Version 0.6.7

v0.6.7 - 2024-11-27

Changed

• minimum python version increased to 3.9
• minimum numpy version increased to 1.25

Fixed

• removed scipy dependency to resolve Issue #588

Version 0.6.6

v0.6.6 - 2024-11-20

Changed

• added predict_with_uncertainty function by @degenfabian in PR #584
• handle mono-classification in SHAP by @degenfabian in PR #582
• improvements to tree building in C++

Fixed

• issue that develop/debug options were not being honored in Windows when 1<n_jobs in joblib
• fix several bugs in C++ from negative hessians or negative gain values caused by floating point noise

Version 0.6.5

v0.6.5 - 2024-10-23

Changed

• default EBM parameters changed to improve model performance
• switch to using exact versions of exp/log instead of the previously used approximate versions

Fixed

• fix issue where very large feature values fail in the UI PR #581 by @degenfabian

Version 0.6.4

v0.6.4 - 2024-09-28

Added

• support for regularization parameters reg_alpha, and reg_lambda in EBMs
• support for the parameter max_delta_step in EBMs
• improved fitting speed for most of the alternative objectives

Version 0.6.3

v0.6.3 - 2024-08-07

Added

• visualizations for the APRL (Automatic Piecewise Linear Regression) package by @mathias-von-ottenbreit

Changed

• early_stopping_tolerance default changed to 1e-5 to reduce EBMs fitting time slightly
• shuffle initial feature order within each bag and during greedy boosting

Fixed

• fixed numpy 2.0 issue in the Marginal class

Version 0.6.2

v0.6.2 - 2024-06-22

Added

• pass optional kwargs to DecisionTreeClassifier in PR #537 by @busFred
• support for multiclass purification
• support for higher dimensional purification
• allow higher levels of purification than would be supported via the tolerance parameter

Changed

• numpy 2.0 support for EBMs
• update documentation regarding monotonicity in PR #531 by @Krzys25
• moved purification utility from "interpret/glassbox/_ebm/_research" to "interpret.utils"

Fixed

• possible fix for issue #543 where merge_ebms was creating unexpected NaN values

Version 0.6.1

v0.6.1 - 2024-04-14

Fixed

• added compatibility with numpy 2.0 thanks to @DerWeh in PR #525
• fixed bug that was preventing SIMD from being used in python
• removed approximate division in SIMD since the approximation was too inaccurate

Changed

• EBM fitting time reduced

Version 0.6.0

v0.6.0 - 2024-03-16

Added

• Documentation on recommended hyperparameters to help users optimize their models.
• Support for monotone_constraints during model fitting, although post-processed monotonization is still suggested/preferred.
• The EBMModel class now includes _more_tags for better integration with the scikit-learn API, thanks to contributions from @DerWeh.

Changed

• Default max_rounds parameter increased from 5,000 to 25,000, for improved model accuracy.
• Numerous code simplifications, additional tests, and enhancements for scikit-learn compatibility, thanks to @DerWeh.
• The greedy boosting algorithm has been updated to support variable-length greedy sections, offering more flexibility during model training.
• Full compatibility with Python 3.12.
• Removal of the DecisionListClassifier from our documentation, as the skope-rules package seems to no longer be actively maintained.

Fixed

• The sweep function now properly returns self, correcting an oversight identified by @alvanli.
• Default exclude parameter set to None, aligning with scikit-learn's expected defaults, fixed by @DerWeh.
• A potential bug when converting features from categorical to continuous values has been addressed.
• Updated to handle the new return format for TreeShap in the SHAP 0.45.0 release.

Breaking Changes

• replaced the greediness __init__ parameter with greedy_ratio and cyclic_progress parameters for better control of the boosting process
  (see documentation for notes on greedy_ratio and cyclic_progress)
• replaced breakpoint_iteration_ with best_iteration_, which now contains the number of boosting steps rather than the number of boosting rounds

Version 0.5.1

v0.5.1 - 2024-02-08

Added

• Added new __init__ parameter: interaction_smoothing_rounds
• Added new __init__ parameter: min_hessian
• synthetic dataset generator (make_synthetic) for testing GAMs and for documentation

Changed

• default parameters have been modified to improve the accuracy of EBMs
• changed boosting internals to use LogitBoost to improve accuracy
• changed interaction detection to use hessians to improve interaction selection
• enabled smoothing_rounds by default to improve the smoothness of EBMs
• added the ability to specify interactions via feature names or negative indexing
• improved the speed of Morris sensitivity and partial dependence
• python 3.12 support for core EBMs. Some of our optional dependencies do not yet support python 3.12 though
• made early stopping more consistent and changed the early_stopping_tolerance to be a percentage

Fixed

• avoid displaying a scroll bar by default in jupyter notebook cells
• removed the dependency on deprecated distutils

Breaking Changes

• changed the internal representation for classifiers that have just 1 class

Version 0.5.0

v0.5.0 - 2023-12-13

Added

• added support for AVX-512 in PyPI installations to improve fitting speed
• introduced an option to disable SIMD optimizations through the debug_mode function in python
• exposed public utils.link_func and utils.inv_link functions

Changed

• the interpret-core package now installs the dependencies required to build and predict EBMs
  by default without needing to specify the [required] pip install flag
• experimental/private support for OVR multiclass EBMs
• added bagged_intercept_ attribute to store the intercepts for the bagged models

Fixed

• resolved an issue in merge_ebms where the merge would fail if all EBMs in the
  merge contained features with only one bin (issue #485)
• resolved multiple future warnings from other packages

Breaking Changes

• changed how monoclassification (degenerate classification with 1 class) is expressed
• replaced predict_and_contrib function with simpler eval_terms function that returns
  only the per-term contribution values. If you need both the contributions and predictions use:
  interpret.utils.inv_link(ebm.eval_terms(X).sum(axis=1) + ebm.intercept_, ebm.link_)
• separate to_json into to_jsonable (for python objects) and to_json (for files) functions
• create a new link function string for multiclass that is separate from binary classification
• for better scikit-learn compliance, removed the decision_function from the ExplainableBoostingRegressor