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Tribuo 4.1.0 API

Tribuo is a library for building and deploying Machine Learning models.

See: Description

Core Packages 
Package Description
org.tribuo
Provides the core interfaces and classes for using Tribuo.
org.tribuo.data
Provides classes for loading in data from disk, processing it into examples, and splitting datasets for things like cross-validation and train-test splits.
org.tribuo.data.columnar
Provides classes for processing columnar data and generating Examples.
org.tribuo.data.columnar.extractors
Provides implementations of FieldExtractor.
org.tribuo.data.columnar.processors.feature
Provides implementations of FeatureProcessor.
org.tribuo.data.columnar.processors.field
Provides implementations of FieldProcessor.
org.tribuo.data.columnar.processors.response
Provides implementations of ResponseProcessor.
org.tribuo.data.csv
Provides classes which can load columnar data (using a RowProcessor) from a CSV (or other character delimited format) file.
org.tribuo.data.sql
Provides classes which can load columnar data (using a RowProcessor) from a SQL source.
org.tribuo.data.text
Provides interfaces for converting text inputs into Features and Examples.
org.tribuo.data.text.impl
Provides implementations of text data processors.
org.tribuo.dataset
Provides utility datasets which subsample or otherwise transform the wrapped dataset.
org.tribuo.datasource
Simple data sources for ingesting or aggregating data.
org.tribuo.ensemble
Provides an interface for model prediction combinations, two base classes for ensemble models, a base class for ensemble excuses, and a Bagging implementation.
org.tribuo.evaluation
Evaluation base classes, along with code for train/test splits and cross validation.
org.tribuo.evaluation.metrics
This package contains the infrastructure classes for building evaluation metrics.
org.tribuo.hash
Provides the base interface and implementations of the Model hashing which obscures the feature names stored in a model.
org.tribuo.impl
Provides implementations of base classes and interfaces from org.tribuo.
org.tribuo.json
Provides interop with JSON formatted data, along with tools for interacting with JSON provenance objects.
org.tribuo.math
Contains the implementation of Tribuo's math library, it's gradient descent optimisers, kernels and a set of math related utils.
org.tribuo.math.kernel
Provides a Kernel interface for Mercer kernels, along with implementations of standard kernels.
org.tribuo.math.la
Provides a linear algebra system used for numerical operations in Tribuo.
org.tribuo.math.optimisers
Provides implementations of StochasticGradientOptimiser.
org.tribuo.math.optimisers.util
Provides some utility tensors for use in gradient optimisers.
org.tribuo.math.util
Provides math related util classes.
org.tribuo.provenance
Provides Tribuo specific infrastructure for the Provenance system which tracks models and datasets.
org.tribuo.provenance.impl
Provides internal implementations for empty provenance classes and TrainerProvenance.
org.tribuo.sequence
Provides core classes for working with sequences of Examples.
org.tribuo.transform
Provides infrastructure for applying transformations to a Dataset.
org.tribuo.transform.transformations
Provides implementations of standard transformations like binning, scaling, taking logs and exponents.
org.tribuo.util
Provides utilities which don't have other Tribuo dependencies.
Shared Backend Packages 
Package Description
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.sgd
Provides the base classes for models trained with stochastic gradient descent.
org.tribuo.common.tree
Provides common functionality for building decision trees, irrespective of the predicted Output.
org.tribuo.common.tree.impl
Provides internal implementation classes for building decision trees.
org.tribuo.common.xgboost
Provides abstract classes for interfacing with XGBoost abstracting away all the Output dependent parts.
Anomaly Detection Packages 
Package Description
org.tribuo.anomaly
Provides classes and infrastructure for anomaly detection problems.
org.tribuo.anomaly.evaluation
Evaluation classes for anomaly detection.
org.tribuo.anomaly.example
Provides a anomaly data generator used for testing implementations.
org.tribuo.anomaly.liblinear
Provides an interface to LibLinear-java for anomaly detection problems.
org.tribuo.anomaly.libsvm
Provides an interface to LibSVM for anomaly detection problems.
Multi-class Classification Packages 
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.dtree.impurity
Provides classification impurity metrics for 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
Provides infrastructure for Stochastic Gradient Descent for classification problems.
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.sgd.objectives
Provides classification loss functions for Stochastic Gradient Descent.
org.tribuo.classification.xgboost
Provides an interface to XGBoost for classification problems.
Clustering Packages 
Package Description
org.tribuo.clustering
Provides classes and infrastructure for working with clustering problems.
org.tribuo.clustering.evaluation
Evaluation classes for clustering.
org.tribuo.clustering.example
Provides a clustering data generator used for testing implementations.
org.tribuo.clustering.kmeans
Provides a multithreaded implementation of K-Means, with a configurable distance function.
Multi-label Classification Packages 
Package Description
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.
org.tribuo.multilabel.evaluation
Evaluation classes for multi-label classification using MultiLabel.
org.tribuo.multilabel.example
Provides a multi-label data generator for testing implementations.
org.tribuo.multilabel.sgd
Provides infrastructure for Stochastic Gradient Descent for multi-label classification problems.
org.tribuo.multilabel.sgd.linear
Provides an implementation of a multi-label classification linear model using Stochastic Gradient Descent.
org.tribuo.multilabel.sgd.objectives
Provides multi-label classification loss functions for Stochastic Gradient Descent.
Regression Packages 
Package Description
org.tribuo.regression
Provides classes and infrastructure for regression problems with single or multiple output dimensions.
org.tribuo.regression.baseline
Provides simple baseline regression predictors.
org.tribuo.regression.ensemble
Provides EnsembleCombiner implementations for working with multi-output regression problems.
org.tribuo.regression.evaluation
Evaluation classes for single or multi-dimensional regression.
org.tribuo.regression.example
Provides some example regression data generators for testing implementations.
org.tribuo.regression.impl
Provides skeletal implementations of Regressor Trainer that can wrap a single dimension trainer/model and produce one prediction per dimension independently.
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.rtree.impl
Provides internal implementation classes for the regression trees.
org.tribuo.regression.rtree.impurity
Provides implementations of regression tree impurity metrics.
org.tribuo.regression.sgd
Provides infrastructure for Stochastic Gradient Descent based regression models.
org.tribuo.regression.sgd.linear
Provides an implementation of linear regression using Stochastic Gradient Descent.
org.tribuo.regression.sgd.objectives
Provides regression loss functions for Stochastic Gradient Descent.
org.tribuo.regression.slm
Provides implementations of sparse linear regression using various forms of regularisation penalty.
org.tribuo.regression.xgboost
Provides an interface to XGBoost for regression problems.
External Interface Packages 
Package Description
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.onnx.extractors
Provides feature extraction implementations which use ONNX models.
org.tribuo.interop.tensorflow
Provides an interface to TensorFlow, allowing the training of non-sequential models using any supported Tribuo output type.
org.tribuo.interop.tensorflow.example
Example architectures for use with Tribuo's TF interface.
org.tribuo.interop.tensorflow.sequence
Provides an interface for working with TensorFlow sequence models, using Tribuo's SequenceModel abstraction.
Utility Packages 
Package Description
org.tribuo.tests
This package provides helper classes for Tribuo's unit tests.
org.tribuo.util.infotheory
This package provides static classes of information theoretic functions.
org.tribuo.util.infotheory.example
This package provides demos for the information theoretic function classes in org.tribuo.util.infotheory.
org.tribuo.util.infotheory.impl
This package provides the implementations and helper classes for the information theoretic functions in org.tribuo.util.infotheory.
org.tribuo.util.tokens
Core definitions for tokenization.
org.tribuo.util.tokens.impl
Simple fixed rule tokenizers.
org.tribuo.util.tokens.impl.wordpiece
Provides an implementation of a Wordpiece tokenizer which implements to the Tribuo Tokenizer API.
org.tribuo.util.tokens.options
OLCUT Options implementations which can construct Tokenizers of various types.
org.tribuo.util.tokens.universal
An implementation of a "universal" tokenizer which will split on word boundaries or character boundaries for languages where word boundaries are contextual.
Tribuo is a library for building and deploying Machine Learning models. It provides classification, regression, clustering, anomaly detection and multi-label classification algorithms. Tribuo is strongly typed, with most of the relevant types living in the org.tribuo package. Task specific types live in the root package of that task (e.g., org.tribuo.classification). Tribuo is modular, each package/module is scoped to have minimal dependencies, and there are few cross-cutting packages. Tribuo's development is lead by Oracle Labs' Machine Learning Research Group, the source is hosted on Github, and more documentation is available on the project website tribuo.org.
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