Interpret

Changed
- Training speed improvements due to the use of SIMD on Intel processors.
Results may vary, but expect approx 2.75x faster for classification and 1.3x faster for RMSE regression
- Changed from using 64-bit floats to using 32-bit floats internally. Regression performed on datasets with large
targets that sum to greater than 3.4E+38 will overflow.
Fixed
- Fixed an issue with the monotonize function that would occur when monotonizing a feature with missing values
- Resolved issue where excluding the 1st feature would cause an exception

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

Added
- support for visualizations in streamlit
Fixed
- fixed dangling pointer issue in call to CalcInteractionStrength

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

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

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