Changelog

Development version

Version 0.x.x, 2023-xx-xx

  • …

Current version

Version 0.2.8, 2023-09-24

  • Fixes to make package work with xgboost 2.0

  • Various internal changes for xgboost sklearn API consistency

Version 0.2.7, 2023-03-12

  • Fix overflow issues for normal distribution, :issue:`64`

  • Removed verbosity hack in model training

  • Better support for pickle/joblib, :issue:`82`

Older versions

Version 0.2.6, 2023-01-21

Version 0.2.5, 2022-11-01

  • Added example script for hyperparameter tuning

  • Python requires >= 3.8 & xgboost >= 1.7.0 compatibility

Version 0.2.4, 2022-04-23

  • Added more precise loss description, negative log likelihood vs error

  • Various updates to conform with xgboost==1.6.0 release

Version 0.2.3, 2021-12-28

  • Added type hints to XGBDistribution model class

  • Hotfix to add error raising if sample weights are used (which is not yet implemented)

Version 0.2.2, 2021-10-23

  • Hot fix to enable compatibility with xgboost v1.5.0 (enable_categorical kwarg)

Version 0.2.1, 2021-10-10

  • Fixed the objective parameter in trained model to be reflective of distribution

  • Support for model saving and loading with pickle (please don’t use pickle)

  • Added count data example with distribution heatmap, :issue:`45`

  • Updated docs to include estimators parameter, :issue:`43`

  • Implemented cleaner model saving, tests against binary and json formats

Version 0.2.0, 2021-08-14

  • Performed experiments on various datasets to assess XGBDistribution performance

  • Added exponential distribution

  • Added Laplace distribution

Version 0.1.2, 2021-07-10

  • Added poisson distribution

  • Added negative-binomial distribution

  • Changed naming conventions of distributions

  • Safety checks on distribution parameters

Version 0.1.1, 2021-07-01

  • Added lognormal distribution

  • Cleanup of distribution code, tested

  • Silenced warnings during fit and predict steps

  • Explicit link to RTD, showing available distributions

  • CI tests running in Python 3.6, 3.7, 3.8

Version 0.1.0, 2021-06-20

  • First release of xgboost-distribution package

  • Contains XGBDistribution estimator, an xgboost wrapper with natural gradients

  • Normal distribution implemented