apache/systemds
Oct 2024 – Oct 2024
1 Month active
Languages Used
DMLJava
Technical Skills
Algorithm ImplementationData ScienceExplainable AIMachine LearningSoftware Testing
louislepage
PROFILE
Louislepage
Developed and integrated a built-in ShapExplainer function for the apache/systemds repository, enabling computation of Shapley values for feature attribution in machine learning models. The implementation included algorithmic components for preparing feature masks, sampling background data, applying masks, and calculating both Shapley values and expected model outputs. Comprehensive unit and component tests were added to ensure correctness and regression safety. This work, delivered in DML and Java, enhances model interpretability by providing explainable AI capabilities within SystemDS. The feature supports data-driven analysis of model predictions, allowing users to better understand feature contributions in their machine learning workflows.
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
67 peopleSame Organization
@campus.tu-berlin.de12
Shared Repositories
55
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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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