- Added type hints to XGBDistribution model class - Hotfix to add error raising if sample weights are used (which is not yet implemented)
0.2.2
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- Hot fix to enable compatibility with xgboost v1.5.0 (enable_categorical kwarg)
0.2.1
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- 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
0.2.0
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- Performed experiments on various datasets to assess XGBDistribution performance - Added exponential distribution - Added Laplace distribution
0.1.2
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- Added poisson distribution - Added negative-binomial distribution - Changed naming conventions of distributions - Safety checks on distribution parameters
0.1.1
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- 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