Uses of Class
org.tribuo.classification.Label

Packages that use Label

Package	Description
org.tribuo.classification	Provides classes and infrastructure for multiclass classification problems.
org.tribuo.classification.baseline	Provides simple baseline multiclass classifiers.
org.tribuo.classification.dtree	Provides implementations of decision trees for classification problems.
org.tribuo.classification.dtree.impl	Provides internal implementation classes for classification decision trees.
org.tribuo.classification.ensemble	Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
org.tribuo.classification.evaluation	Evaluation classes for multi-class classification.
org.tribuo.classification.example	Provides a multiclass data generator used for testing implementations.
org.tribuo.classification.experiments	Provides a set of main methods for interacting with classification tasks.
org.tribuo.classification.explanations	Provides core infrastructure for local model based explanations.
org.tribuo.classification.explanations.lime	Provides an implementation of LIME (Locally Interpretable Model Explanations).
org.tribuo.classification.liblinear	Provides an interface to LibLinear-java for classification problems.
org.tribuo.classification.libsvm	Provides an interface to LibSVM for classification problems.
org.tribuo.classification.mnb	Provides an implementation of multinomial naive bayes (i.e., naive bayes for non-negative count data).
org.tribuo.classification.sequence	Provides infrastructure for SequenceModels which emit Labels at each step of the sequence.
org.tribuo.classification.sequence.example	Provides a classification sequence data generator for smoke testing implementations.
org.tribuo.classification.sequence.viterbi	Provides an implementation of Viterbi for generating structured outputs, which can sit on top of any Label based classification model.
org.tribuo.classification.sgd.crf	Provides an implementation of a linear chain CRF trained using Stochastic Gradient Descent.
org.tribuo.classification.sgd.kernel	Provides a SGD implementation of a Kernel SVM using the Pegasos algorithm.
org.tribuo.classification.sgd.linear	Provides an implementation of a classification linear model using Stochastic Gradient Descent.
org.tribuo.classification.xgboost	Provides an interface to XGBoost for classification problems.
org.tribuo.common.nearest	Provides a K-Nearest Neighbours implementation which works across all Tribuo Output types.
org.tribuo.interop.onnx	This package contains a Tribuo wrapper around the ONNX Runtime.
org.tribuo.interop.tensorflow	Provides an interface to Tensorflow, allowing the training of non-sequential models using any supported Tribuo output type.
org.tribuo.multilabel	Provides classes and infrastructure for working with multi-label classification problems.
org.tribuo.multilabel.baseline	Provides an implementation of independent multi-label classification that wraps a Label Trainer and uses it to make independent predictions of each label.

Uses of Label in org.tribuo.classification

Classes in org.tribuo.classification with type parameters of type Label

Modifier and Type	Interface and Description
interface	ClassificationOptions<TRAINER extends Trainer<Label>> An Options that can produce a classification Trainer based on the provided arguments.

Fields in org.tribuo.classification declared as Label

Modifier and Type	Field and Description
static Label	LabelFactory.UNKNOWN_LABEL The singleton unknown label, used for unlablled examples.

Fields in org.tribuo.classification with type parameters of type Label

Modifier and Type	Field and Description
protected Map<String,Label>	LabelInfo.labels The label domain.

Methods in org.tribuo.classification that return Label

Modifier and Type	Method and Description
Label	Label.copy()
<V> Label	LabelFactory.generateOutput(V label) Generates the Label string by calling toString on the input.
Label	ImmutableLabelInfo.getOutput(int id)
Label	LabelFactory.getUnknownOutput()

Methods in org.tribuo.classification that return types with arguments of type Label

Modifier and Type	Method and Description
ImmutableOutputInfo<Label>	LabelFactory.constructInfoForExternalModel(Map<Label,Integer> mapping)
ImmutableOutputInfo<Label>	LabelInfo.generateImmutableOutputInfo()
MutableOutputInfo<Label>	LabelFactory.generateInfo() Generates an empty MutableLabelInfo.
MutableOutputInfo<Label>	LabelInfo.generateMutableOutputInfo()
Set<Label>	LabelInfo.getDomain() Returns the set of possible Labels that this LabelInfo has seen.
Set<Label>	ImmutableLabelInfo.getDomain() Returns the set of possible Labels that this LabelInfo has seen.
Evaluator<Label,LabelEvaluation>	LabelFactory.getEvaluator()
Iterator<com.oracle.labs.mlrg.olcut.util.Pair<Integer,Label>>	ImmutableLabelInfo.iterator()
static Model<Label>	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 Label

Modifier and Type	Method and Description
boolean	Label.fullEquals(Label o)
int	ImmutableLabelInfo.getID(Label output)
long	LabelInfo.getLabelCount(Label label) Gets the count of the supplied label, or 0 if the label is unknown.
void	MutableLabelInfo.observe(Label output)

Method parameters in org.tribuo.classification with type arguments of type Label

Modifier and Type	Method and Description
ImmutableOutputInfo<Label>	LabelFactory.constructInfoForExternalModel(Map<Label,Integer> mapping)
static Model<Label>	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

Methods in org.tribuo.classification.baseline that return types with arguments of type Label

Modifier and Type	Method and Description
Optional<Excuse<Label>>	DummyClassifierModel.getExcuse(Example<Label> example)
Prediction<Label>	DummyClassifierModel.predict(Example<Label> example)
Model<Label>	DummyClassifierTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)

Method parameters in org.tribuo.classification.baseline with type arguments of type Label

Modifier and Type	Method and Description
Optional<Excuse<Label>>	DummyClassifierModel.getExcuse(Example<Label> example)
Prediction<Label>	DummyClassifierModel.predict(Example<Label> example)
Model<Label>	DummyClassifierTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)

Uses of Label in org.tribuo.classification.dtree

Methods in org.tribuo.classification.dtree that return types with arguments of type Label

Modifier and Type	Method and Description
protected AbstractTrainingNode<Label>	CARTClassificationTrainer.mkTrainingNode(Dataset<Label> examples)

Method parameters in org.tribuo.classification.dtree with type arguments of type Label

Modifier and Type	Method and Description
protected AbstractTrainingNode<Label>	CARTClassificationTrainer.mkTrainingNode(Dataset<Label> examples)

Uses of Label in org.tribuo.classification.dtree.impl

Methods in org.tribuo.classification.dtree.impl that return types with arguments of type Label

Modifier and Type	Method and Description
List<AbstractTrainingNode<Label>>	ClassifierTrainingNode.buildTree(int[] featureIDs) Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
Node<Label>	ClassifierTrainingNode.convertTree() Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.

Constructor parameters in org.tribuo.classification.dtree.impl with type arguments of type Label

Constructor and Description
ClassifierTrainingNode(LabelImpurity impurity, Dataset<Label> examples) Constructor which creates the inverted file.

Uses of Label in org.tribuo.classification.ensemble

Fields in org.tribuo.classification.ensemble with type parameters of type Label

Modifier and Type	Field and Description
protected Trainer<Label>	AdaBoostTrainer.innerTrainer

Methods in org.tribuo.classification.ensemble that return types with arguments of type Label

Modifier and Type	Method and Description
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Model<Label>	AdaBoostTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) If the trainer implements WeightedExamples then do boosting by weighting, otherwise do boosting by sampling.
Trainer<Label>	ClassificationEnsembleOptions.wrapTrainer(Trainer<Label> trainer)

Method parameters in org.tribuo.classification.ensemble with type arguments of type Label

Modifier and Type	Method and Description
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Model<Label>	AdaBoostTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) If the trainer implements WeightedExamples then do boosting by weighting, otherwise do boosting by sampling.
Trainer<Label>	ClassificationEnsembleOptions.wrapTrainer(Trainer<Label> trainer)

Constructor parameters in org.tribuo.classification.ensemble with type arguments of type Label

Constructor and Description
AdaBoostTrainer(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

Methods in org.tribuo.classification.evaluation that return types with arguments of type Label

Modifier and Type	Method and Description
ConfusionMatrix<Label>	LabelMetric.Context.getCM()
ImmutableOutputInfo<Label>	LabelConfusionMatrix.getDomain()
ToDoubleBiFunction<MetricTarget<Label>,LabelMetric.Context>	LabelMetrics.getImpl() Returns the implementing function for this metric.
MetricTarget<Label>	LabelMetric.getTarget()

Methods in org.tribuo.classification.evaluation with parameters of type Label

Modifier and Type	Method and Description
double	LabelEvaluation.accuracy(Label label) The per label accuracy of the evaluation.
double	LabelEvaluation.AUCROC(Label label) 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	LabelConfusionMatrix.confusion(Label predicted, Label trueClass)
double	LabelConfusionMatrix.fn(Label cls)
double	LabelConfusionMatrix.fp(Label cls)
LabelEvaluationUtil.PRCurve	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	LabelConfusionMatrix.support(Label label)
double	LabelConfusionMatrix.tn(Label cls)
double	LabelConfusionMatrix.tp(Label cls)

Method parameters in org.tribuo.classification.evaluation with type arguments of type Label

Modifier and Type	Method and Description
static 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)
LabelMetric.Context	LabelMetric.createContext(Model<Label> model, List<Prediction<Label>> predictions)
LabelMetric.Context	LabelMetric.createContext(Model<Label> model, 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)
protected LabelEvaluation	LabelEvaluator.createEvaluation(LabelMetric.Context ctx, Map<MetricID<Label>,Double> results, EvaluationProvenance provenance)
protected Set<LabelMetric>	LabelEvaluator.createMetrics(Model<Label> model)
LabelMetric	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> labelOrder) Sets the label order used in LabelConfusionMatrix.toString().

Constructor parameters in org.tribuo.classification.evaluation with type arguments of type Label

Constructor and Description
Context(Model<Label> model, List<Prediction<Label>> predictions)
Context(Model<Label> model, List<Prediction<Label>> predictions)
Context(SequenceModel<Label> model, List<Prediction<Label>> predictions)
Context(SequenceModel<Label> model, List<Prediction<Label>> 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 new LabelMetric for the supplied metric target, using the supplied function.
LabelMetric(MetricTarget<Label> tgt, String name, ToDoubleBiFunction<MetricTarget<Label>,LabelMetric.Context> impl) Construct a new LabelMetric for the supplied metric target, using the supplied function.

Uses of Label in org.tribuo.classification.example

Methods in org.tribuo.classification.example that return types with arguments of type Label

Modifier and Type	Method and Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest(double negate) Generates a pair of datasets with sparse features and unknown features in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest(double negate) Generates a pair of datasets with sparse features and unknown features in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.denseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.denseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	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}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	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}.
static Example<Label>	LabelledDataGenerator.emptyExample() Generates an example with no features.
static Example<Label>	LabelledDataGenerator.invalidSparseExample() Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.sparseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.sparseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.sparseTrainTest(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	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

Fields in org.tribuo.classification.experiments with type parameters of type Label

Modifier and Type	Field and Description
Trainer<Label>	ConfigurableTrainTest.ConfigurableTrainTestOptions.trainer

Methods in org.tribuo.classification.experiments that return types with arguments of type Label

Modifier and Type	Method and Description
Trainer<Label>	AllTrainerOptions.getTrainer()
static com.oracle.labs.mlrg.olcut.util.Pair<Model<Label>,Dataset<Label>>	Test.load(Test.ConfigurableTestOptions o)
static com.oracle.labs.mlrg.olcut.util.Pair<Model<Label>,Dataset<Label>>	Test.load(Test.ConfigurableTestOptions o)
static Map<Label,Float>	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 Label

Modifier and Type	Method and Description
Prediction<Label>	Explanation.getPrediction() The original model's prediction which is being explained.

Method parameters in org.tribuo.classification.explanations with type arguments of type Label

Modifier and Type	Method and Description
Explanation<T>	TabularExplainer.explain(Example<Label> example) Explain why the supplied Example is classified a certain way.

Uses of Label in org.tribuo.classification.explanations.lime

Fields in org.tribuo.classification.explanations.lime with type parameters of type Label

Modifier and Type	Field and Description
protected Model<Label>	LIMEBase.innerModel

Methods in org.tribuo.classification.explanations.lime that return types with arguments of type Label

Modifier and Type	Method and Description
Prediction<Label>	LIMEExplanation.getPrediction()
static Example<Label>	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 Label

Modifier and Type	Method and Description
LIMEExplanation	LIMEBase.explain(Example<Label> example)
protected com.oracle.labs.mlrg.olcut.util.Pair<LIMEExplanation,List<Example<Regressor>>>	LIMEBase.explainWithSamples(Example<Label> example)
static Regressor	LIMEBase.transformOutput(Prediction<Label> prediction) Transforms a Prediction for a multiclass problem into a Regressor output which represents the probability for each class.

Constructor parameters in org.tribuo.classification.explanations.lime with type arguments of type Label

Constructor and Description
LIMEBase(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)
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

Methods in org.tribuo.classification.liblinear that return types with arguments of type Label

Modifier and Type	Method and Description
protected LibLinearModel<Label>	LibLinearClassificationTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models)
protected Excuse<Label>	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.
Prediction<Label>	LibLinearClassificationModel.predict(Example<Label> example)

Method parameters in org.tribuo.classification.liblinear with type arguments of type Label

Modifier and Type	Method and Description
protected 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)
protected Excuse<Label>	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.
Prediction<Label>	LibLinearClassificationModel.predict(Example<Label> example)
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

Methods in org.tribuo.classification.libsvm that return types with arguments of type Label

Modifier and Type	Method and Description
protected LibSVMModel<Label>	LibSVMClassificationTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> outputIDInfo, List<libsvm.svm_model> models)
Prediction<Label>	LibSVMClassificationModel.predict(Example<Label> example)

Method parameters in org.tribuo.classification.libsvm with type arguments of type Label

Modifier and Type	Method and Description
protected 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)
Prediction<Label>	LibSVMClassificationModel.predict(Example<Label> example)
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 Label

Constructor and Description
LibSVMClassificationTrainer(SVMParameters<Label> parameters)

Uses of Label in org.tribuo.classification.mnb

Methods in org.tribuo.classification.mnb that return types with arguments of type Label

Modifier and Type	Method and Description
Optional<Excuse<Label>>	MultinomialNaiveBayesModel.getExcuse(Example<Label> example)
Prediction<Label>	MultinomialNaiveBayesModel.predict(Example<Label> example)
Model<Label>	MultinomialNaiveBayesTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Method parameters in org.tribuo.classification.mnb with type arguments of type Label

Modifier and Type	Method and Description
Optional<Excuse<Label>>	MultinomialNaiveBayesModel.getExcuse(Example<Label> example)
Prediction<Label>	MultinomialNaiveBayesModel.predict(Example<Label> example)
Model<Label>	MultinomialNaiveBayesTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of Label in org.tribuo.classification.sequence

Methods in org.tribuo.classification.sequence that return types with arguments of type Label

Modifier and Type	Method and Description
Map<MetricID<Label>,Double>	LabelSequenceEvaluation.asMap()
ConfusionMatrix<Label>	LabelSequenceEvaluation.getConfusionMatrix() Gets the confusion matrix backing this evaluation.
List<Prediction<Label>>	LabelSequenceEvaluation.getPredictions() Gets the flattened predictions.

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

Modifier and Type	Method and Description
double	LabelSequenceEvaluation.accuracy(Label label)
double	LabelSequenceEvaluation.confusion(Label predictedLabel, Label trueLabel) Note: confusion is not stored in the underlying map, so it won't show up in aggregation.
double	LabelSequenceEvaluation.f1(Label label)
double	LabelSequenceEvaluation.fn(Label label)
double	LabelSequenceEvaluation.fp(Label label)
double	LabelSequenceEvaluation.precision(Label label)
double	LabelSequenceEvaluation.recall(Label label)
double	LabelSequenceEvaluation.tn(Label label)
double	LabelSequenceEvaluation.tp(Label label)

Method parameters in org.tribuo.classification.sequence with type arguments of type Label

Modifier and Type	Method and Description
protected 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 Label

Constructor and Description
ConfidencePredictingSequenceModel(String name, ModelProvenance description, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> labelIDMap)
LabelSequenceEvaluation(Map<MetricID<Label>,Double> results, LabelMetric.Context ctx, EvaluationProvenance provenance)

Uses of Label in org.tribuo.classification.sequence.example

Methods in org.tribuo.classification.sequence.example that return types with arguments of type Label

Modifier and Type	Method and Description
static SequenceExample<Label>	SequenceDataGenerator.generateEmptyExample() This generates a sequence example with no examples.
static SequenceExample<Label>	SequenceDataGenerator.generateGorillaA()
static SequenceExample<Label>	SequenceDataGenerator.generateGorillaB()
static MutableSequenceDataset<Label>	SequenceDataGenerator.generateGorillaDataset(int numCopies)
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

Methods in org.tribuo.classification.sequence.viterbi that return types with arguments of type Label

Modifier and Type	Method and Description
protected List<Label>	ViterbiModel.getTopLabels(Map<String,Label> distribution)
protected static List<Label>	ViterbiModel.getTopLabels(Map<String,Label> distribution, int stackSize)
List<List<Prediction<Label>>>	ViterbiModel.predict(SequenceDataset<Label> examples)
List<Prediction<Label>>	ViterbiModel.predict(SequenceExample<Label> examples)
SequenceModel<Label>	ViterbiTrainer.train(SequenceDataset<Label> dataset, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) The viterbi train method is unique because it delegates to a regular Model train method, but before it does, it adds features derived from preceding labels.

Method parameters in org.tribuo.classification.sequence.viterbi with type arguments of type Label

Modifier and Type	Method and Description
List<Feature>	NoopFeatureExtractor.extractFeatures(List<Label> previousOutcomes, double value)
List<Feature>	LabelFeatureExtractor.extractFeatures(List<Label> previousOutcomes, double value) Generates features based on the previously produced labels.
List<Feature>	DefaultFeatureExtractor.extractFeatures(List<Label> previousOutcomes, double value)
ViterbiTrainer	ViterbiTrainerOptions.getSequenceTrainer(Trainer<Label> innerTrainer)
protected List<Label>	ViterbiModel.getTopLabels(Map<String,Label> distribution)
protected static List<Label>	ViterbiModel.getTopLabels(Map<String,Label> distribution, int stackSize)
List<List<Prediction<Label>>>	ViterbiModel.predict(SequenceDataset<Label> examples)
List<Prediction<Label>>	ViterbiModel.predict(SequenceExample<Label> examples)
SequenceModel<Label>	ViterbiTrainer.train(SequenceDataset<Label> dataset, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) The viterbi train method is unique because it delegates to a regular Model train method, but before it does, it adds features derived from preceding labels.

Constructor parameters in org.tribuo.classification.sequence.viterbi with type arguments of type Label

Constructor and Description
ViterbiTrainer(Trainer<Label> trainer, LabelFeatureExtractor labelFeatureExtractor, int stackSize, ViterbiModel.ScoreAggregation scoreAggregation)
ViterbiTrainer(Trainer<Label> trainer, LabelFeatureExtractor labelFeatureExtractor, ViterbiModel.ScoreAggregation scoreAggregation)

Uses of Label in org.tribuo.classification.sgd.crf

Methods in org.tribuo.classification.sgd.crf that return types with arguments of type Label

Modifier and Type	Method and Description
List<Prediction<Label>>	CRFModel.predict(SequenceExample<Label> example)

Method parameters in org.tribuo.classification.sgd.crf with type arguments of type Label

Modifier and Type	Method and Description
static com.oracle.labs.mlrg.olcut.util.Pair<int[],SparseVector[]>	CRFModel.convert(SequenceExample<Label> example, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> labelIDMap) Converts a SequenceExample into an array of SparseVectors and labels suitable for CRF prediction.
static com.oracle.labs.mlrg.olcut.util.Pair<int[],SparseVector[]>	CRFModel.convert(SequenceExample<Label> example, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> labelIDMap) Converts a SequenceExample into an array of SparseVectors and labels suitable for CRF prediction.
List<Prediction<Label>>	CRFModel.predict(SequenceExample<Label> example)
List<Double>	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)
CRFModel	CRFTrainer.train(SequenceDataset<Label> sequenceExamples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of Label in org.tribuo.classification.sgd.kernel

Methods in org.tribuo.classification.sgd.kernel that return types with arguments of type Label

Modifier and Type	Method and Description
Optional<Excuse<Label>>	KernelSVMModel.getExcuse(Example<Label> example)
Prediction<Label>	KernelSVMModel.predict(Example<Label> example)

Method parameters in org.tribuo.classification.sgd.kernel with type arguments of type Label

Modifier and Type	Method and Description
Optional<Excuse<Label>>	KernelSVMModel.getExcuse(Example<Label> example)
Prediction<Label>	KernelSVMModel.predict(Example<Label> example)
KernelSVMModel	KernelSVMTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of Label in org.tribuo.classification.sgd.linear

Methods in org.tribuo.classification.sgd.linear that return types with arguments of type Label

Modifier and Type	Method and Description
Optional<Excuse<Label>>	LinearSGDModel.getExcuse(Example<Label> example)
Prediction<Label>	LinearSGDModel.predict(Example<Label> example)
Model<Label>	LinearSGDTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Method parameters in org.tribuo.classification.sgd.linear with type arguments of type Label

Modifier and Type	Method and Description
Optional<Excuse<Label>>	LinearSGDModel.getExcuse(Example<Label> example)
Prediction<Label>	LinearSGDModel.predict(Example<Label> example)
Model<Label>	LinearSGDTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of Label in org.tribuo.classification.xgboost

Methods in org.tribuo.classification.xgboost that return types with arguments of type Label

Modifier and Type	Method and Description
List<Prediction<Label>>	XGBoostClassificationConverter.convertBatchOutput(ImmutableOutputInfo<Label> info, List<float[][]> probabilitiesList, int[] numValidFeatures, Example<Label>[] examples)
Prediction<Label>	XGBoostClassificationConverter.convertOutput(ImmutableOutputInfo<Label> info, List<float[]> probabilitiesList, int numValidFeatures, Example<Label> example)
XGBoostModel<Label>	XGBoostClassificationTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Method parameters in org.tribuo.classification.xgboost with type arguments of type Label

Modifier and Type	Method and Description
List<Prediction<Label>>	XGBoostClassificationConverter.convertBatchOutput(ImmutableOutputInfo<Label> info, List<float[][]> probabilitiesList, int[] numValidFeatures, Example<Label>[] examples)
Prediction<Label>	XGBoostClassificationConverter.convertOutput(ImmutableOutputInfo<Label> info, List<float[]> probabilitiesList, int numValidFeatures, Example<Label> example)
Prediction<Label>	XGBoostClassificationConverter.convertOutput(ImmutableOutputInfo<Label> info, List<float[]> probabilitiesList, int numValidFeatures, Example<Label> example)
XGBoostModel<Label>	XGBoostClassificationTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of Label in org.tribuo.common.nearest

Methods in org.tribuo.common.nearest that return types with arguments of type Label

Modifier and Type	Method and Description
KNNTrainer<Label>	KNNClassifierOptions.getTrainer()

Uses of Label in org.tribuo.interop.onnx

Methods in org.tribuo.interop.onnx that return Label

Modifier and Type	Method and Description
Label	LabelTransformer.transformToOutput(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo)

Methods in org.tribuo.interop.onnx that return types with arguments of type Label

Modifier and Type	Method and Description
List<Label>	LabelTransformer.transformToBatchOutput(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo)
List<Prediction<Label>>	LabelTransformer.transformToBatchPrediction(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo, int[] numValidFeatures, List<Example<Label>> examples)
Prediction<Label>	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 Label

Modifier and Type	Method and Description
List<Label>	LabelTransformer.transformToBatchOutput(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo)
List<Prediction<Label>>	LabelTransformer.transformToBatchPrediction(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo, int[] numValidFeatures, List<Example<Label>> examples)
List<Prediction<Label>>	LabelTransformer.transformToBatchPrediction(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo, int[] numValidFeatures, List<Example<Label>> examples)
Label	LabelTransformer.transformToOutput(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo)
Prediction<Label>	LabelTransformer.transformToPrediction(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo, int numValidFeatures, Example<Label> example)
Prediction<Label>	LabelTransformer.transformToPrediction(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Label> outputIDInfo, int numValidFeatures, Example<Label> example)

Uses of Label in org.tribuo.interop.tensorflow

Methods in org.tribuo.interop.tensorflow that return Label

Modifier and Type	Method and Description
Label	LabelTransformer.transformToOutput(org.tensorflow.Tensor<?> tensor, ImmutableOutputInfo<Label> outputIDInfo)

Methods in org.tribuo.interop.tensorflow that return types with arguments of type Label

Modifier and Type	Method and Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	TrainTest.load(Path trainingPath, Path testingPath, OutputFactory<Label> outputFactory)
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	TrainTest.load(Path trainingPath, Path testingPath, OutputFactory<Label> outputFactory)
List<Label>	LabelTransformer.transformToBatchOutput(org.tensorflow.Tensor<?> tensor, ImmutableOutputInfo<Label> outputIDInfo)
List<Prediction<Label>>	LabelTransformer.transformToBatchPrediction(org.tensorflow.Tensor<?> tensor, ImmutableOutputInfo<Label> outputIDInfo, int[] numValidFeatures, List<Example<Label>> examples)
Prediction<Label>	LabelTransformer.transformToPrediction(org.tensorflow.Tensor<?> tensor, ImmutableOutputInfo<Label> outputIDInfo, int numValidFeatures, Example<Label> example)

Methods in org.tribuo.interop.tensorflow with parameters of type Label

Modifier and Type	Method and Description
org.tensorflow.Tensor<?>	LabelTransformer.transform(Label example, ImmutableOutputInfo<Label> outputIDInfo)

Method parameters in org.tribuo.interop.tensorflow with type arguments of type Label

Modifier and Type	Method and Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	TrainTest.load(Path trainingPath, Path testingPath, OutputFactory<Label> outputFactory)
static void	TrainTest.saveModel(Path outputPath, Model<Label> model)
org.tensorflow.Tensor<?>	LabelTransformer.transform(Label example, ImmutableOutputInfo<Label> outputIDInfo)
org.tensorflow.Tensor<?>	LabelTransformer.transform(List<Example<Label>> examples, ImmutableOutputInfo<Label> outputIDInfo)
org.tensorflow.Tensor<?>	LabelTransformer.transform(List<Example<Label>> examples, ImmutableOutputInfo<Label> outputIDInfo)
List<Label>	LabelTransformer.transformToBatchOutput(org.tensorflow.Tensor<?> tensor, ImmutableOutputInfo<Label> outputIDInfo)
List<Prediction<Label>>	LabelTransformer.transformToBatchPrediction(org.tensorflow.Tensor<?> tensor, ImmutableOutputInfo<Label> outputIDInfo, int[] numValidFeatures, List<Example<Label>> examples)
List<Prediction<Label>>	LabelTransformer.transformToBatchPrediction(org.tensorflow.Tensor<?> tensor, ImmutableOutputInfo<Label> outputIDInfo, int[] numValidFeatures, List<Example<Label>> examples)
Label	LabelTransformer.transformToOutput(org.tensorflow.Tensor<?> tensor, ImmutableOutputInfo<Label> outputIDInfo)
Prediction<Label>	LabelTransformer.transformToPrediction(org.tensorflow.Tensor<?> tensor, ImmutableOutputInfo<Label> outputIDInfo, int numValidFeatures, Example<Label> example)
Prediction<Label>	LabelTransformer.transformToPrediction(org.tensorflow.Tensor<?> tensor, ImmutableOutputInfo<Label> outputIDInfo, int numValidFeatures, Example<Label> example)

Uses of Label in org.tribuo.multilabel

Fields in org.tribuo.multilabel declared as Label

Modifier and Type	Field and Description
static Label	MultiLabel.NEGATIVE_LABEL A Label representing the binary negative label.

Methods in org.tribuo.multilabel that return Label

Modifier and Type	Method and Description
Label	MultiLabel.createLabel(Label otherLabel) Creates a binary label from this multilabel.

Methods in org.tribuo.multilabel that return types with arguments of type Label

Modifier and Type	Method and Description
Set<Label>	MultiLabel.getLabelSet() The set of labels contained in this multilabel.

Methods in org.tribuo.multilabel with parameters of type Label

Modifier and Type	Method and Description
boolean	MultiLabel.contains(Label input) Does this MultiLabel contain this Label?
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.

Method parameters in org.tribuo.multilabel with type arguments of type Label

Modifier and Type	Method and Description
static String	MultiLabelFactory.generateLabelString(Set<Label> input) Generates a comma separated string of labels from a Set of Label.

Constructors in org.tribuo.multilabel with parameters of type Label

Constructor and Description
MultiLabel(Label label) Builds a MultiLabel from a single Label.

Constructor parameters in org.tribuo.multilabel with type arguments of type Label

Constructor and Description
MultiLabel(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

Constructor parameters in org.tribuo.multilabel.baseline with type arguments of type Label

Constructor and Description
IndependentMultiLabelTrainer(Trainer<Label> innerTrainer)