Uses of Class
org.tribuo.classification.Label
Packages that use Label
Package
Description
Provides classes and infrastructure for multiclass classification problems.
Provides simple baseline multiclass classifiers.
Provides implementations of decision trees for classification problems.
Provides internal implementation classes for classification decision trees.
Provides majority vote ensemble combiners for classification
along with an implementation of multiclass Adaboost.
Evaluation classes for multi-class classification.
Provides a multiclass data generator used for testing implementations, along with several synthetic data generators
for 2d binary classification problems to be used in demos or tutorials.
Provides a set of main methods for interacting with classification tasks.
Provides core infrastructure for local model based explanations.
Provides an implementation of LIME (Locally Interpretable Model Explanations).
Provides an interface to LibLinear-java for classification problems.
Provides an interface to LibSVM for classification problems.
Provides an implementation of multinomial naive bayes (i.e., naive bayes for non-negative count data).
Provides infrastructure for
SequenceModel
s which
emit Label
s at each step of the sequence.Provides a classification sequence data generator for smoke testing implementations.
Provides an implementation of Viterbi for generating structured outputs, which can sit on top of any
Label
based classification model.Provides an implementation of a linear chain CRF trained using Stochastic Gradient Descent.
Provides an implementation of a classification factorization machine using Stochastic Gradient Descent.
Provides a SGD implementation of a Kernel SVM using the Pegasos algorithm.
Provides an implementation of a classification linear model using Stochastic Gradient Descent.
Provides an interface to XGBoost for classification problems.
Provides a K-Nearest Neighbours implementation which works across
all Tribuo
Output
types.Code for uploading models to Oracle Cloud Infrastructure Data Science, and also for scoring models deployed
in Oracle Cloud Infrastructure Data Science.
This package contains a Tribuo wrapper around the ONNX Runtime.
Provides an interface to TensorFlow, allowing the training of non-sequential models using any supported
Tribuo output type.
Provides classes and infrastructure for working with multi-label classification problems.
Provides implementations of binary relevance based multi-label classification
algorithms.
Provides a multi-label ensemble combiner that performs a (possibly
weighted) majority vote among each label independently, along with an
implementation of classifier chain ensembles.
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Uses of Label in org.tribuo.classification
Classes in org.tribuo.classification with type parameters of type LabelModifier and TypeInterfaceDescriptioninterface
ClassificationOptions<TRAINER extends Trainer<Label>>
AnOptions
that can produce a classificationTrainer
based on the provided arguments.Classes in org.tribuo.classification that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionclass
AnImmutableOutputInfo
object forLabel
s.final class
An immutable multi-class classification label.final class
A factory for making Label related classes.class
The base class for information about multi-class classification Labels.class
A mutableLabelInfo
.Fields in org.tribuo.classification declared as LabelModifier and TypeFieldDescriptionstatic final Label
LabelFactory.UNKNOWN_LABEL
The singleton unknown label, used for unlablled examples.Fields in org.tribuo.classification with type parameters of type LabelMethods in org.tribuo.classification that return LabelModifier and TypeMethodDescriptionLabel.copy()
<V> Label
LabelFactory.generateOutput
(V label) Generates the Label string by calling toString on the input.ImmutableLabelInfo.getOutput
(int id) LabelFactory.getUnknownOutput()
Methods in org.tribuo.classification that return types with arguments of type LabelModifier and TypeMethodDescriptionLabelFactory.constructInfoForExternalModel
(Map<Label, Integer> mapping) LabelInfo.generateImmutableOutputInfo()
LabelFactory.generateInfo()
Generates an empty MutableLabelInfo.LabelInfo.generateMutableOutputInfo()
ImmutableLabelInfo.getDomain()
Returns the set of possibleLabel
s that this LabelInfo has seen.LabelInfo.getDomain()
Returns the set of possibleLabel
s that this LabelInfo has seen.LabelFactory.getEvaluator()
ImmutableLabelInfo.iterator()
TrainTestHelper.run
(com.oracle.labs.mlrg.olcut.config.ConfigurationManager cm, DataOptions dataOptions, Trainer<Label> trainer) This method trains a model on the specified training data, and evaluates it on the specified test data.Methods in org.tribuo.classification with parameters of type LabelModifier and TypeMethodDescriptionboolean
Label.fullEquals
(Label o) int
long
LabelInfo.getLabelCount
(Label label) Gets the count of the supplied label, or 0 if the label is unknown.void
Method parameters in org.tribuo.classification with type arguments of type LabelModifier and TypeMethodDescriptionLabelFactory.constructInfoForExternalModel
(Map<Label, Integer> mapping) boolean
ImmutableLabelInfo.domainAndIDEquals
(ImmutableOutputInfo<Label> other) TrainTestHelper.run
(com.oracle.labs.mlrg.olcut.config.ConfigurationManager cm, DataOptions dataOptions, Trainer<Label> trainer) This method trains a model on the specified training data, and evaluates it on the specified test data.void
WeightedLabels.setLabelWeights
(Map<Label, Float> map) Sets the label weights used by this trainer. -
Uses of Label in org.tribuo.classification.baseline
Subclasses with type arguments of type Label in org.tribuo.classification.baselineModifier and TypeClassDescriptionclass
A model which performs dummy classifications (e.g., constant output, uniform sampled labels, stratified sampled labels).Classes in org.tribuo.classification.baseline that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionfinal class
A trainer for simple baseline classifiers.Methods in org.tribuo.classification.baseline that return types with arguments of type LabelModifier and TypeMethodDescriptionDummyClassifierTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance) DummyClassifierTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount) Method parameters in org.tribuo.classification.baseline with type arguments of type LabelModifier and TypeMethodDescriptionDummyClassifierTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance) DummyClassifierTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount) -
Uses of Label in org.tribuo.classification.dtree
Subclasses with type arguments of type Label in org.tribuo.classification.dtreeModifier and TypeClassDescriptionclass
ATrainer
that uses an approximation of the CART algorithm to build a decision tree.Methods in org.tribuo.classification.dtree that return types with arguments of type LabelModifier and TypeMethodDescriptionprotected AbstractTrainingNode
<Label> CARTClassificationTrainer.mkTrainingNode
(Dataset<Label> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer) Method parameters in org.tribuo.classification.dtree with type arguments of type LabelModifier and TypeMethodDescriptionprotected AbstractTrainingNode
<Label> CARTClassificationTrainer.mkTrainingNode
(Dataset<Label> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer) -
Uses of Label in org.tribuo.classification.dtree.impl
Subclasses with type arguments of type Label in org.tribuo.classification.dtree.implModifier and TypeClassDescriptionclass
A decision tree node used at training time.Methods in org.tribuo.classification.dtree.impl that return types with arguments of type LabelModifier and TypeMethodDescriptionClassifierTrainingNode.buildRandomTree
(int[] featureIDs, SplittableRandom rng) Builds a CART tree with randomly chosen split points.ClassifierTrainingNode.buildTree
(int[] featureIDs, SplittableRandom rng, boolean useRandomSplitPoints) Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).ClassifierTrainingNode.convertTree()
Constructor parameters in org.tribuo.classification.dtree.impl with type arguments of type LabelModifierConstructorDescriptionClassifierTrainingNode
(LabelImpurity impurity, Dataset<Label> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer) Constructor which creates the inverted file. -
Uses of Label in org.tribuo.classification.ensemble
Classes in org.tribuo.classification.ensemble that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionclass
Implements Adaboost.SAMME one of the more popular algorithms for multiclass boosting.final class
A combiner which performs a weighted or unweighted vote across the predicted labels.final class
A combiner which performs a weighted or unweighted vote across the predicted labels.Fields in org.tribuo.classification.ensemble with type parameters of type LabelMethods in org.tribuo.classification.ensemble that return types with arguments of type LabelModifier and TypeMethodDescriptionFullyWeightedVotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions) FullyWeightedVotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights) VotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions) VotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights) AdaBoostTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) If the trainer implementsWeightedExamples
then do boosting by weighting, otherwise do boosting by sampling.AdaBoostTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) ClassificationEnsembleOptions.wrapTrainer
(Trainer<Label> trainer) Wraps the supplied trainer using the ensemble trainer described by these options.Method parameters in org.tribuo.classification.ensemble with type arguments of type LabelModifier and TypeMethodDescriptionFullyWeightedVotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions) FullyWeightedVotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions) FullyWeightedVotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights) FullyWeightedVotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights) VotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions) VotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions) VotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights) VotingCombiner.combine
(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights) AdaBoostTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) If the trainer implementsWeightedExamples
then do boosting by weighting, otherwise do boosting by sampling.AdaBoostTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) ClassificationEnsembleOptions.wrapTrainer
(Trainer<Label> trainer) Wraps the supplied trainer using the ensemble trainer described by these options.Constructor parameters in org.tribuo.classification.ensemble with type arguments of type LabelModifierConstructorDescriptionAdaBoostTrainer
(Trainer<Label> trainer, int numMembers) Constructs an adaboost trainer using the supplied weak learner trainer and the specified number of boosting rounds.AdaBoostTrainer
(Trainer<Label> trainer, int numMembers, long seed) Constructs an adaboost trainer using the supplied weak learner trainer, the specified number of boosting rounds and the supplied seed. -
Uses of Label in org.tribuo.classification.evaluation
Subclasses with type arguments of type Label in org.tribuo.classification.evaluationModifier and TypeClassDescriptionfinal class
static final class
The context for aLabelMetric
is aConfusionMatrix
.Subinterfaces with type arguments of type Label in org.tribuo.classification.evaluationModifier and TypeInterfaceDescriptioninterface
Adds multi-class classification specific metrics toClassifierEvaluation
.Classes in org.tribuo.classification.evaluation that implement interfaces with type arguments of type LabelMethods in org.tribuo.classification.evaluation that return types with arguments of type LabelModifier and TypeMethodDescriptionLabelMetric.Context.getCM()
Gets the confusion matrix.LabelConfusionMatrix.getDomain()
LabelMetrics.getImpl()
Returns the implementing function for this metric.LabelConfusionMatrix.getLabelOrder()
Gets the current label order.LabelMetric.getTarget()
LabelConfusionMatrix.observed()
Methods in org.tribuo.classification.evaluation with parameters of type LabelModifier and TypeMethodDescriptiondouble
The per label accuracy of the evaluation.double
Area under the ROC curve.static double
LabelMetrics.AUCROC
(Label label, List<Prediction<Label>> predictions) Area under the ROC curve.double
LabelEvaluation.averagedPrecision
(Label label) 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.static double
LabelMetrics.averagedPrecision
(Label label, List<Prediction<Label>> predictions) double
double
double
LabelEvaluation.precisionRecallCurve
(Label label) Calculates the Precision Recall curve for a single label.static LabelEvaluationUtil.PRCurve
LabelMetrics.precisionRecallCurve
(Label label, List<Prediction<Label>> predictions) double
double
double
Method parameters in org.tribuo.classification.evaluation with type arguments of type LabelModifier and TypeMethodDescriptionstatic double
LabelMetrics.AUCROC
(Label label, List<Prediction<Label>> predictions) Area under the ROC curve.static double
LabelMetrics.AUCROC
(MetricTarget<Label> tgt, List<Prediction<Label>> predictions) Area under the ROC curve.static double
LabelMetrics.AUCROC
(MetricTarget<Label> tgt, List<Prediction<Label>> predictions) Area under the ROC curve.static double
LabelMetrics.averagedPrecision
(Label label, List<Prediction<Label>> predictions) static double
LabelMetrics.averagedPrecision
(MetricTarget<Label> tgt, List<Prediction<Label>> predictions) static double
LabelMetrics.averagedPrecision
(MetricTarget<Label> tgt, List<Prediction<Label>> predictions) protected LabelMetric.Context
LabelEvaluator.createContext
(Model<Label> model, List<Prediction<Label>> predictions) protected LabelMetric.Context
LabelEvaluator.createContext
(Model<Label> model, List<Prediction<Label>> predictions) LabelMetric.createContext
(Model<Label> model, List<Prediction<Label>> predictions) LabelMetric.createContext
(Model<Label> model, List<Prediction<Label>> predictions) protected LabelEvaluation
LabelEvaluator.createEvaluation
(LabelMetric.Context ctx, Map<MetricID<Label>, Double> results, EvaluationProvenance provenance) protected Set
<LabelMetric> LabelEvaluator.createMetrics
(Model<Label> model) LabelMetrics.forTarget
(MetricTarget<Label> tgt) Gets the LabelMetric wrapped around the supplied MetricTarget.static LabelEvaluationUtil.PRCurve
LabelMetrics.precisionRecallCurve
(Label label, List<Prediction<Label>> predictions) void
LabelConfusionMatrix.setLabelOrder
(List<Label> newLabelOrder) Sets the label order used inLabelConfusionMatrix.toString()
.Constructor parameters in org.tribuo.classification.evaluation with type arguments of type LabelModifierConstructorDescriptionConstructs a context and compute the confusion matrix using the specified model and predictions.Constructs a context and compute the confusion matrix using the specified model and predictions.Context
(SequenceModel<Label> model, List<Prediction<Label>> predictions) Constructs a context and compute the confusion matrix using the specified model and predictions.Context
(SequenceModel<Label> model, List<Prediction<Label>> predictions) Constructs a context and compute the confusion matrix using the specified model and predictions.LabelConfusionMatrix
(ImmutableOutputInfo<Label> domain, List<Prediction<Label>> predictions) Creates a confusion matrix from the supplied predictions and label info.LabelConfusionMatrix
(ImmutableOutputInfo<Label> domain, List<Prediction<Label>> predictions) Creates a confusion matrix from the supplied predictions and label info.LabelConfusionMatrix
(Model<Label> model, List<Prediction<Label>> predictions) Creates a confusion matrix from the supplied predictions, using the label info from the supplied model.LabelConfusionMatrix
(Model<Label> model, List<Prediction<Label>> predictions) Creates a confusion matrix from the supplied predictions, using the label info from the supplied model.LabelMetric
(MetricTarget<Label> tgt, String name, ToDoubleBiFunction<MetricTarget<Label>, LabelMetric.Context> impl) Construct a newLabelMetric
for the supplied metric target, using the supplied function.LabelMetric
(MetricTarget<Label> tgt, String name, ToDoubleBiFunction<MetricTarget<Label>, LabelMetric.Context> impl) Construct a newLabelMetric
for the supplied metric target, using the supplied function. -
Uses of Label in org.tribuo.classification.example
Classes in org.tribuo.classification.example that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionclass
The base class for the 2d binary classification data sources inorg.tribuo.classification.example
.Fields in org.tribuo.classification.example declared as LabelModifier and TypeFieldDescriptionstatic final Label
DemoLabelDataSource.FIRST_CLASS
The first class.static final Label
DemoLabelDataSource.SECOND_CLASS
The second class.Fields in org.tribuo.classification.example with type parameters of type LabelMethods in org.tribuo.classification.example that return types with arguments of type LabelModifier and TypeMethodDescriptionLabelledDataGenerator.binarySparseTrainTest()
Generates a pair of datasets with sparse features and unknown features in the test data.LabelledDataGenerator.binarySparseTrainTest()
Generates a pair of datasets with sparse features and unknown features in the test data.LabelledDataGenerator.binarySparseTrainTest
(double negate) Generates a pair of datasets with sparse features and unknown features in the test data.LabelledDataGenerator.binarySparseTrainTest
(double negate) Generates a pair of datasets with sparse features and unknown features in the test data.LabelledDataGenerator.denseTrainTest()
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}.LabelledDataGenerator.denseTrainTest()
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}.LabelledDataGenerator.denseTrainTest
(double negate) 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}.LabelledDataGenerator.denseTrainTest
(double negate) 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}.LabelledDataGenerator.emptyExample()
Generates an example with no features.CheckerboardDataSource.generate()
ConcentricCirclesDataSource.generate()
DemoLabelDataSource.generate()
Generates the examples using the configured fields.GaussianLabelDataSource.generate()
InterlockingCrescentsDataSource.generate()
NoisyInterlockingCrescentsDataSource.generate()
LabelledDataGenerator.invalidSparseExample()
Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.DemoLabelDataSource.iterator()
LabelledDataGenerator.sparseTrainTest()
Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.LabelledDataGenerator.sparseTrainTest()
Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.LabelledDataGenerator.sparseTrainTest
(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.LabelledDataGenerator.sparseTrainTest
(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data. -
Uses of Label in org.tribuo.classification.experiments
Classes in org.tribuo.classification.experiments that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionclass
Aggregates all the classification algorithms.Fields in org.tribuo.classification.experiments with type parameters of type LabelModifier and TypeFieldDescriptionConfigurableTrainTest.ConfigurableTrainTestOptions.trainer
Load a trainer from the config file.Methods in org.tribuo.classification.experiments that return types with arguments of type LabelModifier and TypeMethodDescriptionAllTrainerOptions.getTrainer()
Test.load
(Test.ConfigurableTestOptions o) Loads in the model and the dataset from the options.Test.load
(Test.ConfigurableTestOptions o) Loads in the model and the dataset from the options.ConfigurableTrainTest.processWeights
(List<String> input) Converts the weight text input format into an object suitable for use in a Trainer. -
Uses of Label in org.tribuo.classification.explanations
Methods in org.tribuo.classification.explanations that return types with arguments of type LabelModifier and TypeMethodDescriptionExplanation.getPrediction()
The original model's prediction which is being explained.Method parameters in org.tribuo.classification.explanations with type arguments of type Label -
Uses of Label in org.tribuo.classification.explanations.lime
Fields in org.tribuo.classification.explanations.lime with type parameters of type LabelMethods in org.tribuo.classification.explanations.lime that return types with arguments of type LabelModifier and TypeMethodDescriptionLIMEExplanation.getPrediction()
LIMEBase.samplePoint
(Random rng, ImmutableFeatureMap fMap, long numTrainingExamples, SparseVector input) Samples a single example from the supplied feature map and input vector.Method parameters in org.tribuo.classification.explanations.lime with type arguments of type LabelModifier and TypeMethodDescriptionprotected com.oracle.labs.mlrg.olcut.util.Pair
<LIMEExplanation, List<Example<Regressor>>> LIMEBase.explainWithSamples
(Example<Label> example) static Regressor
LIMEBase.transformOutput
(Prediction<Label> prediction) Transforms aPrediction
for a multiclass problem into aRegressor
output which represents the probability for each class.Constructor parameters in org.tribuo.classification.explanations.lime with type arguments of type LabelModifierConstructorDescriptionLIMEBase
(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples) Constructs a LIME explainer for a model which uses tabular data (i.e., no special treatment for text features).LIMEColumnar
(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples, RowProcessor<Label> exampleGenerator, Tokenizer tokenizer) Constructs a LIME explainer for a model which uses the columnar data processing system.LIMEColumnar
(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples, RowProcessor<Label> exampleGenerator, Tokenizer tokenizer) Constructs a LIME explainer for a model which uses the columnar data processing system.LIMEExplanation
(SparseModel<Regressor> model, Prediction<Label> prediction, RegressionEvaluation evaluation) Constructs a LIME explanation.LIMEText
(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples, TextFeatureExtractor<Label> extractor, Tokenizer tokenizer) Constructs a LIME explainer for a model which uses text data.LIMEText
(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples, TextFeatureExtractor<Label> extractor, Tokenizer tokenizer) Constructs a LIME explainer for a model which uses text data. -
Uses of Label in org.tribuo.classification.liblinear
Subclasses with type arguments of type Label in org.tribuo.classification.liblinearModifier and TypeClassDescriptionclass
AModel
which wraps a LibLinear-java classification model.class
ATrainer
which wraps a liblinear-java classifier trainer.Classes in org.tribuo.classification.liblinear that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionfinal class
The carrier type for liblinear classification modes.Methods in org.tribuo.classification.liblinear that return types with arguments of type LabelModifier and TypeMethodDescriptionprotected LibLinearModel
<Label> LibLinearClassificationTrainer.createModel
(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models) LibLinearClassificationModel.innerGetExcuse
(Example<Label> e, double[][] allFeatureWeights) The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.Method parameters in org.tribuo.classification.liblinear with type arguments of type LabelModifier and TypeMethodDescriptionprotected LibLinearModel
<Label> LibLinearClassificationTrainer.createModel
(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models) protected com.oracle.labs.mlrg.olcut.util.Pair
<de.bwaldvogel.liblinear.FeatureNode[][], double[][]> LibLinearClassificationTrainer.extractData
(Dataset<Label> data, ImmutableOutputInfo<Label> outputInfo, ImmutableFeatureMap featureMap) protected com.oracle.labs.mlrg.olcut.util.Pair
<de.bwaldvogel.liblinear.FeatureNode[][], double[][]> LibLinearClassificationTrainer.extractData
(Dataset<Label> data, ImmutableOutputInfo<Label> outputInfo, ImmutableFeatureMap featureMap) LibLinearClassificationModel.innerGetExcuse
(Example<Label> e, double[][] allFeatureWeights) The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.void
LibLinearClassificationTrainer.setLabelWeights
(Map<Label, Float> weights) protected de.bwaldvogel.liblinear.Parameter
LibLinearClassificationTrainer.setupParameters
(ImmutableOutputInfo<Label> labelIDMap) -
Uses of Label in org.tribuo.classification.libsvm
Subclasses with type arguments of type Label in org.tribuo.classification.libsvmModifier and TypeClassDescriptionclass
A classification model that uses an underlying LibSVM model to make the predictions.class
A trainer for classification models that uses LibSVM.Classes in org.tribuo.classification.libsvm that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionclass
The carrier type for LibSVM classification modes.Methods in org.tribuo.classification.libsvm that return types with arguments of type LabelModifier and TypeMethodDescriptionprotected LibSVMModel
<Label> LibSVMClassificationTrainer.createModel
(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> outputIDInfo, List<libsvm.svm_model> models) Method parameters in org.tribuo.classification.libsvm with type arguments of type LabelModifier and TypeMethodDescriptionprotected LibSVMModel
<Label> LibSVMClassificationTrainer.createModel
(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> outputIDInfo, List<libsvm.svm_model> models) protected com.oracle.labs.mlrg.olcut.util.Pair
<libsvm.svm_node[][], double[][]> LibSVMClassificationTrainer.extractData
(Dataset<Label> data, ImmutableOutputInfo<Label> outputInfo, ImmutableFeatureMap featureMap) protected com.oracle.labs.mlrg.olcut.util.Pair
<libsvm.svm_node[][], double[][]> LibSVMClassificationTrainer.extractData
(Dataset<Label> data, ImmutableOutputInfo<Label> outputInfo, ImmutableFeatureMap featureMap) void
LibSVMClassificationTrainer.setLabelWeights
(Map<Label, Float> weights) protected libsvm.svm_parameter
LibSVMClassificationTrainer.setupParameters
(ImmutableOutputInfo<Label> outputIDInfo) Constructor parameters in org.tribuo.classification.libsvm with type arguments of type LabelModifierConstructorDescriptionLibSVMClassificationTrainer
(SVMParameters<Label> parameters) Constructs a classification LibSVM trainer using the specified parameters andTrainer.DEFAULT_SEED
.LibSVMClassificationTrainer
(SVMParameters<Label> parameters, long seed) Constructs a classification LibSVM trainer using the specified parameters and seed. -
Uses of Label in org.tribuo.classification.mnb
Subclasses with type arguments of type Label in org.tribuo.classification.mnbModifier and TypeClassDescriptionclass
AModel
for multinomial Naive Bayes with Laplace smoothing.Classes in org.tribuo.classification.mnb that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionclass
ATrainer
which trains a multinomial Naive Bayes model with Laplace smoothing.Methods in org.tribuo.classification.mnb that return types with arguments of type LabelModifier and TypeMethodDescriptionMultinomialNaiveBayesTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) MultinomialNaiveBayesTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) Method parameters in org.tribuo.classification.mnb with type arguments of type LabelModifier and TypeMethodDescriptionMultinomialNaiveBayesTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) MultinomialNaiveBayesTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) -
Uses of Label in org.tribuo.classification.sequence
Subclasses with type arguments of type Label in org.tribuo.classification.sequenceModifier and TypeClassDescriptionclass
A Sequence model which can provide confidence predictions for subsequence predictions.class
A sequence evaluator for labels.Classes in org.tribuo.classification.sequence that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionclass
A class that can be used to evaluate a sequence label classification model element wise on a given set of data.Fields in org.tribuo.classification.sequence with type parameters of type LabelModifier and TypeFieldDescriptionSeqTrainTest.SeqTrainTestOptions.trainer
Name of the trainer in the configuration file.Methods in org.tribuo.classification.sequence that return types with arguments of type LabelModifier and TypeMethodDescriptionLabelSequenceEvaluation.asMap()
LabelSequenceEvaluation.getConfusionMatrix()
Gets the confusion matrix backing this evaluation.LabelSequenceEvaluation.getPredictions()
Gets the flattened predictions.Methods in org.tribuo.classification.sequence with parameters of type LabelModifier and TypeMethodDescriptiondouble
Gets the accuracy for this label.double
Note: confusion is not stored in the underlying map, so it won't show up in aggregation.double
The F1 for this label.double
The false negative count for this label.double
The false positive count for this label.double
The precision for this label.double
The recall for this label.double
The true negative count for this label.double
Gets the true positive count for that label.Method parameters in org.tribuo.classification.sequence with type arguments of type LabelModifier and TypeMethodDescriptionprotected LabelMetric.Context
LabelSequenceEvaluator.createContext
(SequenceModel<Label> model, List<List<Prediction<Label>>> predictions) protected LabelMetric.Context
LabelSequenceEvaluator.createContext
(SequenceModel<Label> model, List<List<Prediction<Label>>> predictions) protected LabelSequenceEvaluation
LabelSequenceEvaluator.createEvaluation
(LabelMetric.Context ctx, Map<MetricID<Label>, Double> results, EvaluationProvenance provenance) protected Set
<LabelMetric> LabelSequenceEvaluator.createMetrics
(SequenceModel<Label> model) static <SUB extends ConfidencePredictingSequenceModel.Subsequence>
List<Double> ConfidencePredictingSequenceModel.multiplyWeights
(List<Prediction<Label>> predictions, List<SUB> subsequences) A scoring method which multiplies together the per prediction scores.abstract <SUB extends ConfidencePredictingSequenceModel.Subsequence>
List<Double> ConfidencePredictingSequenceModel.scoreSubsequences
(SequenceExample<Label> example, List<Prediction<Label>> predictions, List<SUB> subsequences) The scoring function for the subsequences.abstract <SUB extends ConfidencePredictingSequenceModel.Subsequence>
List<Double> ConfidencePredictingSequenceModel.scoreSubsequences
(SequenceExample<Label> example, List<Prediction<Label>> predictions, List<SUB> subsequences) The scoring function for the subsequences.Constructor parameters in org.tribuo.classification.sequence with type arguments of type LabelModifierConstructorDescriptionprotected
ConfidencePredictingSequenceModel
(String name, ModelProvenance description, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> labelIDMap) Constructs a ConfidencePredictingSequenceModel with the supplied parameters.protected
LabelSequenceEvaluation
(Map<MetricID<Label>, Double> results, LabelMetric.Context ctx, EvaluationProvenance provenance) Constructs a LabelSequenceEvaluation using the supplied parameters. -
Uses of Label in org.tribuo.classification.sequence.example
Methods in org.tribuo.classification.sequence.example that return types with arguments of type LabelModifier and TypeMethodDescriptionstatic SequenceExample
<Label> SequenceDataGenerator.generateEmptyExample()
This generates a sequence example with no examples.static SequenceExample
<Label> SequenceDataGenerator.generateGorillaA()
Generates a sequence example with a mixture of features and three labels "O", "Status" and "Monkey".static SequenceExample
<Label> SequenceDataGenerator.generateGorillaB()
Generates a sequence example with a mixture of features and three labels "O", "Status" and "Monkey".static MutableSequenceDataset
<Label> SequenceDataGenerator.generateGorillaDataset
(int numCopies) Generates a simple dataset consisting of numCopies repeats of two sequences.static SequenceExample
<Label> SequenceDataGenerator.generateInvalidExample()
This generates a sequence example with features that are unused by the training data.static SequenceExample
<Label> SequenceDataGenerator.generateOtherInvalidExample()
This generates a sequence example where the first example has no features. -
Uses of Label in org.tribuo.classification.sequence.viterbi
Subclasses with type arguments of type Label in org.tribuo.classification.sequence.viterbiClasses in org.tribuo.classification.sequence.viterbi that implement interfaces with type arguments of type LabelMethods in org.tribuo.classification.sequence.viterbi that return types with arguments of type LabelModifier and TypeMethodDescriptionViterbiModel.getTopLabels
(Map<String, Label> distribution) ViterbiModel.getTopLabels
(Map<String, Label> distribution, int stackSize) List
<List<Prediction<Label>>> ViterbiModel.predict
(SequenceDataset<Label> examples) ViterbiModel.predict
(SequenceExample<Label> examples) ViterbiTrainer.train
(SequenceDataset<Label> dataset, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) The viterbi train method is unique because it delegates to a regularModel
train method, but before it does, it adds features derived from preceding labels.Method parameters in org.tribuo.classification.sequence.viterbi with type arguments of type LabelModifier and TypeMethodDescriptionDefaultFeatureExtractor.extractFeatures
(List<Label> previousOutcomes, double value) LabelFeatureExtractor.extractFeatures
(List<Label> previousOutcomes, double value) Generates features based on the previously produced labels.NoopFeatureExtractor.extractFeatures
(List<Label> previousOutcomes, double value) ViterbiTrainerOptions.getSequenceTrainer
(Trainer<Label> innerTrainer) Creates a viterbi trainer wrapping the supplied label trainer.ViterbiModel.getTopLabels
(Map<String, Label> distribution) ViterbiModel.getTopLabels
(Map<String, Label> distribution, int stackSize) List
<List<Prediction<Label>>> ViterbiModel.predict
(SequenceDataset<Label> examples) ViterbiModel.predict
(SequenceExample<Label> examples) ViterbiTrainer.train
(SequenceDataset<Label> dataset, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) The viterbi train method is unique because it delegates to a regularModel
train method, but before it does, it adds features derived from preceding labels.Constructor parameters in org.tribuo.classification.sequence.viterbi with type arguments of type LabelModifierConstructorDescriptionViterbiTrainer
(Trainer<Label> trainer, LabelFeatureExtractor labelFeatureExtractor, int stackSize, ViterbiModel.ScoreAggregation scoreAggregation) Constructs a ViterbiTrainer wrapping the specified trainer.ViterbiTrainer
(Trainer<Label> trainer, LabelFeatureExtractor labelFeatureExtractor, ViterbiModel.ScoreAggregation scoreAggregation) Constructs a ViterbiTrainer wrapping the specified trainer, with an unbounded stack size. -
Uses of Label in org.tribuo.classification.sgd.crf
Classes in org.tribuo.classification.sgd.crf that implement interfaces with type arguments of type LabelMethods in org.tribuo.classification.sgd.crf that return types with arguments of type LabelMethod parameters in org.tribuo.classification.sgd.crf with type arguments of type LabelModifier and TypeMethodDescriptionstatic com.oracle.labs.mlrg.olcut.util.Pair
<int[], SparseVector[]> CRFModel.convert
(SequenceExample<Label> example, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> labelIDMap) Deprecated.static com.oracle.labs.mlrg.olcut.util.Pair
<int[], SparseVector[]> CRFModel.convert
(SequenceExample<Label> example, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> labelIDMap) Deprecated.As it's replaced withCRFModel.convertToVector(org.tribuo.sequence.SequenceExample<T>, org.tribuo.ImmutableFeatureMap)
which is more flexible.static com.oracle.labs.mlrg.olcut.util.Pair
<int[], SGDVector[]> CRFModel.convertToVector
(SequenceExample<Label> example, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> labelIDMap) Converts aSequenceExample
into an array ofSGDVector
s and labels suitable for CRF prediction.static com.oracle.labs.mlrg.olcut.util.Pair
<int[], SGDVector[]> CRFModel.convertToVector
(SequenceExample<Label> example, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> labelIDMap) Converts aSequenceExample
into an array ofSGDVector
s and labels suitable for CRF prediction.CRFModel.predict
(SequenceExample<Label> example) CRFModel.scoreChunks
(SequenceExample<Label> example, List<Chunk> chunks) Scores the chunks using constrained belief propagation.<SUB extends ConfidencePredictingSequenceModel.Subsequence>
List<Double> CRFModel.scoreSubsequences
(SequenceExample<Label> example, List<Prediction<Label>> predictions, List<SUB> subsequences) <SUB extends ConfidencePredictingSequenceModel.Subsequence>
List<Double> CRFModel.scoreSubsequences
(SequenceExample<Label> example, List<Prediction<Label>> predictions, List<SUB> subsequences) CRFTrainer.train
(SequenceDataset<Label> sequenceExamples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) -
Uses of Label in org.tribuo.classification.sgd.fm
Subclasses with type arguments of type Label in org.tribuo.classification.sgd.fmModifier and TypeClassDescriptionclass
The inference time version of a factorization machine trained using SGD.class
A trainer for a classification factorization machine using SGD.Methods in org.tribuo.classification.sgd.fm that return types with arguments of type LabelModifier and TypeMethodDescriptionMethods in org.tribuo.classification.sgd.fm with parameters of type LabelModifier and TypeMethodDescriptionprotected Integer
FMClassificationTrainer.getTarget
(ImmutableOutputInfo<Label> outputInfo, Label output) Method parameters in org.tribuo.classification.sgd.fm with type arguments of type LabelModifier and TypeMethodDescriptionprotected FMClassificationModel
FMClassificationTrainer.createModel
(String name, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Label> outputInfo, FMParameters parameters) protected Integer
FMClassificationTrainer.getTarget
(ImmutableOutputInfo<Label> outputInfo, Label output) -
Uses of Label in org.tribuo.classification.sgd.kernel
Subclasses with type arguments of type Label in org.tribuo.classification.sgd.kernelModifier and TypeClassDescriptionclass
The inference time version of a kernel model trained using Pegasos.Classes in org.tribuo.classification.sgd.kernel that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionclass
A trainer for a kernelised model using the Pegasos optimiser.Methods in org.tribuo.classification.sgd.kernel that return types with arguments of type LabelModifier and TypeMethodDescriptionMethod parameters in org.tribuo.classification.sgd.kernel with type arguments of type LabelModifier and TypeMethodDescriptionKernelSVMTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) KernelSVMTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) -
Uses of Label in org.tribuo.classification.sgd.linear
Subclasses with type arguments of type Label in org.tribuo.classification.sgd.linearModifier and TypeClassDescriptionclass
The inference time version of a linear model trained using SGD.class
A trainer for a linear classifier using SGD.Methods in org.tribuo.classification.sgd.linear that return types with arguments of type LabelModifier and TypeMethodDescriptionMethods in org.tribuo.classification.sgd.linear with parameters of type LabelModifier and TypeMethodDescriptionprotected Integer
LinearSGDTrainer.getTarget
(ImmutableOutputInfo<Label> outputInfo, Label output) Method parameters in org.tribuo.classification.sgd.linear with type arguments of type LabelModifier and TypeMethodDescriptionprotected LinearSGDModel
LinearSGDTrainer.createModel
(String name, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Label> outputInfo, LinearParameters parameters) protected Integer
LinearSGDTrainer.getTarget
(ImmutableOutputInfo<Label> outputInfo, Label output) -
Uses of Label in org.tribuo.classification.xgboost
Subclasses with type arguments of type Label in org.tribuo.classification.xgboostModifier and TypeClassDescriptionfinal class
ATrainer
which wraps the XGBoost training procedure.Classes in org.tribuo.classification.xgboost that implement interfaces with type arguments of type LabelMethods in org.tribuo.classification.xgboost that return types with arguments of type LabelModifier and TypeMethodDescriptionXGBoostClassificationConverter.convertBatchOutput
(ImmutableOutputInfo<Label> info, List<float[][]> probabilitiesList, int[] numValidFeatures, Example<Label>[] examples) XGBoostClassificationConverter.convertOutput
(ImmutableOutputInfo<Label> info, List<float[]> probabilitiesList, int numValidFeatures, Example<Label> example) XGBoostClassificationTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) XGBoostClassificationTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) Method parameters in org.tribuo.classification.xgboost with type arguments of type LabelModifier and TypeMethodDescriptionXGBoostClassificationConverter.convertBatchOutput
(ImmutableOutputInfo<Label> info, List<float[][]> probabilitiesList, int[] numValidFeatures, Example<Label>[] examples) XGBoostClassificationConverter.convertOutput
(ImmutableOutputInfo<Label> info, List<float[]> probabilitiesList, int numValidFeatures, Example<Label> example) XGBoostClassificationConverter.convertOutput
(ImmutableOutputInfo<Label> info, List<float[]> probabilitiesList, int numValidFeatures, Example<Label> example) XGBoostClassificationTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) XGBoostClassificationTrainer.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) -
Uses of Label in org.tribuo.common.nearest
Classes in org.tribuo.common.nearest that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionclass
CLI Options for training a k-nearest neighbour predictor.Methods in org.tribuo.common.nearest that return types with arguments of type Label -
Uses of Label in org.tribuo.interop.oci
Classes in org.tribuo.interop.oci that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionfinal class
Methods in org.tribuo.interop.oci that return types with arguments of type LabelModifier and TypeMethodDescriptionOCILabelConverter.convertOutput
(DenseMatrix scores, int[] numValidFeatures, List<Example<Label>> examples, ImmutableOutputInfo<Label> outputIDInfo) OCILabelConverter.convertOutput
(DenseVector scores, int numValidFeature, Example<Label> example, ImmutableOutputInfo<Label> outputIDInfo) Method parameters in org.tribuo.interop.oci with type arguments of type LabelModifier and TypeMethodDescriptionOCILabelConverter.convertOutput
(DenseMatrix scores, int[] numValidFeatures, List<Example<Label>> examples, ImmutableOutputInfo<Label> outputIDInfo) OCILabelConverter.convertOutput
(DenseMatrix scores, int[] numValidFeatures, List<Example<Label>> examples, ImmutableOutputInfo<Label> outputIDInfo) OCILabelConverter.convertOutput
(DenseVector scores, int numValidFeature, Example<Label> example, ImmutableOutputInfo<Label> outputIDInfo) OCILabelConverter.convertOutput
(DenseVector scores, int numValidFeature, Example<Label> example, ImmutableOutputInfo<Label> outputIDInfo) -
Uses of Label in org.tribuo.interop.onnx
Classes in org.tribuo.interop.onnx that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionclass
Methods in org.tribuo.interop.onnx that return LabelModifier and TypeMethodDescriptionLabelTransformer.transformToOutput
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo) Methods in org.tribuo.interop.onnx that return types with arguments of type LabelModifier and TypeMethodDescriptionLabelTransformer.transformToBatchOutput
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo) LabelTransformer.transformToBatchPrediction
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo, int[] numValidFeatures, List<Example<Label>> examples) LabelTransformer.transformToPrediction
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo, int numValidFeatures, Example<Label> example) Method parameters in org.tribuo.interop.onnx with type arguments of type LabelModifier and TypeMethodDescriptionprotected float[][]
LabelOneVOneTransformer.getBatchPredictions
(List<ai.onnxruntime.OnnxValue> inputs, ImmutableOutputInfo<Label> outputIDInfo) Rationalises the output of an onnx model into a standard format suitable for downstream work in Tribuo.protected float[][]
LabelTransformer.getBatchPredictions
(List<ai.onnxruntime.OnnxValue> inputs, ImmutableOutputInfo<Label> outputIDInfo) Rationalises the output of an onnx model into a standard format suitable for downstream work in Tribuo.LabelTransformer.transformToBatchOutput
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo) LabelTransformer.transformToBatchPrediction
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo, int[] numValidFeatures, List<Example<Label>> examples) LabelTransformer.transformToOutput
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo) LabelTransformer.transformToPrediction
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo, int numValidFeatures, Example<Label> example) LabelTransformer.transformToPrediction
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo, int numValidFeatures, Example<Label> example) -
Uses of Label in org.tribuo.interop.tensorflow
Classes in org.tribuo.interop.tensorflow that implement interfaces with type arguments of type LabelModifier and TypeClassDescriptionclass
Can convert aLabel
into aTensor
containing one hot encoding of the label and can convert aTFloat16
orTFloat32
into aPrediction
or aLabel
.Methods in org.tribuo.interop.tensorflow that return LabelModifier and TypeMethodDescriptionLabelConverter.convertToOutput
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Label> outputIDInfo) Methods in org.tribuo.interop.tensorflow that return types with arguments of type LabelModifier and TypeMethodDescriptionLabelConverter.convertToBatchOutput
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Label> outputIDInfo) LabelConverter.convertToBatchPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Label> outputIDInfo, int[] numValidFeatures, List<Example<Label>> examples) LabelConverter.convertToPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Label> outputIDInfo, int numValidFeatures, Example<Label> example) Methods in org.tribuo.interop.tensorflow with parameters of type LabelModifier and TypeMethodDescriptionorg.tensorflow.Tensor
LabelConverter.convertToTensor
(Label example, ImmutableOutputInfo<Label> outputIDInfo) Method parameters in org.tribuo.interop.tensorflow with type arguments of type LabelModifier and TypeMethodDescriptionLabelConverter.convertToBatchOutput
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Label> outputIDInfo) LabelConverter.convertToBatchPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Label> outputIDInfo, int[] numValidFeatures, List<Example<Label>> examples) LabelConverter.convertToBatchPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Label> outputIDInfo, int[] numValidFeatures, List<Example<Label>> examples) LabelConverter.convertToOutput
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Label> outputIDInfo) LabelConverter.convertToPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Label> outputIDInfo, int numValidFeatures, Example<Label> example) LabelConverter.convertToPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Label> outputIDInfo, int numValidFeatures, Example<Label> example) org.tensorflow.Tensor
LabelConverter.convertToTensor
(List<Example<Label>> examples, ImmutableOutputInfo<Label> outputIDInfo) org.tensorflow.Tensor
LabelConverter.convertToTensor
(List<Example<Label>> examples, ImmutableOutputInfo<Label> outputIDInfo) org.tensorflow.Tensor
LabelConverter.convertToTensor
(Label example, ImmutableOutputInfo<Label> outputIDInfo) -
Uses of Label in org.tribuo.multilabel
Fields in org.tribuo.multilabel declared as LabelModifier and TypeFieldDescriptionstatic final Label
MultiLabel.NEGATIVE_LABEL
A Label representing the binary negative label.Methods in org.tribuo.multilabel that return LabelModifier and TypeMethodDescriptionMultiLabel.createLabel
(Label otherLabel) Creates a binary label from this multilabel.Methods in org.tribuo.multilabel that return types with arguments of type LabelModifier and TypeMethodDescriptionMultiLabel.getLabelSet()
The set of labels contained in this multilabel.Methods in org.tribuo.multilabel with parameters of type LabelModifier and TypeMethodDescriptionboolean
Does this MultiLabel contain this Label?MultiLabel.createLabel
(Label otherLabel) Creates a binary label from this multilabel.long
MultiLabelInfo.getLabelCount
(Label label) Get the number of times this Label was observed, or 0 if unknown.MultiLabel.getLabelScore
(Label label) The score for the specified label if present, returns an empty optional otherwise.Method parameters in org.tribuo.multilabel with type arguments of type LabelModifier and TypeMethodDescriptionstatic String
MultiLabelFactory.generateLabelString
(Set<Label> input) Constructors in org.tribuo.multilabel with parameters of type LabelConstructor parameters in org.tribuo.multilabel with type arguments of type LabelModifierConstructorDescriptionMultiLabel
(Set<Label> labels) Builds a MultiLabel object from a Set of Labels.MultiLabel
(Set<Label> labels, double score) Builds a MultiLabel object from a Set of Labels, when the whole set has a score as well as (optionally) the individual labels. -
Uses of Label in org.tribuo.multilabel.baseline
Methods in org.tribuo.multilabel.baseline that return types with arguments of type LabelModifier and TypeMethodDescriptionClassifierChainModel.getLabelOrder()
Returns the training label order.Constructor parameters in org.tribuo.multilabel.baseline with type arguments of type LabelModifierConstructorDescriptionClassifierChainTrainer
(Trainer<Label> innerTrainer, long seed) Builds a classifier chain trainer using the specified member trainer and seed.ClassifierChainTrainer
(Trainer<Label> innerTrainer, List<String> labelOrder) Builds a classifier chain trainer using the specified member trainer and seed.IndependentMultiLabelTrainer
(Trainer<Label> innerTrainer) Constructs an independent multi-label trainer wrapped around the supplied classification trainer. -
Uses of Label in org.tribuo.multilabel.ensemble
Constructor parameters in org.tribuo.multilabel.ensemble with type arguments of type LabelModifierConstructorDescriptionCCEnsembleTrainer
(Trainer<Label> innerTrainer, int numMembers, long seed) Constructs a classifier chain ensemble trainer.
CRFModel.convertToVector(org.tribuo.sequence.SequenceExample<T>, org.tribuo.ImmutableFeatureMap)
which is more flexible.