This is the first Tribuo point release after the initial public announcement. It fixes many of the issues our early users have found, and improves the documentation in the areas flagged by those users. We also added a couple of small new methods as part of fixing the bugs, and added two new tutorials: one on columnar data loading and one on external model loading (i.e. XGBoost and ONNX models).
FileNotFoundExceptionrather than a mysterious
NullPointerExceptionwhen it can’t find the file.
JsonDataSource(consistent exceptions thrown, proper termination of reading in several cases).
LibSVMTrainerdidn’t track state between repeated calls to train.
LabelTransformerwhere it wouldn’t read pytorch outputs properly.
Mapsuitable for the
XGBoostModelto allow users to access a copy of the internal models.
This release fixes a few issues we found in the tutorials just before launch.
The IDXDataSource was added as an alternative way to load MNIST as the LibSVM website (which the tutorial was originally based on) was intermittently down during our pre-launch period.
This is the first public release of the Tribuo Java Machine Learning library. Tribuo provides classification, regression, clustering and anomaly detection algorithms along with data loading, transformation and model evaluation code. Tribuo also provides support for loading external ONNX models and scoring them in Java as well as support for training and evaluating deep learning models using TensorFlow.
Tribuo’s development started in 2016 led by Oracle Labs’ Machine Learning Research Group, and has been in production inside Oracle since 2017. It’s now available under an Apache 2.0 license, and we’ll continue to develop it in the open, including accepting community PRs under the Oracle Contributor Agreement.
The Tribuo source code repository contains release notes for all versions of Tribuo.