v4.3 (Latest)v4.2 v4.1 v4.0

Tribuo v4.3.1 Release Notes

Small patch release to bump some dependencies and pull in minor fixes. The most notable fix allows CART trees to generate pure nodes, which previously they had been prevented from doing. This will likely improve the classification tree performance both for single trees and when used in an ensemble like RandomForests.

  • FeatureHasher should have an option to not hash the values, and TokenPipeline should default to not hashing the values (#309).
  • Improving the documentation for loading multi-label data with CSVLoader (#306).
  • Allows Example.densify to add arbitrary features (#304).
  • Adds accessors to ClassifierChainModel and IndependentMultiLabelModel so the individual models can be accessed (#302).
  • Allows CART trees to create pure leaves (#303).
  • Bumping jackson-core to 2.14.1, jackson-databind to 2.14.1, OpenCSV to 5.7.1 (pulling in the fixed commons-text 1.10.0).

Contributors

Tribuo v4.3 Release Notes

Tribuo v4.3 adds feature selection for classification problems, support for guided generation of model cards, and protobuf serialization for all serializable classes. In addition there is a new interface for distance based computations which can now use a kd-tree or brute force comparisons, the sparse linear model package has been rewritten to use Tribuo’s linear algebra system improving the speed and reducing memory consumption, and we’ve added some more tutorials.

Note this is likely the last feature release of Tribuo to support Java 8. The next major version of Tribuo will require Java 17. In addition, support for using java.io.Serializable for serialization will be removed in the next major release, and Tribuo will exclusively use protobuf based serialization.

Feature Selection

In this release we’ve added support for feature selection algorithms to the dataset and provenance systems, along with implementations of 4 information theoretic feature selection algorithms for use in classification problems. The algorithms (MIM, CMIM, mRMR and JMI) are described in this review paper. Continuous inputs are discretised into a fixed number of equal width bins before the mutual information is computed. These algorithms are a useful feature selection baseline, and we welcome contributions to extend the set of supported algorithms.

  • Feature selection algorithms #254.

Model Card Support

Model Cards are a popular way of describing a model, its training data, expected applications and any use cases that should be avoided. In this release we’ve added guided generation of model cards, where many fields are automatically generated from the provenance information inside each Tribuo model. Fields which require user input (such as the expected use cases for a model, or its license) can be added via a CLI program, and the resulting model card can be saved in json format.

At the moment, the automatic data extraction fails on some kinds of nested ensemble models which are generated without using a Tribuo Trainer class, in the future we’ll look at improving the data extraction for this case.

Protobuf Serialization

In this release we’ve added protocol buffer definitions for serializing all of Tribuo’s serializable types, along with the necessary code to interact with those definitions. This effort has improved the validation of serialized data, and will allow Tribuo models to be upwards compatible across major versions of Tribuo. Any serialized model or dataset from Tribuo v4.2 or earlier can be loaded in and saved out into the new format which will ensure compatibility with the next major version of Tribuo.

  • Protobuf support for core types (#226, #255, #262, #264).
  • Protobuf support for models (Multinomial Naive Bayes #267, Sparse linear models #269, XGBoost #270, OCI, ONNX and TF #271, LibSVM #272, LibLinear #273, SGD #275, Clustering models #276, Baseline models and ensembles #277, Trees #278).
  • Docs and supporting programs (#279).

Smaller improvements

We added an interface for querying the nearest neighbours of a vector, and updated HDBSCAN, K-Means and K-NN to use the new interface. The old implementation has been renamed the “brute force” search operator, and a new implementation which uses a kd-tree has been added.

We migrated off Apache Commons Math, which necessitated adding several methods to Tribuo’s math library. In the process we refactored the sparse linear model code, removing redundant matrix operations and greatly improving the speed of LASSO.

  • Refactor sparse linear models and remove Apache Commons Math (#241).

The ONNX export support has been refactored to allow the use of different ONNX opsets, and custom ONNX operations. This allows users of Tribuo’s ONNX export support to supply their own operations, and increases the flexibility of the ONNX support on the JVM.

  • ONNX operator refactor (#245).

ONNX Runtime has been upgraded to v1.12.1, which includes Linux ARM64 and macOS ARM64 binaries. As a result we’ve removed the ONNX tests from the arm Maven profile, and so those tests will execute on Linux & macOS ARM64 platforms.

  • ONNX Runtime upgrade (#256).

Small improvements

  • Improved the assignment to the noise cluster in HDBSCAN (#222).
  • Upgrade liblinear-java to v2.44 (#228).
  • Added accessors for the HDBSCAN cluster exemplars (#229).
  • Improve validation of salts when hashing feature names (#237).
  • Added accessors to TransformedModel for the wrapped model (#244).
  • Added a regex text preprocessor (#247).
  • Upgrade OpenCSV to v5.6 (#259).
  • Added a builder to RowProcessor to make it less confusing (#263).
  • Upgrade TF-Java to v0.4.2 (#281).
  • Upgrade OCI Java SDK to v2.46.0, protobuf-java to 3.19.6, XGBoost to 1.6.2, jackson to 2.14.0-rc1 (#288).

Bug Fixes

  • Fix for HDBSCAN small cluster generation (#236).
  • XGBoost provenance capture (#239.

Contributors

All Release Notes

The Tribuo source code repository contains release notes for all versions of Tribuo.