Tribuo v4.0.2

Bugs fixed:
- Fixed a locale issue in the evaluation tests.
- Fixed issues with RowProcessor (expand regexes not being called,
  improper provenance capture).
- IDXDataSource now throws FileNotFoundException rather than a mysterious NPE when it can't find the file.
- Fixed issues in JsonDataSource (consistent exceptions thrown, proper
  termination of reading in several cases).
- Fixed an issue where regression models couldn't be serialized due to a
  non-serializable lambda.
- Fixed UTF-8 BOM issues in CSV loading.
- Fixed an issue where LibSVMTrainer didn't track state between repeated
  calls to train.
- Fixed issues in the evaluators to ensure consistent exception throwing
  when discovering unlabelled or unknown ground truth outputs.
- Fixed a bug in ONNX LabelTransformer where it wouldn't read
  pytorch outputs properly.
- Bumped to OLCUT 5.1.5 to fix a provenance -> configuration conversion
  issue.

New additions:
- Added a method which converts a Jackson ObjectNode into a Map suitable
  for the RowProcessor.
- Added missing serialization tests to all the models.
- Added a getInnerModels method to LibSVMModel, LibLinearModel and
  XGBoostModel to allow users to access a copy of the internal models.
- More documentation.
- Columnar data loading tutorial.
- External model (XGBoost & ONNX) tutorial.

Dependency updates:
- OLCUT 5.1.5 (brings in jline 3.16.0 and jackson 2.11.3).

Tribuo v4.0.1

- Fixes an issue where CSVReader would fail to read csv files with
  extraneous newlines at the end of the file.
- Adds an IDXDataSource which reads IDX (i.e. MNIST) formatted data.

Tribuo v4.0.0

- Initial public release

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.