release v0.17.2

• Adds missing 'intercept' field on local explanation 'mli' data. Resolves issues: microsoft/responsible-ai-toolbox#367 #260
• Fix sphinx doc warnings
• Update readme with latest package description
• Fix model type parameter being called and defined incorrectly in mimic explainer. Resolves issue: #390
• Removed LIME warning, made it an exception inside the explainer if package is missing

release v0.17.1

patch release 0.17.1 of interpret-community SDK

• fix sphinx documentation issue with constants params
• remove private methods as these are not being called from anywhere in the interpret-community SDK:
  • _transform_data
  • _unsort_2d

release v0.17.0

• upgrade setuptools to latest to fix pypi upload error
• add setuptools upgrade to env setup script prior to pip install command
• update to interpret-core 0.2.4
• Support y_pred as pandas DataFrame for surrogate model predictions
• add deprecation message for old dashboard, add new raiwidgets dashboard to notebooks
• fix load explanation methods for numpy based variables

release v0.16.0

• 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)

release v0.15.4

• Fix number of features for raw explanations when using LinearExplainableModel
• Fix explanation dashboard json serialization issue

release v0.15.3

• fix issue with serializing the lightgbm surrogate model on mimic explainer for lightgbm >= 3.0.0

release v0.15.2

• add eval_data to get_raw_explanation
• remove use_cdn from explanation dashboard
• fix credentialed VM response data when using axios library
• reuse version in sphinx build

release v0.15.1

• fix tolist error for sparse feature importances and sparse raw_to_output_map on creating raw explanation
• move LIME from azureml-contrib-interpret package to interpret-community
• fix error during visualization on explanation with id for eval_y_predicted and eval_y_predicted_proba in datasets mixin
• add true_y parameter to ExplanationDashboard notebooks to demonstrate additional functionality

release v0.15.0

• remove visualization directory as it is being moved to a common respository
• update tests to support lightgbm 3.0 update
• optimize data mapper memory usage
• update CDN path for widget
• add mimic explainer test for high-dimensional dense raw features explanation
• fix lightgbm 3.0 support by densifying input matrix to reenable tests
• fix issues with importing version in setup.py before dependencies installed

release v0.14.4

• fix open in new tab on public VMs (specifically tested on Azure DSVM)
• handle case where classifier has predict function that returns probabilities in model wrapper
• added tests for shap_values_output for tree explainer and mimic explainer, fixed issue with XGBoost and TreeExplainer case