Changelog

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.

[1.7.0] - 2021-03-16

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

[1.6.2] - 2021-03-10

Improvements in this release:

Bugfix, allow passing kwargs to dependence plot in ShapModelInterpreter #90

[1.6.1] - 2021-03-09

Improvements in this release:

Added ShapRFECV support for all sklearn compatible search CVs. #76 #49

[1.6.0] - 2021-03-01

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

[1.5.1] - 2020-12-04

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

[1.5.0] - 2020-11-18

Improve SHAP RFECV API and documentation

[1.4.4] - 2020-11-11

Fix issue with the distribution uploaded to pypi

[1.4.0] - 2020-11-10 (Broken)

Add SHAP RFECV for features elimination

[1.3.0] - 2020-11-05 (Broken)

Add SHAP Model Inspector with docs and tests

[1.2.0] - 2020-09-30

Add resemblance model, with SHAP based importance
Improve the docs for resemblance model
Refactor stats tests, improve docs and expose functionality to users

[1.1.1] - 2020-09-08

Improve Tree Bucketer, enable user to pass own tree object

[1.1.0] - 2020-08-24

Improve docs for stats_tests
Refactor stats_tests

[1.0.1] - 2020-08-07

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

[1.0.0] - 2020-02-24

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

[0.1.3] - 2020-02-24

Added

VolalityEstimation now has random_seed argument

Changed

Improved unit testing
Improved documentation README and CONTRIBUTING

Fixed

Added dependency on scipy 1.4+

[0.1.2] - 2019-10-29

Added

Readthedocs documentation website

[0.1.1] - 2019-10-09

Added

Added CHANGELOG.md

Changed

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