Tribuo - A Java prediction library (v4.3)

Tribuo is a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification. Tribuo provides implementations of popular ML algorithms and also wraps other libraries to provide a unified interface. Tribuo contains all the code necessary to load, featurise and transform data. Additionally, it includes the evaluation classes for all supported prediction types. Development is led by Oracle Labs' Machine Learning Research Group; we welcome community contributions.

All trainers are configurable using the OLCUT configuration system. This allows a user to define a trainer in an xml or json file and repeatably build models. Example configurations for each of the supplied Trainers can be found in the config folder of each package. These configuration files can also be written in json or edn by using the appropriate OLCUT configuration dependency. Models and datasets are serializable using Java serialization.

All models and evaluations include a serializable provenance object which records the creation time of the model or evaluation, the identity of the data and any transformations applied to it, as well as the hyperparameters of the trainer. In the case of evaluations, this provenance information also includes the specific model used. Provenance information can be extracted as JSON, or serialised directly using Java serialisation. For production deployments, provenance information can be redacted and replaced with a hash to provide model tracking through an external system. Many Tribuo models can be exported in ONNX format for deployment in other languages, platforms or cloud services.

Tribuo runs on Java 8+, and we test on LTS versions of Java along with the latest release. Tribuo itself is a pure Java library and is supported on all Java platforms; however, some of our interfaces require native code and are thus supported only where there is native library support. We test on x86_64 architectures on Windows 10, macOS and Linux (RHEL/OL/CentOS 7+), as these are supported platforms for the native libraries with which we interface. If you're interested in another platform and wish to use one of the native library interfaces (ONNX Runtime, TensorFlow, and XGBoost), we recommend reaching out to the developers of those libraries. Note the model card and reproducibility packages require Java 17, and as such are not part of the tribuo-all Maven Central deployment.

Documentation

• Library Architecture
• Package Overview
• Javadoc 4.3, 4.2, 4.1, 4.0
• Helper Programs
• Developer Documentation
• Roadmap
• Frequently Asked Questions

Tutorials

Tutorial notebooks, including examples of Classification, Clustering, Regression, Anomaly Detection, TensorFlow, document classification, columnar data loading, working with externally trained models, and the configuration system, can be found in the tutorials. These use the IJava Jupyter notebook kernel, and work with Java 10+, except the model card & reproducibility tutorials which require Java 17. To convert the tutorials' code back to Java 8, in most cases simply replace the var keyword with the appropriate types.

Algorithms

General predictors

Tribuo includes implementations of several algorithms suitable for a wide range of prediction tasks:

Algorithm	Implementation	Notes
Bagging	Tribuo	Can use any Tribuo trainer as the base learner
Random Forest	Tribuo	For both classification and regression
Extra Trees	Tribuo	For both classification and regression
K-NN	Tribuo	Includes options for several parallel backends, as well as a single threaded backend
Neural Networks	TensorFlow	Train a neural network in TensorFlow via the Tribuo wrapper. Models can be deployed using the ONNX interface or the TF interface

The ensembles and K-NN use a combination function to produce their output. These combiners are prediction task specific, but the ensemble & K-NN implementations are task agnostic. We provide voting and averaging combiners for multi-class classification, multi-label classification and regression tasks.

Classification

Tribuo has implementations or interfaces for:

Algorithm	Implementation	Notes
Linear models	Tribuo	Uses SGD and allows any gradient optimizer
Factorization Machines	Tribuo	Uses SGD and allows any gradient optimizer
CART	Tribuo
SVM-SGD	Tribuo	An implementation of the Pegasos algorithm
Adaboost.SAMME	Tribuo	Can use any Tribuo classification trainer as the base learner
Multinomial Naive Bayes	Tribuo
Regularised Linear Models	LibLinear
SVM	LibSVM or LibLinear	LibLinear only supports linear SVMs
Gradient Boosted Decision Trees	XGBoost

Tribuo also supplies a linear chain CRF for sequence classification tasks. This CRF is trained via SGD using any of Tribuo's gradient optimizers.

Tribuo has a set of information theoretic feature selection algorithms which can be applied to classification tasks. Feature inputs are automatically discretised into equal width bins. At the moment this includes implementations of mutual information maximisation (MIM), Conditional Mutual Information Maximisation (CMIM), minimum Redundancy Maximum Relevancy (mRMR) and Joint Mutual Information (JMI).

To explain classifier predictions there is an implementation of the LIME algorithm. Tribuo's implementation allows the mixing of text and tabular data, along with the use of any sparse model as an explainer (e.g., regression trees, lasso etc), however it does not support images.

Regression

Tribuo's regression algorithms are multidimensional by default. Single dimensional implementations are wrapped in order to produce multidimensional output.

Algorithm	Implementation	Notes
Linear models	Tribuo	Uses SGD and allows any gradient optimizer
Factorization Machines	Tribuo	Uses SGD and allows any gradient optimizer
CART	Tribuo
Lasso	Tribuo	Using the LARS algorithm
Elastic Net	Tribuo	Using the co-ordinate descent algorithm
Regularised Linear Models	LibLinear
SVM	LibSVM or LibLinear	LibLinear only supports linear SVMs
Gradient Boosted Decision Trees	XGBoost

Clustering

Tribuo includes infrastructure for clustering and also supplies two clustering algorithm implementations. We expect to implement additional algorithms over time.

Algorithm	Implementation	Notes
HDBSCAN*	Tribuo	A density-based algorithm which discovers clusters and outliers
K-Means	Tribuo	Includes both sequential and parallel backends, and the K-Means++ initialisation algorithm

Anomaly Detection

Tribuo offers infrastructure for anomaly detection tasks. We expect to add new implementations over time.

Algorithm	Implementation	Notes
One-class SVM	LibSVM
One-class linear SVM	LibLinear

Multi-label classification

Tribuo offers infrastructure for multi-label classification, along with a wrapper which converts any of Tribuo's multi-class classification algorithms into a multi-label classification algorithm. We expect to add more multi-label specific implementations over time.

Algorithm	Implementation	Notes
Independent wrapper	Tribuo	Converts a multi-class classification algorithm into a multi-label one by producing a separate classifier for each label
Classifier Chains	Tribuo	Provides classifier chains and randomized classifier chain ensembles using any of Tribuo's multi-class classification algorithms
Linear models	Tribuo	Uses SGD and allows any gradient optimizer
Factorization Machines	Tribuo	Uses SGD and allows any gradient optimizer

Interfaces

In addition to our own implementations of Machine Learning algorithms, Tribuo also provides a common interface to popular ML tools on the JVM. If you're interested in contributing a new interface, open a GitHub Issue, and we can discuss how it would fit into Tribuo.

Currently we have interfaces to:

• LibLinear - via the LibLinear-java port of the original LibLinear (v2.44).
• LibSVM - using the pure Java transformed version of the C++ implementation (v3.25).
• ONNX Runtime - via the Java API contributed by our group (v1.12.1).
• TensorFlow - Using TensorFlow Java v0.4.2 (based on TensorFlow v2.7.4). This allows the training and deployment of TensorFlow models entirely in Java.
• XGBoost - via the built in XGBoost4J API (v1.6.2).

Binaries

Binaries are available on Maven Central, using groupId org.tribuo. To pull all the Java 8 compatible components of Tribuo, including the bindings for TensorFlow, ONNX Runtime and XGBoost (which are native libraries), use:

Maven:

<dependency>
    <groupId>org.tribuo</groupId>
    <artifactId>tribuo-all</artifactId>
    <version>4.3.1</version>
    <type>pom</type>
</dependency>

or from Gradle:

implementation ("org.tribuo:tribuo-all:4.3.1@pom") {
    transitive = true // for build.gradle (i.e., Groovy)
    // isTransitive = true // for build.gradle.kts (i.e., Kotlin)
}

The tribuo-all dependency is a pom which depends on all the Tribuo subprojects except for the model card and reproducibility projects which require Java 17.

Most of Tribuo is pure Java and thus cross-platform, however some of the interfaces link to libraries which use native code. Those interfaces (TensorFlow, ONNX Runtime and XGBoost) only run on supported platforms for the respective published binaries, and Tribuo has no control over which binaries are supplied. If you need support for a specific platform, reach out to the maintainers of those projects. As of the 4.1 release these native packages all provide x86_64 binaries for Windows, macOS and Linux. It is also possible to compile each package for macOS ARM64 (i.e., Apple Silicon), though there are no binaries available on Maven Central for that platform for TensorFlow or XGBoost. As of the 4.3 release Tribuo now depends on a version of ONNX Runtime which includes support for macOS ARM64 and Linux aarch64 platforms. When developing on an ARM platform you can select the arm profile in Tribuo's pom.xml to disable the native library tests.

Individual jars are published for each Tribuo module. It is preferable to depend only on the modules necessary for the specific project. This prevents your code from unnecessarily pulling in large dependencies like TensorFlow.

Compiling from source

Tribuo uses Apache Maven v3.5 or higher to build. Tribuo is compatible with Java 8+, and we test on LTS versions of Java along with the latest release. To build, simply run mvn clean package. All Tribuo's dependencies should be available on Maven Central. Please file an issue for build-related issues if you're having trouble (though do check if you're missing proxy settings for Maven first, as that's a common cause of build failures, and out of our control). Note if you're building using Java 16 or earlier the model card and reproducibility packages will be disabled.

Repository Layout

Development happens on the main branch, which has the version number of the next Tribuo release with "-SNAPSHOT" appended to it. Tribuo major and minor releases will be tagged on the main branch, and then have a branch named vA.B.X-release-branch (for release vA.B.0) branched from the tagged release commit for any point releases (i.e., vA.B.1, vA.B.2 etc) following from that major/minor release. Those point releases are tagged on the specific release branch e.g., v4.0.2 is tagged on the v4.0.X-release-branch.

Contributing

We welcome contributions! See our contribution guidelines.

We have a discussion mailing list tribuo-devel@oss.oracle.com, archived here. We're investigating different options for real time chat, check back in the future. For bug reports, feature requests or other issues, please file a Github Issue.

Security

Please consult the security guide for our responsible security vulnerability disclosure process.

License

Tribuo is licensed under the Apache 2.0 License.

Release Notes:

• v4.3.1 - Small bug fixes, notably to CART trees and Example.densify, bumps dependencies to more secure versions.
• v4.3.0 - Model card support, feature selection for classification, protobuf serialization format, kd-tree for distance computations, speed improvements for sparse linear models. Version bumps for most dependencies, and various other small fixes and improvements.
• v4.2.2 - Small bug fixes, bump TF-Java to 0.4.2, jackson to 2.13.4, protobuf-java to 3.19.6, OpenCSV to 5.7.1.
• v4.2.1 - Bug fixes for KMeans' multithreading, nondeterministic iteration orders affecting ONNX export and K-Means initialization, and upgraded TF-Java to 0.4.1.
• v4.2.0 - Added factorization machines, classifier chains, HDBSCAN. Added ONNX export and OCI Data Science integration. Added reproducibility framework. Various other small fixes and improvements, including the regression fixes from v4.1.1. Filled out the remaining javadoc, added 4 new tutorials (onnx export, multi-label classification, reproducibility, hdbscan), expanded existing tutorials.
• v4.1.1 - Bug fixes for multi-output regression, multi-label evaluation, KMeans & KNN with SecurityManager, and update TF-Java 0.4.0.
• v4.1.0 - Added TensorFlow training support, a BERT feature extractor, ExtraTrees, K-Means++, many linear model & CRF performance improvements, new tutorials on TF and document classification. Many bug fixes & documentation improvements.
• v4.0.2 - Many bug fixes (CSVDataSource, JsonDataSource, RowProcessor, LibSVMTrainer, Evaluations, Regressor serialization). Improved javadoc and documentation. Added two new tutorials (columnar data and external models).
• v4.0.1 - Bugfix for CSVReader to cope with blank lines, added IDXDataSource to allow loading of native MNIST format data.
• v4.0.0 - Initial public release.
• v3 - Added provenance system, the external model support and onnx integrations.
• v2 - Expanded beyond a classification system, to support regression, clustering and multi-label classification.
• v1 - Initial internal release. This release only supported multi-class classification.