Package | Description |
---|---|
org.tribuo |
Provides the core interfaces and classes for using Tribuo.
|
org.tribuo.anomaly.libsvm |
Provides an interface to LibSVM for anomaly detection problems.
|
org.tribuo.classification.baseline |
Provides simple baseline multiclass classifiers.
|
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
SequenceModel s which
emit Label s at each step of the sequence. |
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.clustering.kmeans |
Provides a multithreaded implementation of K-Means, with a
configurable distance function.
|
org.tribuo.common.liblinear |
Provides base classes for using liblinear from Tribuo.
|
org.tribuo.common.libsvm |
The base interface to LibSVM.
|
org.tribuo.common.nearest |
Provides a K-Nearest Neighbours implementation which works across
all Tribuo
Output types. |
org.tribuo.common.tree |
Provides common functionality for building decision trees, irrespective
of the predicted
Output . |
org.tribuo.common.xgboost |
Provides abstract classes for interfacing with XGBoost abstracting away all the
Output
dependent parts. |
org.tribuo.ensemble |
Provides an interface for model prediction combinations,
two base classes for ensemble models, a base class for
ensemble excuses, and a Bagging implementation.
|
org.tribuo.interop |
This package contains the abstract implementation of an external model
trained by something outside of Tribuo.
|
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.baseline | |
org.tribuo.provenance |
Provides Tribuo specific infrastructure for the
Provenance system which
tracks models and datasets. |
org.tribuo.regression.baseline |
Provides simple baseline regression predictors.
|
org.tribuo.regression.impl | |
org.tribuo.regression.liblinear |
Provides an interface to liblinear for regression problems.
|
org.tribuo.regression.libsvm |
Provides an interface to LibSVM for regression problems.
|
org.tribuo.regression.rtree |
Provides an implementation of decision trees for regression problems.
|
org.tribuo.regression.sgd.linear |
Provides an implementation of linear regression using Stochastic Gradient Descent.
|
org.tribuo.regression.slm |
Provides implementations of sparse linear regression using various forms of regularisation penalty.
|
org.tribuo.sequence |
Provides core classes for working with sequences of
Example s. |
org.tribuo.transform |
Provides infrastructure for applying transformations to a
Dataset . |
Modifier and Type | Field and Description |
---|---|
protected ModelProvenance |
Model.provenance
The model provenance.
|
Modifier and Type | Method and Description |
---|---|
ModelProvenance |
Model.getProvenance() |
Modifier and Type | Method and Description |
---|---|
protected abstract Model<T> |
Model.copy(String newName,
ModelProvenance newProvenance)
Copies a model, replacing it's provenance and name with the supplied values.
|
Constructor and Description |
---|
Model(String name,
ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
boolean generatesProbabilities)
Constructs a new model, storing the supplied fields.
|
SparseModel(String name,
ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
boolean generatesProbabilities,
Map<String,List<String>> activeFeatures)
Constructs a sparse model from the supplied arguments.
|
Modifier and Type | Method and Description |
---|---|
protected LibSVMAnomalyModel |
LibSVMAnomalyModel.copy(String newName,
ModelProvenance newProvenance) |
protected LibSVMModel<Event> |
LibSVMAnomalyTrainer.createModel(ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<Event> outputIDInfo,
List<libsvm.svm_model> models) |
Modifier and Type | Method and Description |
---|---|
protected DummyClassifierModel |
DummyClassifierModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Method and Description |
---|---|
protected LibLinearClassificationModel |
LibLinearClassificationModel.copy(String newName,
ModelProvenance newProvenance) |
protected LibLinearModel<Label> |
LibLinearClassificationTrainer.createModel(ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<Label> outputIDInfo,
List<de.bwaldvogel.liblinear.Model> models) |
Modifier and Type | Method and Description |
---|---|
protected LibSVMClassificationModel |
LibSVMClassificationModel.copy(String newName,
ModelProvenance newProvenance) |
protected LibSVMModel<Label> |
LibSVMClassificationTrainer.createModel(ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<Label> outputIDInfo,
List<libsvm.svm_model> models) |
Modifier and Type | Method and Description |
---|---|
protected MultinomialNaiveBayesModel |
MultinomialNaiveBayesModel.copy(String newName,
ModelProvenance newProvenance) |
Constructor and Description |
---|
ConfidencePredictingSequenceModel(String name,
ModelProvenance description,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<Label> labelIDMap) |
Modifier and Type | Method and Description |
---|---|
protected KernelSVMModel |
KernelSVMModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Method and Description |
---|---|
protected LinearSGDModel |
LinearSGDModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Method and Description |
---|---|
protected KMeansModel |
KMeansModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Method and Description |
---|---|
protected abstract LibLinearModel<T> |
LibLinearTrainer.createModel(ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
List<de.bwaldvogel.liblinear.Model> models)
Construct the appropriate subtype of LibLinearModel for the prediction task.
|
Constructor and Description |
---|
LibLinearModel(String name,
ModelProvenance description,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> labelIDMap,
boolean generatesProbabilities,
List<de.bwaldvogel.liblinear.Model> models)
Constructs a LibLinear model from the supplied arguments.
|
Modifier and Type | Method and Description |
---|---|
protected abstract LibSVMModel<T> |
LibSVMTrainer.createModel(ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
List<libsvm.svm_model> models)
Construct the appropriate subtype of LibSVMModel for the prediction task.
|
Constructor and Description |
---|
LibSVMModel(String name,
ModelProvenance description,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
boolean generatesProbabilities,
List<libsvm.svm_model> models)
Constructs a LibSVMModel from the supplied arguments.
|
Modifier and Type | Method and Description |
---|---|
protected KNNModel<T> |
KNNModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Method and Description |
---|---|
protected TreeModel<T> |
TreeModel.copy(String newName,
ModelProvenance newProvenance) |
Constructor and Description |
---|
TreeModel(String name,
ModelProvenance description,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
boolean generatesProbabilities,
Map<String,List<String>> activeFeatures)
Constructs a trained decision tree model.
|
Modifier and Type | Method and Description |
---|---|
protected Model<T> |
XGBoostModel.copy(String newName,
ModelProvenance newProvenance) |
protected XGBoostExternalModel<T> |
XGBoostExternalModel.copy(String newName,
ModelProvenance newProvenance) |
protected XGBoostModel<T> |
XGBoostTrainer.createModel(String name,
ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
List<ml.dmlc.xgboost4j.java.Booster> models,
XGBoostOutputConverter<T> converter) |
Modifier and Type | Method and Description |
---|---|
protected Model<T> |
EnsembleModel.copy(String name,
ModelProvenance newProvenance) |
Constructor and Description |
---|
ExternalModel(String name,
ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
boolean generatesProbabilities,
Map<String,Integer> featureMapping) |
ExternalModel(String name,
ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
int[] featureForwardMapping,
int[] featureBackwardMapping,
boolean generatesProbabilities) |
Modifier and Type | Method and Description |
---|---|
protected Model<T> |
ONNXExternalModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Method and Description |
---|---|
protected TensorflowModel<T> |
TensorflowModel.copy(String newName,
ModelProvenance newProvenance) |
protected Model<T> |
TensorflowExternalModel.copy(String newName,
ModelProvenance newProvenance) |
protected TensorflowCheckpointModel<T> |
TensorflowCheckpointModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Method and Description |
---|---|
protected IndependentMultiLabelModel |
IndependentMultiLabelModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Class and Description |
---|---|
class |
EnsembleModelProvenance
Model provenance for ensemble models.
|
Modifier and Type | Method and Description |
---|---|
ModelProvenance |
EvaluationProvenance.getModelProvenance()
The model provenance.
|
Modifier and Type | Method and Description |
---|---|
com.oracle.labs.mlrg.olcut.provenance.ListProvenance<? extends ModelProvenance> |
EnsembleModelProvenance.getMemberProvenance() |
Constructor and Description |
---|
EvaluationProvenance(ModelProvenance modelProvenance,
DataProvenance datasetProvenance) |
Constructor and Description |
---|
EnsembleModelProvenance(String className,
OffsetDateTime time,
DatasetProvenance datasetProvenance,
TrainerProvenance trainerProvenance,
com.oracle.labs.mlrg.olcut.provenance.ListProvenance<? extends ModelProvenance> memberProvenance) |
EnsembleModelProvenance(String className,
OffsetDateTime time,
DatasetProvenance datasetProvenance,
TrainerProvenance trainerProvenance,
Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance,
com.oracle.labs.mlrg.olcut.provenance.ListProvenance<? extends ModelProvenance> memberProvenance) |
Modifier and Type | Method and Description |
---|---|
protected Model<Regressor> |
DummyRegressionModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Method and Description |
---|---|
protected abstract SkeletalIndependentRegressionModel |
SkeletalIndependentRegressionTrainer.createModel(Map<String,T> models,
ModelProvenance provenance,
ImmutableFeatureMap featureMap,
ImmutableOutputInfo<Regressor> outputInfo)
Constructs the appropriate subclass of
SkeletalIndependentRegressionModel for this trainer. |
protected abstract SkeletalIndependentRegressionSparseModel |
SkeletalIndependentRegressionSparseTrainer.createModel(Map<String,T> models,
ModelProvenance provenance,
ImmutableFeatureMap featureMap,
ImmutableOutputInfo<Regressor> outputInfo)
Constructs the appropriate subclass of
SkeletalIndependentRegressionModel for this trainer. |
Constructor and Description |
---|
SkeletalIndependentRegressionModel(String name,
String[] dimensions,
ModelProvenance modelProvenance,
ImmutableFeatureMap featureMap,
ImmutableOutputInfo<Regressor> outputInfo)
models.size() must equal labelInfo.getDomain().size()
|
SkeletalIndependentRegressionSparseModel(String name,
String[] dimensions,
ModelProvenance modelProvenance,
ImmutableFeatureMap featureMap,
ImmutableOutputInfo<Regressor> outputInfo,
Map<String,List<String>> activeFeatures)
models.size() must equal labelInfo.getDomain().size()
|
Modifier and Type | Method and Description |
---|---|
protected LibLinearRegressionModel |
LibLinearRegressionModel.copy(String newName,
ModelProvenance newProvenance) |
protected LibLinearModel<Regressor> |
LibLinearRegressionTrainer.createModel(ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<Regressor> outputIDInfo,
List<de.bwaldvogel.liblinear.Model> models) |
Modifier and Type | Method and Description |
---|---|
protected LibSVMRegressionModel |
LibSVMRegressionModel.copy(String newName,
ModelProvenance newProvenance) |
protected LibSVMModel<Regressor> |
LibSVMRegressionTrainer.createModel(ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<Regressor> outputIDInfo,
List<libsvm.svm_model> models) |
Modifier and Type | Method and Description |
---|---|
protected IndependentRegressionTreeModel |
IndependentRegressionTreeModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Method and Description |
---|---|
protected LinearSGDModel |
LinearSGDModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Method and Description |
---|---|
protected Model<Regressor> |
SparseLinearModel.copy(String newName,
ModelProvenance newProvenance) |
Modifier and Type | Method and Description |
---|---|
ModelProvenance |
SequenceModel.getProvenance() |
Constructor and Description |
---|
SequenceModel(String name,
ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDMap) |
Modifier and Type | Method and Description |
---|---|
protected TransformedModel<T> |
TransformedModel.copy(String name,
ModelProvenance newProvenance) |
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