apache/systemds
Oct 2024 – Oct 2024
1 Month active
Languages Used
DMLJava
Technical Skills
Algorithm ImplementationData ScienceExplainable AIMachine LearningSoftware Testing
louislepage
PROFILE
Louislepage
Laurent Lepage developed a built-in ShapExplainer function for the apache/systemds repository, enabling computation of Shapley values for feature attribution in machine learning models. He implemented supporting utilities for mask preparation, background data sampling, mask application, and final value computation, all designed to enhance model interpretability and support explainable AI workflows. The work was carried out using DML and Java, with a focus on algorithm implementation and data science principles. Laurent ensured robustness by adding comprehensive unit and component tests, resulting in a well-structured feature that integrates seamlessly into SystemDS and provides reliable, data-driven insights for end users.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,050
Activity Months1
Your Network
54 people
Work History
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for Apache SystemDS: Delivered a built-in ShapExplainer function to compute Shapley values for feature attribution in SystemDS. The release includes helper utilities for preparing masks, sampling background data, applying masks, and computing final Shapley values and the expected model output, along with unit and component tests to validate correctness. This work enhances model interpretability and enables data-driven feature attribution for end users. Commit: [SYSTEMDS-3669] Builtin for computation of shapley values (b651db516da31222018396bb996b3d825766c7da).
1 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for Apache SystemDS: Delivered a built-in ShapExplainer function to compute Shapley values for feature attribution in SystemDS. The release includes helper utilities for preparing masks, sampling background data, applying masks, and computing final Shapley values and the expected model output, along with unit and component tests to validate correctness. This work enhances model interpretability and enables data-driven feature attribution for end users. Commit: [SYSTEMDS-3669] Builtin for computation of shapley values (b651db516da31222018396bb996b3d825766c7da).
October 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
DMLJava
Technical Skills
Algorithm ImplementationData ScienceExplainable AIMachine LearningSoftware Testing
Repositories Contributed To
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