All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
Improvements in this release:
- Fix the bug in the Shap Dependence Plot #153
- Add HowTo guide for using grouped data #154
Improvements in this release:
- Fix p-value calculation in PSI #142
Improvements in this release:
- Fix catboost bug when calculating SHAP values #147
- Supply eval_sample_weight for fit in EarlyStoppingShapRFECV #144
- Remove codecov.io #145
- Remove sample_row from probatus #140
Improvements in this release:
- Enable use of sample_weight in ShapRFECV and EarlyStoppingShapRFECV #139
- Fix bug in EarlyStoppingShapRFECV #139
- Fix issue with categorical features in SHAP #138
- Missing values handled by AutoDist #126
- Fix issue with missing histogram in DependencePlot #137
Improvements in this release:
- Implemented EarlyStoppingShapRFECV #108
- Added support for Python 3.9 #132
Improvements in this release:
- Add error if model pipeline passed to SHAP #129
- Fixed PSI bug with empty bins #116
- Unit tests are run daily #113
- TreeBucketer has been refactored #124
- Fixes to failing test pipeline #120
- Improving language in docs #109, #107
Improvements in this release:
- Create a comparison of imputation strategies #86
- Added support for passing check_additivity argument #103
- Range of code styling issues fixed, based on precommit config #100
- Renamed TreeDependencePlotter to DependencePlotter and exposed the docs #94
- Enable instalation of extra dependencies #97
- Added how to notebook to ensure reproducibility #99
- Description of vision of probatus #91
Improvements in this release:
- Bugfix, allow passing kwargs to dependence plot in ShapModelInterpreter #90
Improvements in this release:
- Added ShapRFECV support for all sklearn compatible search CVs. #76 #49
Improvements in this release:
- Added features list to README #53
- Added docs for sample row functionality #54
- Added 'open in colab' badges to tutorial notebooks #56
- Deploy documentation on release #47
- Added columns_to_keep for shap feature elimination #63
- Updated docs for usage of columns to keep functionality in SHAPRFECV #66
- Added shap support for linear models #69
- Installed probatus in colab notebooks #80
- Minor infrastructure tweaks #81
Various improvements to the consistency and usability of the package
- Unit test docstring and notebooks #41
- Unified scoring metric within probatus #27
- Improve docstrings consistency documentation #25
- Implemented unified interface #24
- Added images to API docs documentation #23
- Added verbose parameter to ShapRFECV #21
- Make API more consistent #19
- Set model parameter name to clf across probatus
- Set default random_state to None
- Ensure that verbose is used consistently in probatus
- Unify parameter class_names for classes in which it is relevant
- Add return scores parameter to compute wherever applicable
- Add sample row functionality to utils #17
- Make an experiment comparing sklearn.RFECV with ShapRFECV #16
- ShapModelInterpreter calculate train set feature importance #13
- Improve SHAP RFECV API and documentation
- Fix issue with the distribution uploaded to pypi
- Add SHAP RFECV for features elimination
- Add SHAP Model Inspector with docs and tests
- Add resemblance model, with SHAP based importance
- Improve the docs for resemblance model
- Refactor stats tests, improve docs and expose functionality to users
- Improve Tree Bucketer, enable user to pass own tree object
- Improve docs for stats_tests
- Refactor stats_tests
- TreeBucketer, which bins the data based on the target distribution, using Decision Trees fitted on a single feature
- PSI calculation includes the p-values calculation
- metric_volatility and sample_similarity rebuilt
- New documentation
- Faster tests
- Improved and simplified API
- Scorer class added to the package
- Removed data from repository
- Hiding unfinished functionality from the user
- VolalityEstimation now has random_seed argument
- Improved unit testing
- Improved documentation README and CONTRIBUTING
- Added dependency on scipy 1.4+
- Readthedocs documentation website
- Added CHANGELOG.md
- Renamed to probatus
- Improved testing by adding pyflakes to CI
- probatus.metric_uncertainty.VolatilityEstimation is now deterministic, added random_state parameter
0.1.0 - 2019-09-21
Initial release, commit ecbd0d08a6eea370afda4a4790edeb4ee382995c