Version 0.4.2

v0.4.2 - 2023-05-31

Added

• support for specifying outer bags

Changed

• exceptions raised in the joblib child processes will be re-raised in the main process rather than be expressed as a TerminatedWorkerError
• small additional improvements in memory compression
• small improvements in maximizing the benefit of the privacy budget for Differentially Private EBMs

Fixed

• fixed segfault that was occurring in the Anaconda build
• fixed a bug that would prevent Differentially Private EBMs from using the exclude parameter

Version 0.4.1

[v0.4.1] - 2023-05-16

Added

• support for visualizations in streamlit

Fixed

• fixed dangling pointer issue in call to CalcInteractionStrength

Version 0.4.0

[v0.4.0] - 2023-05-11

Added

• alternative objective functions: poisson_deviance, tweedie_deviance, gamma_deviance, pseudo_huber, rmse_log (log link)
• greediness __init__ parameter that allows selecting a behavior between cyclic boosting and greedy boosting
• smoothing_rounds __init__ parameter
• added type hints to the EBM __init__ parameters and class attributes
• init_score parameter to allow boosting and prediction on top of a previous model
• multiclass support in merge_ebms
• ability to monotonize features using post process model editing

Changed

• default BaseLinear regressor is changed from Lasso to LinearRegression class
• placed limits on the amount of memory used to find interactions with high cardinality categoricals

Fixed

• validation_size of 0 is now handled by disabling early_stopping and using the final model

Breaking Changes

• replaced the __init__ param "mains" with "exclude"
• removed the binning __init__ param as this functionality was already fully supported in feature_types
• removed the unused zero_val_count attribute and n_samples attribute
• renamed the noise_scale_ attribute to noise_scale_boosting_ and added noise_scale_binning_ to DPEBMs

Version 0.3.2

[v0.3.2] - 2023-03-14

Fixed

• fix the issue that the shared library would only work on newer linux versions

Version 0.3.1

[v0.3.1] - 2023-03-13

Added

• Mac m1 support in conda-forge
• SPOTGreedy prototype selection (PR #392)

Fixed

• fix visualization when both cloud and non-cloud environments are detected (PR #210)
• fix ShapTree bug where it was treating classifiers as regressors
• resolve scikit-learn warnings occurring when models were trained using Pandas DataFrames
• change the defaults to prefer 'continuous' over 'nominal' when a feature has 1 or 2 unique float64 values

Breaking Changes

• in the blackbox and greybox explainers, change from accepting a predict_fn to
  accepting either a model or a predict_fn
• feature type 'categorical' has been renamed to 'nominal' for the remaining
  feature_type parameters in the package (EBMs were already using 'nominal')
• removed the unused sampler parameters to the Explainer classes

Version 0.3.0

[v0.3.0] - 2022-11-16

Added

• Full Complexity EBMs with higher order interactions supported: GA3M, GA4M, GA5M, etc...
  3-way and higher-level interactions lose exact global interpretability, but retain exact local explanations
  Higher level interactions need to be explicitly specified. No automatic FAST detection yet
• Mac m1 support
• support for ordinals
• merge_ebms now supports merging models with interactions, including higher-level interactions
• added classic composition option during Differentially Private binning
• support for different kinds of feature importances (avg_weight, min_max)
• exposed interaction detection API (FAST algorithm)
• API to calculate and show the importances of groups of features and terms.

Changed

• memory efficiency: About 20x less memory is required during fitting
• predict time speed improvements. About 50x faster for Pandas CategoricalDType,
  and varying levels of improvements for other data types
• handling of the differential privacy DPOther bin, and non-DP unknowns has been unified by having a universal unknown bin
• bin weights have been changed from per-feature to per-term and are now multi-dimensional
• improved scikit-learn compliance: We now conform to the scikit-learn 1.0 feature names API by using
  self.feature_names_in_ for the X column names and self.n_features_in_.
  We use the matching self.feature_types_in_ for feature types, and self.term_names_ for the additive term names.

Fixed

• merge_ebms now distributes bin weights proportionally according to volume when splitting bins
• DP-EBMs now use sample weights instead of bin counts, which preserves privacy budget
• improved scikit-learn compliance: The following init attributes are no longer overwritten
  during calls to fit: self.interactions, self.feature_names, self.feature_types
• better handling of floating point overflows when calculating gain and validation metrics

Breaking Changes

• EBMUtils.merge_models function has been renamed to merge_ebms
• renamed binning type 'quantile_humanized' to 'rounded_quantile'
• feature type 'categorical' has been specialized into separate 'nominal' and 'ordinal' types
• EBM models have changed public attributes:

  • feature_groups_ -> term_features_
    global_selector -> n_samples_, unique_val_counts_, and zero_val_counts_
    domain_size_ -> min_target_, max_target_
    additive_terms_ -> term_scores_
    bagged_models_ -> BaseCoreEBM has been depricated and the only useful attribute has been moved 
                      into the main EBM class (bagged_models_.model_ -> bagged_scores_)
    feature_importances_ -> has been changed into the function term_importances(), which can now also 
                            generate different types of importances
    preprocessor_ & pair_preprocessor_ -> attributes have been moved into the main EBM model class (details below)
    

• EBMPreprocessor attributes have been moved to the main EBM model class

  • col_names_ -> feature_names_in_
    col_types_ -> feature_types_in_
    col_min_ -> feature_bounds_
    col_max_ -> feature_bounds_
    col_bin_edges_ -> bins_
    col_mapping_ -> bins_
    hist_counts_ -> histogram_counts_
    hist_edges_ -> histogram_edges_
    col_bin_counts_ -> bin_weights_ (and is now a per-term tensor)
    

Version 0.2.7

v0.2.7 - 2021-09-23

Added

• Synapse cloud support for visualizations.

Fixed

• All category names in bar charts now visible for inline rendering (used in cloud environments).
• Joblib preference was previously being overriden. This has been reverted to honor the user's preference.
• Bug in categorical binning for differentially privatized EBMs has been fixed.

Version 0.2.6

v0.2.6 - 2021-07-20

Adde6

• Differential-privacy augmented EBMs now available as interpret.privacy.{DPExplainableBoostingClassifier,DPExplainableBoostingRegressor}.
• Packages interpret and interpret-core now distributed via docker.

Changed

• Sampling code including stratification within EBM now performed in native code.

Fixed

• Computer provider with joblib can now support multiple engines with serialization support.
• Labels are now all shown for inline rendering of horizontal bar charts.
• JS dependencies updated.

Version 0.2.5

v0.2.5 - 2021-06-21

Added

• Sample weight support added for EBM.
• Joint predict_and_contrib added to EBM where both predictions and feature contributions are generated in one call.
• EBM predictions now substantially faster with categorical featured predictions.
• Preliminary documentation for all of interpret now public at https://interpret.ml/docs.
• Decision trees now work in cloud environments (InlineRenderer support).
• Packages interpret and interpret-core now distributed via sdist.

Fixed

• EBM uniform binning bug fixed where empty bins can raise exceptions.
• Users can no longer include duplicate interaction terms for EBM.
• CSS adjusted for inline rendering such that it does not interfere with its hosting environment.
• JS dependencies updated.

Experimental

• Ability to merge multiple EBM models into one. Found in interpret.glassbox.ebm.utils.

Version 0.2.4

v0.2.4 - 2021-01-19

Fixed

• Bug fix on global EBM plots.
• Rendering fix for AzureML notebooks.

Changed

• JavaScript dependencies for inline renderers updated.