interpretml/interpret v0.2.0

Version 0.2.0

on GitHub

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.