v0.4.0

v0.4.0 - 2024-08-23

Full changelog

Features

• Paper updates:
  • Calibrated Explanations for Regression has been accepted to Machine Learning. It is currently in press.
• Code improvements:
  • Substantial speedup achieved through the newly implemented explain method! This method implements the core algorithm while minimizing the number of calls to core._predict, substantially speeding up the code without altering the algorithmic logic of calibrated_explanations. The explain method is used exclusively from this version on when calling explain_factual or explain_counterfactual.
    • Re-ran the ablation study for classification, looking at the impact of calibration set size, number of percentile samplings for numeric features and the number of features.
      • Uploaded a pdf version of the ablation study, making the results easier to overview.
    • Re-ran the evaluation for regression, measuring stability, robustness and running times with and without normalization.
  • Improved the safe_import to allow import ... from ... constructs.
  • Restructured package
    • Added a utils folder:
      • Moved discretizers.py to utils
      • Moved utils.py to utils and renamed to helper.py
    • Made explanations public
    • Made VennAbers and interval_regressor restricted
  • Experimental functionality introduced:
    • Several new experimental features have been introduced. These will be presented as Features once they are thoroughly tested and evaluated.
• Code interface improvements:
  • Added support for the MondrianCategorizer from crepes in the WrapCalibratedExplainer.
  • Added wrapper functions in WrapCalibratedExplainer redirecting to CalibratedExplainer:
    • Including predict, predict_proba, and set_difficulty_estimator.
    • Moved any remaining implementations of functions in WrapCalibratedExplainer to CalibratedExplainer.
  • Renamed the plot_all and plot_explanation functions to plot. Updated all usages of the plot function.
  • Added __len__ and __getitem__ to CalibratedExplanations.
    • __getitem__ allow indexing with int, slice, and lists (both boolean and integer lists). When more than one explanation is retrieved, a new CalibratedExplanations is returned, otherwise, the indexed CalibratedExplanation is returned.
• Documentation improvements:
  • Restructured and extended the documentation.
    • Updated the information at the entry page
    • Added an API reference
• Improvements of the CI setup:
  • Updated CI to run pytest before pylint.
  • Updated CI to avoid running tests when commit message starts with 'info:' or 'docs:'.
• Testing improvements
  • Improved tests to test predict and predict_proba functions in CalibratedExplainer better.
  • Added several other tests to increase coverage.

Fixes

• Fixed minor errors in the predict and predict_proba functions in CalibratedExplainer.
• Several other minor bug fixes have also been made.