source for two classes generated from separate Gaussians.
- GaussianLabelDataSource(int, long, double[], double[], double[], double[]) - Constructor for class org.tribuo.classification.example.GaussianLabelDataSource
-
Constructs a data source which contains two classes where each class is sampled from a 2d Gaussian with the specified parameters.
- GBTREE - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.BoosterType
-
A gradient boosted decision tree.
- GEMM - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators
-
General Matrix Multiply:
alpha*AB + beta*C
. - general - Variable in class org.tribuo.classification.experiments.ConfigurableTrainTest.ConfigurableTrainTestOptions
-
Options for loading in data.
- general - Variable in class org.tribuo.classification.experiments.RunAll.RunAllOptions
-
Options for loading in data.
- general - Variable in class org.tribuo.classification.experiments.TrainTest.AllClassificationOptions
-
Options for loading in data.
- general - Variable in class org.tribuo.classification.liblinear.TrainTest.TrainTestOptions
-
The data loading options.
- general - Variable in class org.tribuo.classification.libsvm.TrainTest.TrainTestOptions
-
The data loading options.
- general - Variable in class org.tribuo.classification.mnb.TrainTest.TrainTestOptions
-
The data loading options.
- general - Variable in class org.tribuo.classification.sgd.fm.TrainTest.TrainTestOptions
-
The data loading options.
- general - Variable in class org.tribuo.classification.sgd.kernel.TrainTest.TrainTestOptions
-
The data loading options.
- general - Variable in class org.tribuo.classification.sgd.TrainTest.TrainTestOptions
-
The data loading options.
- general - Variable in class org.tribuo.classification.xgboost.TrainTest.TrainTestOptions
-
The data loading options.
- general - Variable in class org.tribuo.clustering.hdbscan.TrainTest.HdbscanCLIOptions
-
The data loading options.
- general - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
-
The data loading options.
- general - Variable in class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions
-
Data loading options.
- general - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
-
The data loading options.
- general - Variable in class org.tribuo.regression.libsvm.TrainTest.LibSVMOptions
-
The data loading options.
- general - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions
-
The data loading options.
- general - Variable in class org.tribuo.regression.sgd.fm.TrainTest.FMRegressionOptions
-
The dataset loading options.
- general - Variable in class org.tribuo.regression.sgd.TrainTest.SGDOptions
-
The dataset loading options.
- general - Variable in class org.tribuo.regression.slm.TrainTest.SLMOptions
-
The data loading options.
- general - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
-
The data loading options.
- generalOptions - Variable in class org.tribuo.classification.dtree.TrainTest.TrainTestOptions
-
The data loading options.
- generate() - Method in class org.tribuo.classification.example.CheckerboardDataSource
- generate() - Method in class org.tribuo.classification.example.ConcentricCirclesDataSource
- generate() - Method in class org.tribuo.classification.example.DemoLabelDataSource
-
Generates the examples using the configured fields.
- generate() - Method in class org.tribuo.classification.example.GaussianLabelDataSource
- generate() - Method in class org.tribuo.classification.example.InterlockingCrescentsDataSource
- generate() - Method in class org.tribuo.classification.example.NoisyInterlockingCrescentsDataSource
- generateBootstrap() - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance
-
Generates the indices from this DatasetViewProvenance by rerunning the bootstrap sample.
- generateBootstrapIndices(int, int, SplittableRandom) - Static method in class org.tribuo.util.Util
-
Draws a bootstrap sample of indices.
- generateBootstrapIndices(int, Random) - Static method in class org.tribuo.util.Util
-
Draws a bootstrap sample of indices.
- generateBootstrapIndices(int, SplittableRandom) - Static method in class org.tribuo.util.Util
-
Draws a bootstrap sample of indices.
- generateCDF(double[]) - Static method in class org.tribuo.util.Util
-
Generates a cumulative distribution function from the supplied probability mass function.
- generateCDF(float[]) - Static method in class org.tribuo.util.Util
-
Generates a cumulative distribution function from the supplied probability mass function.
- generateCDF(long[], long) - Static method in class org.tribuo.util.Util
-
Generates a cumulative distribution function from the supplied probability mass function.
- generateCorrelated(int, int, double, double) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo
-
These correlations don't map to mutual information values, as if xyDraw is above xyCorrelation then the draw is completely random.
- generateDataset() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator
-
Generate training and testing datasets.
- generateDataset(int, double[], double[], double[], double[], double[], double[], double[], double[], double[], double[], double[], long) - Static method in class org.tribuo.clustering.example.GaussianClusterDataSource
-
Generates a clustering dataset drawn from a mixture of 5 Gaussians.
- generateDataset(int, double[], double[], double[], double[], float, long) - Static method in class org.tribuo.anomaly.example.GaussianAnomalyDataSource
-
Generates an anomaly detection dataset sampling each feature uniformly from a univariate Gaussian.
- generateDataset(int, float[], float[], float[], float[], boolean[], float, float[], float[], long) - Static method in class org.tribuo.multilabel.example.MultiLabelGaussianDataSource
-
Generates a multi-label output drawn from three gaussian functions.
- generateDataset(int, float[], float, float, float, float, float, float, long) - Static method in class org.tribuo.regression.example.NonlinearGaussianDataSource
-
Generates a single dimensional output drawn from N(w_0*x_0 + w_1*x_1 + w_2*x_1*x_0 + w_3*x_1*x_1*x_1 + intercept,variance).
- generateDataset(int, float, float, float, float, float, long) - Static method in class org.tribuo.regression.example.GaussianDataSource
-
Generates a single dimensional output drawn from N(slope*x + intercept,variance).
- generateEmptyExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
-
This generates a sequence example with no examples.
- generateExample(long, Map<String, String>, boolean) - Method in class org.tribuo.data.columnar.RowProcessor
-
Generate an
Example
from the supplied row. - generateExample(Map<String, String>, boolean) - Method in class org.tribuo.data.columnar.RowProcessor
-
Generate an
Example
from the supplied row. - generateExample(ColumnarIterator.Row, boolean) - Method in class org.tribuo.data.columnar.RowProcessor
-
Generate an
Example
from the supplied row. - generateFeatureName(String, String) - Static method in class org.tribuo.data.columnar.ColumnarFeature
-
Generates a feature name based on the field name and the name.
- generateFeatureName(String, String, String) - Static method in class org.tribuo.data.columnar.ColumnarFeature
-
Generates a feature name used for conjunction features.
- generateFeatures(Map<String, String>) - Method in class org.tribuo.data.columnar.RowProcessor
-
Generates the features from the supplied row.
- generateGorillaA() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
-
Generates a sequence example with a mixture of features and three labels "O", "Status" and "Monkey".
- generateGorillaB() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
-
Generates a sequence example with a mixture of features and three labels "O", "Status" and "Monkey".
- generateGorillaDataset(int) - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
-
Generates a simple dataset consisting of numCopies repeats of two sequences.
- generateHashedFeatureMap(FeatureMap, Hasher) - Static method in class org.tribuo.hash.HashedFeatureMap
-
Converts a standard
FeatureMap
by hashing each entry using the supplied hash functionHasher
. - generateIDs(List<? extends VariableInfo>) - Static method in class org.tribuo.ImmutableFeatureMap
-
Generates the feature ids by sorting the features with the String comparator, then sequentially numbering them.
- generateIDs(FeatureMap) - Static method in class org.tribuo.ImmutableFeatureMap
-
Generates the feature ids by sorting the features with the String comparator, then sequentially numbering them.
- generateImmutableOutputInfo() - Method in class org.tribuo.anomaly.AnomalyInfo
- generateImmutableOutputInfo() - Method in class org.tribuo.classification.LabelInfo
- generateImmutableOutputInfo() - Method in class org.tribuo.clustering.ClusteringInfo
- generateImmutableOutputInfo() - Method in class org.tribuo.multilabel.MultiLabelInfo
- generateImmutableOutputInfo() - Method in interface org.tribuo.OutputInfo
-
Generates an
ImmutableOutputInfo
which has a copy of the data in thisOutputInfo
, but also has id values and is immutable. - generateImmutableOutputInfo() - Method in class org.tribuo.regression.RegressionInfo
- generateInfo() - Method in class org.tribuo.anomaly.AnomalyFactory
- generateInfo() - Method in class org.tribuo.classification.LabelFactory
-
Generates an empty MutableLabelInfo.
- generateInfo() - Method in class org.tribuo.clustering.ClusteringFactory
- generateInfo() - Method in class org.tribuo.multilabel.MultiLabelFactory
- generateInfo() - Method in interface org.tribuo.OutputFactory
-
Generates the appropriate
MutableOutputInfo
so the output values can be tracked by aDataset
or other aggregate. - generateInfo() - Method in class org.tribuo.regression.RegressionFactory
- generateInvalidExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
-
This generates a sequence example with features that are unused by the training data.
- generateLabelString(Set<Label>) - Static method in class org.tribuo.multilabel.MultiLabelFactory
- generateMetadata(ColumnarIterator.Row) - Method in class org.tribuo.data.columnar.RowProcessor
-
Generates the example metadata from the supplied row and index.
- generateMethodName(String, String) - Static method in class org.tribuo.protos.ProtoUtil
-
Generate the method name for the type from the protobuf.
- generateMutableOutputInfo() - Method in class org.tribuo.anomaly.AnomalyInfo
- generateMutableOutputInfo() - Method in class org.tribuo.classification.LabelInfo
- generateMutableOutputInfo() - Method in class org.tribuo.clustering.ClusteringInfo
- generateMutableOutputInfo() - Method in class org.tribuo.multilabel.MultiLabelInfo
- generateMutableOutputInfo() - Method in interface org.tribuo.OutputInfo
-
Generates a mutable copy of this
OutputInfo
. - generateMutableOutputInfo() - Method in class org.tribuo.regression.RegressionInfo
- generateOtherInvalidExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
-
This generates a sequence example where the first example has no features.
- generateOutput(V) - Method in class org.tribuo.anomaly.AnomalyFactory
- generateOutput(V) - Method in class org.tribuo.classification.LabelFactory
-
Generates the Label string by calling toString on the input.
- generateOutput(V) - Method in class org.tribuo.clustering.ClusteringFactory
-
Generates a ClusterID by calling toString on the input, then calling Integer.parseInt.
- generateOutput(V) - Method in class org.tribuo.multilabel.MultiLabelFactory
-
Parses the MultiLabel value either by toStringing the input and calling
MultiLabel.parseString(java.lang.String)
or if it's aCollection
iterating over the elements calling toString on each element in turn and usingMultiLabel.parseElement(java.lang.String)
. - generateOutput(V) - Method in interface org.tribuo.OutputFactory
-
Parses the
V
and generates the appropriateOutput
value. - generateOutput(V) - Method in class org.tribuo.regression.RegressionFactory
-
Parses the Regressor value either by toStringing the input and calling
Regressor.parseString(java.lang.String)
or if it's a collection iterating over the elements calling toString on each element in turn and usingRegressor.parseElement(int, java.lang.String)
. - generateOutputs(List<V>) - Method in interface org.tribuo.OutputFactory
-
Generate a list of outputs from the supplied list of inputs.
- generatePlaceholderName(String) - Static method in class org.tribuo.interop.tensorflow.TensorFlowUtil
-
Creates a name for a placeholder based on the supplied variable name.
- generatePRCurve(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil
-
Calculates the Precision Recall curve for a single label.
- generateRealInfo() - Method in class org.tribuo.CategoricalIDInfo
-
Generates a
RealIDInfo
that matches this CategoricalInfo and also contains an id number. - generateRealInfo() - Method in class org.tribuo.CategoricalInfo
-
Generates a
RealInfo
using the currently observed counts to calculate the min, max, mean and variance. - generateROCCurve(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil
-
Calculates the binary ROC for a single label.
- generatesProbabilities - Variable in class org.tribuo.interop.onnx.LabelTransformer
- generatesProbabilities - Variable in class org.tribuo.Model
-
Does this model generate probability distributions in the output.
- generatesProbabilities() - Method in class org.tribuo.classification.xgboost.XGBoostClassificationConverter
- generatesProbabilities() - Method in interface org.tribuo.common.xgboost.XGBoostOutputConverter
-
Does this converter produce probabilities?
- generatesProbabilities() - Method in class org.tribuo.impl.ModelDataCarrier
-
Does this model generate probabilities?
- generatesProbabilities() - Method in class org.tribuo.interop.oci.OCILabelConverter
- generatesProbabilities() - Method in class org.tribuo.interop.oci.OCIMultiLabelConverter
- generatesProbabilities() - Method in interface org.tribuo.interop.oci.OCIOutputConverter
-
Does this OCIOutputConverter generate probabilities?
- generatesProbabilities() - Method in class org.tribuo.interop.oci.OCIRegressorConverter
- generatesProbabilities() - Method in class org.tribuo.interop.onnx.LabelTransformer
- generatesProbabilities() - Method in class org.tribuo.interop.onnx.MultiLabelTransformer
- generatesProbabilities() - Method in interface org.tribuo.interop.onnx.OutputTransformer
-
Does this OutputTransformer generate probabilities.
- generatesProbabilities() - Method in class org.tribuo.interop.onnx.RegressorTransformer
- generatesProbabilities() - Method in class org.tribuo.interop.tensorflow.LabelConverter
- generatesProbabilities() - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
- generatesProbabilities() - Method in interface org.tribuo.interop.tensorflow.OutputConverter
-
Does this OutputConverter generate probabilities.
- generatesProbabilities() - Method in class org.tribuo.interop.tensorflow.RegressorConverter
- generatesProbabilities() - Method in class org.tribuo.Model
-
Does this model generate probabilistic predictions.
- generatesProbabilities() - Method in class org.tribuo.regression.xgboost.XGBoostRegressionConverter
- generatesProbabilities(CommandInterpreter) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
-
Does the model generate probabilities.
- generatesProbabilities(CommandInterpreter) - Method in class org.tribuo.ModelExplorer
-
Checks if the model generates probabilities.
- generateTestData() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator
-
Simple test data for checking multi-label trainers.
- generateTrainData() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator
-
Simple training data for checking multi-label trainers.
- generateTransformer() - Method in class org.tribuo.transform.transformations.SimpleTransform
-
Returns itself.
- generateTransformer() - Method in interface org.tribuo.transform.TransformStatistics
-
Generates the appropriate
Transformer
from the collected statistics. - generateUniform(int, int) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo
-
Generates a sample from a uniform distribution over the integers.
- generateUniformFloatVector(int, float) - Static method in class org.tribuo.util.Util
-
Generates a float vector of the specified length filled with the specified value.
- generateUniformVector(int, double) - Static method in class org.tribuo.util.Util
-
Generates an array of the specified length filled with the specified value.
- generateUniformVector(int, float) - Static method in class org.tribuo.util.Util
-
Generates an array of the specified length filled with the specified value.
- generateWeightedIndicesSample(int, double[], Random) - Static method in class org.tribuo.util.Util
-
Generates a sample of indices weighted by the provided weights.
- generateWeightedIndicesSample(int, double[], SplittableRandom) - Static method in class org.tribuo.util.Util
-
Generates a sample of indices weighted by the provided weights.
- generateWeightedIndicesSample(int, float[], Random) - Static method in class org.tribuo.util.Util
-
Generates a sample of indices weighted by the provided weights.
- generateWeightedIndicesSample(int, float[], SplittableRandom) - Static method in class org.tribuo.util.Util
-
Generates a sample of indices weighted by the provided weights.
- generateWeightedIndicesSampleWithoutReplacement(int, double[], Random) - Static method in class org.tribuo.util.Util
-
Generates a sample of indices weighted by the provided weights without replacement.
- generateWeightedIndicesSampleWithoutReplacement(int, float[], Random) - Static method in class org.tribuo.util.Util
-
Generates a sample of indices weighted by the provided weights without replacement.
- generateWeightsString() - Method in class org.tribuo.classification.sgd.crf.CRFModel
-
Generates a human readable string containing all the weights in this model.
- generateXOR(int) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo
-
Generates a sample from a three variable XOR function.
- get() - Method in class org.tribuo.classification.sgd.crf.CRFParameters
- get() - Method in class org.tribuo.common.sgd.FMParameters
- get() - Method in class org.tribuo.math.LinearParameters
- get() - Method in interface org.tribuo.math.Parameters
-
Get a reference to the underlying
Tensor
array. - get(int) - Method in class org.tribuo.ImmutableFeatureMap
-
Gets the
VariableIDInfo
for this id number. - get(int) - Method in class org.tribuo.math.la.DenseVector
- get(int) - Method in interface org.tribuo.math.la.SGDVector
-
Gets an element from this vector.
- get(int) - Method in class org.tribuo.math.la.SparseVector
- get(int) - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
- get(int) - Method in class org.tribuo.sequence.SequenceExample
-
Gets the example found at the specified index.
- get(int) - Method in class org.tribuo.util.infotheory.impl.RowList
- get(int, int) - Method in class org.tribuo.math.la.DenseMatrix
- get(int, int) - Method in class org.tribuo.math.la.DenseSparseMatrix
- get(int, int) - Method in interface org.tribuo.math.la.Matrix
-
Gets an element from this
Matrix
. - get(int, int) - Method in class org.tribuo.math.optimisers.util.ShrinkingMatrix
- get(String) - Method in class org.tribuo.FeatureMap
-
Gets the variable info associated with that feature name, or null if it's unknown.
- get(String) - Method in class org.tribuo.hash.HashedFeatureMap
- get(String) - Method in class org.tribuo.ImmutableFeatureMap
-
Gets the
VariableIDInfo
for this name. - get(String) - Method in class org.tribuo.transform.TransformerMap
-
Gets the transformers associated with a given feature name.
- get(MetricID<MultiLabel>) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
- get(MetricID<T>) - Method in interface org.tribuo.evaluation.Evaluation
-
Gets the value associated with the specific metric.
- get(MetricID<T>) - Method in interface org.tribuo.sequence.SequenceEvaluation
-
Gets the value associated with the specific metric.
- getA() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
-
Gets the first element.
- getAB() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
-
Gets the pair of the first and second elements.
- getABCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
-
The joint distribution over the first and second variables.
- getABCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
-
The joint distribution over the first and second variables.
- getAC() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
-
Gets the pair of the first and third elements.
- getACCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
-
The joint distribution over the first and third variables.
- getACCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
-
The joint distribution over the first and third variables.
- getACount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
-
The marginal distribution over the first variable.
- getACount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
-
The marginal distribution over the first variable.
- getActiveFeatures() - Method in interface org.tribuo.classification.explanations.Explanation
-
Returns the names of the active features in this explanation.
- getActiveFeatures() - Method in class org.tribuo.classification.explanations.lime.LIMEExplanation
- getActiveFeatures() - Method in class org.tribuo.SparseModel
-
Return an immutable view on the active features for each dimension.
- getAnomalyCount() - Method in class org.tribuo.anomaly.AnomalyInfo
-
The number of anomalous events observed.
- getArch() - Method in class org.tribuo.provenance.ModelProvenance
-
The CPU architecture used to create this model.
- getAttributes() - Method in interface org.tribuo.util.onnx.ONNXOperator
-
The operator attributes.
- getAttributes() - Method in enum class org.tribuo.util.onnx.ONNXOperators
- getAverageTarget() - Method in class org.tribuo.evaluation.metrics.MetricTarget
-
Returns the average this metric computes, or
Optional.empty()
if it targets an output. - getB() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
-
Gets the second element.
- getBaseName() - Method in class org.tribuo.util.onnx.ONNXRef
-
The name of this object.
- getBatchPredictions(List<OnnxValue>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.onnx.LabelOneVOneTransformer
-
Rationalises the output of an onnx model into a standard format suitable for downstream work in Tribuo.
- getBatchPredictions(List<OnnxValue>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.onnx.LabelTransformer
-
Rationalises the output of an onnx model into a standard format suitable for downstream work in Tribuo.
- getBatchSize() - Method in class org.tribuo.interop.ExternalModel
-
Gets the current testing batch size.
- getBatchSize() - Method in class org.tribuo.interop.tensorflow.TensorFlowModel
-
Gets the current testing batch size.
- getBC() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
-
Gets the pair of the second and third elements.
- getBCCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
-
The joint distribution over the second and third variables.
- getBCCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
-
The joint distribution over the second and third variables.
- getBCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
-
The marginal distribution over the second variable.
- getBCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
-
The marginal distribution over the second variable.
- getBias(int) - Method in class org.tribuo.classification.sgd.crf.CRFParameters
-
Returns the bias for the specified label id.
- getBiasesCopy() - Method in class org.tribuo.common.sgd.AbstractFMModel
-
Returns a copy of the output dimension biases.
- getC() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
-
Gets the third element.
- getCCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
-
The marginal distribution over the third variable.
- getCCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
-
The marginal distribution over the third variable.
- getCentroids() - Method in class org.tribuo.clustering.kmeans.KMeansModel
-
Returns a list of features, one per centroid.
CRFModel.convertToVector(org.tribuo.sequence.SequenceExample<T>, org.tribuo.ImmutableFeatureMap)
which is more flexible.