Interpret Versions Save

[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

[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)
    

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.

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.

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.

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.

v0.2.3 - 2021-01-13

Major upgrades to EBM in this release. Automatic interaction detection is now included by default. This will increase accuracy substantially in most cases. Numerous optimizations to support this, especially around binary classification. Expect similar or slightly slower training times due to interactions.

Fixed

• Automated interaction detection uses low-resolution binning for both FAST and pairwise training.

Changed

• EBM argument has been reduced from outer_bags=16 to outer_bags=8.
• EBM now includes interactions by default from interactions=0 to interactions=10.
• Algorithm treeinterpreter is now unstable due to upstream dependencies.
• Automated interaction detection now operates from two-pass to one-pass.
• Numeric approximations used in boosting (i.e. approx log / exp).
• Some arguments have been re-ordered for EBM initialization.

v0.2.2 - 2020-10-19

Fixed

• Fixed bug on predicting unknown categories with EBM.
• Fixed bug on max value being placed in its own bin for EBM pre-processing.
• Numerous native fixes and optimizations.

Added

• Added max_interaction_bins as argument to EBM learners for different sized bins on interactions, separate to mains.
• New binning method 'quantile_humanized' for EBM.

Changed

• Interactions in EBM now use their own pre-processing, separate to mains.
• Python 3.5 no longer supported.
• Switched from Python to native code for binning.
• Switched from Python to native code for PRNG in EBM.

v0.2.1 - 2020-08-07

Added

• Python 3.8 support.

Changed

• Dash based visualizations will always default to listen port 7001 on first attempt; if the first attempt fails it will try a random port between 7002-7999.

Experimental (WIP)

• Further cloud environment support.
• Improvements for multiclass EBM global graphs.

v0.2.0 - 2020-07-21

Breaking Changes

• With warning, EBM classifier adapts internal validation size when there are too few instances relative to number of unique classes. This ensures that there is at least one instance of each class in the validation set.
• Cloud Jupyter environments now use a CDN to fix major rendering bugs and performance.
  • CDN currently used is https://unpkg.com
  • If you want to specify your own CDN, add the following as the top cell

    from interpret import set_visualize_provider
    from interpret.provider import InlineProvider
    from interpret.version import __version__
    
    # Change this to your custom CDN.
    JS_URL = "https://unpkg.com/@interpretml/interpret-inline@{}/dist/interpret-inline.js".format(__version__)
    set_visualize_provider(InlineProvider(js_url=JS_URL))
    

• EBM has changed initialization parameters:

  • schema -> DROPPED
    n_estimators -> outer_bags
    holdout_size -> validation_size
    scoring -> DROPPED
    holdout_split -> DROPPED
    main_attr -> mains
    data_n_episodes -> max_rounds
    early_stopping_run_length -> early_stopping_rounds
    feature_step_n_inner_bags -> inner_bags
    training_step_epsiodes -> DROPPED
    max_tree_splits -> max_leaves
    min_cases_for_splits -> DROPPED
    min_samples_leaf -> ADDED (Minimum number of samples that are in a leaf)
    binning_strategy -> binning
    max_n_bins -> max_bins
    

• EBM has changed public attributes:

  • n_estimators -> outer_bags
    holdout_size -> validation_size
    scoring -> DROPPED
    holdout_split -> DROPPED
    main_attr -> mains
    data_n_episodes -> max_rounds
    early_stopping_run_length -> early_stopping_rounds
    feature_step_n_inner_bags -> inner_bags
    training_step_epsiodes -> DROPPED
    max_tree_splits -> max_leaves
    min_cases_for_splits -> DROPPED
    min_samples_leaf -> ADDED (Minimum number of samples that are in a leaf)
    binning_strategy -> binning
    max_n_bins -> max_bins
    
    attribute_sets_ -> feature_groups_
    attribute_set_models_ -> additive_terms_ (Pairs are now transposed)
    model_errors_ -> term_standard_deviations_
    
    main_episode_idxs_ -> breakpoint_iteration_[0]
    inter_episode_idxs_ -> breakpoint_iteration_[1]
    
    mean_abs_scores_ -> feature_importances_
    

Fixed

• Internal fixes and refactor for native code.
• Updated dependencies for JavaScript layer.
• Fixed rendering bugs and performance issues around cloud Jupyter notebooks.
• Logging flushing bug fixed.
• Labels that are shaped as nx1 matrices now automatically transform to vectors for training.

Experimental (WIP)

• Added support for AzureML notebook VM.
• Added local explanation visualizations for multiclass EBM.