You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This commit was created on GitHub.com and signed with GitHub’s verified signature.
The key has expired.
Add _serialize_json_safe to utils and add unit tests
various memory optimizations in interpret-community done as part of integration with another product
1.) where possible use _local_importance_values since local_importance_values converts from numpy to list
2.) avoid typed_dataset in some paths since that creates a new pandas dataframe
3.) add clear_references parameter to DatasetWrapper such that it can be cleared after use, which should reduce memory usage - note this is only for users who know what they are doing
4.) added test for dense and wide data which can be used for future performance testing as well
Pickling and Unpickling MimicExplainer and Surrogate models
fix categorical handling for scikit-learn 0.24 during one hot encoding for failing test
Add unit test for error_handling.py
Use ndcg from metrics in test_validate_explanations.py
Consolidate MimicExplainer serialization tests
Add support for inverse soft logit for binary classification scenarios
Add replication metric computation in MimicExplainer
various memory optimizations to the explanation-related APIs, particularly explain_global, including:
1.) preventing matrix multiply if matrix is identity in engineered to raw mapping (which happens very often) - this significantly reduces memory usage
2.) differentiating between call from explain_global vs explain_local when entering _explain_local which allows us to skip some duplicate computations which lead to higher memory usage
3.) refactoring out into functions (eg _explain_local_helper) which allows GC of various temporary variables (I could have also used del but it looks much uglier)