Release Notes#

Version 0.1.7#

  • Introduced StandardComplementaryNeymanShapExplainer, a new explainer that utilizes standard Neyman allocation for SHAP value estimation. Existing ComplementaryNeymanShapExplainer has been renamed to LimitedComplementaryNeymanShapExplainer to reflect its limited sampling strategy.

  • Internal refactoring and bug fixes to improve code maintainability and performance.

Version 0.1.6#

  • Includes fixes for complementary-based explainers to ensure correct SHAP value computations.

  • Improved documentation and added more comprehensive tests for all modules.

  • Small fixes for bugs discovered in previous versions during production use.

  • Ensures minimal fraction is respected in HierarchicalExplainer when using importance sampling.

Version 0.1.5#

  • Added ComplementaryNeymanShapExplainer, a new explainer leveraging Neyman allocation for more efficient sampling.

  • Refactored base classes to improve code clarity, maintainability, and facilitate future extensions.

  • Fixed minor bugs in ComplementaryShapApproximation to ensure accurate SHAP value calculations.

  • Fixes bug in PreciseShapExplainer related to generator dead-lock.

  • Introduced new modes in HierarchicalExplainer for more flexible explanation strategies, including: - Multi-modal explanations. - Business-aware first-level splits (at the cost of performance). - Importance-aware approximation budgets (for each group) for better resource allocation.

Version 0.1.4#

  • Added ComplementaryShapApproximation, an explainer using complementary contribution sampling for faster SHAP value estimation.

  • Enhanced global tests to cover new functionalities and improve robustness.

Version 0.1.3#

  • Added HierarchicalExplainer, an explainer using hierarchical sampling to accelerate SHAP value estimation for long text inputs (text modality only).

  • Refactored base classes to improve code organization and maintainability.

Version 0.1.2#

  • Added Monte Carlo-inspired explainers: - LimitedMcShapExplainer: uses limited sampling strategies to efficiently estimate SHAP values. - LimitedMcShapExplainer (restricted to unique masks): improves sampling efficiency by avoiding duplicates.

  • Fixed issues in the connectors module to enhance stability and performance.

Version 0.1.1#

  • Initial release of MLLM-SHAP, a library for SHAP value estimation in Multi-Modal Large Language Models (MLLMs).

  • Added PreciseShapExplainer for exact SHAP value computation.

  • Introduced the connectors module with support for the Liquid Audio LMF-2 multi-modal model.