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Version 0.2.0
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@interpret-ml interpret-ml released this 21 Jul 20:49
v0.2.0
6ddc0cc

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.
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