lionelkusch/hidimstat
Nov 2024 – Oct 2025
8 Months active
Languages Used
NumpyPythonScikit-learnTorchMarkdownTextrstBibTeX
Technical Skills
API DesignCode MaintenanceData ScienceData VisualizationDeep Learning FrameworksDocumentation
Joseph Paillard
PROFILE
Joseph Paillard
Joseph Paillard contributed to the lionelkusch/hidimstat repository by developing and refining advanced model interpretation and feature importance tools for machine learning workflows. Over eight months, he enhanced Conditional Permutation Importance (CPI), LOCO, and related methods, focusing on robustness, reproducibility, and usability. His work included optimizing prediction routines, clarifying API semantics, and expanding educational examples, all while maintaining rigorous documentation and licensing standards. Using Python, NumPy, and pandas, Joseph improved data handling, visualization, and testing infrastructure. His engineering approach emphasized maintainability and clarity, resulting in a more reliable, user-friendly library for statistical modeling and scientific computing applications.
Overall Statistics
Feature vs Bugs
77%Features
Repository Contributions
42Total
Bugs
5
Commits
42
Features
17
Lines of code
9,738
Activity Months8
Your Network
73 people
Work History
October 2025
5 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for lionelkusch/hidimstat. Focused on delivering a practical LOCO feature demonstration, documentation hardening, and test stability improvements. Delivered a LOCO Feature Importance Demonstration with a minimal example and visualization; improved documentation with glossary, notations, and cleaned bibliography; stabilized tests for desparsified Lasso and knockoffs by adjusting simulations and CI expectations. These efforts improve model interpretability, onboarding, and reliability of the test suite.
5 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for lionelkusch/hidimstat. Focused on delivering a practical LOCO feature demonstration, documentation hardening, and test stability improvements. Delivered a LOCO Feature Importance Demonstration with a minimal example and visualization; improved documentation with glossary, notations, and cleaned bibliography; stabilized tests for desparsified Lasso and knockoffs by adjusting simulations and CI expectations. These efforts improve model interpretability, onboarding, and reliability of the test suite.
October 2025
September 2025
6 Commits • 3 Features
Sep 1, 2025September 2025: Focused on increasing flexibility, reproducibility, and developer experience in hidimstat. Implemented direct Scikit-learn estimator support in D0CRT, refined examples with diabetes refactor and added a new CFI demo on wine data, and laid groundwork for comprehensive user/developer guides and RNG management.
September 2025
6 Commits • 3 Features
Sep 1, 2025September 2025: Focused on increasing flexibility, reproducibility, and developer experience in hidimstat. Implemented direct Scikit-learn estimator support in D0CRT, refined examples with diabetes refactor and added a new CFI demo on wine data, and laid groundwork for comprehensive user/developer guides and RNG management.
August 2025
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08: Delivered a key feature to enhance model comparison visualizations in hidimstat, focusing on performance and maintainability. Refactored the plotting workflow to improve readability and speed, reorganized data handling via pandas and seaborn, and simplified results aggregation as a list of dictionaries. Updated the model interpretation narrative to emphasize the Random Forest model's superiority over Lasso. Overall impact: faster, clearer visualizations enabling quicker data-driven decisions. Technologies demonstrated include Python, pandas, seaborn, and refactoring practices that improve code maintainability and scalability.
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08: Delivered a key feature to enhance model comparison visualizations in hidimstat, focusing on performance and maintainability. Refactored the plotting workflow to improve readability and speed, reorganized data handling via pandas and seaborn, and simplified results aggregation as a list of dictionaries. Updated the model interpretation narrative to emphasize the Random Forest model's superiority over Lasso. Overall impact: faster, clearer visualizations enabling quicker data-driven decisions. Technologies demonstrated include Python, pandas, seaborn, and refactoring practices that improve code maintainability and scalability.
August 2025
April 2025
10 Commits • 3 Features
Apr 1, 2025April 2025 monthly summary for lionelkusch/hidimstat (2025-04). Focused on robustness, API clarity, documentation quality, and expanded educational demonstrations for feature importance methods. Key outcomes include fixes that improve reproducibility and data handling, clearer API semantics, richer documentation, and a broader set of educational examples that demonstrate CPI/LOCO/PFI capabilities and caveats. Delivered: - Robust random state and imputation handling in CPI and Permutation Importance to ensure predictable results. - Group IDs data structure fix: switched from NumPy array to Python list to prevent data handling issues. - API clarity: renamed public method score to importance across CPI, LOCO, and PFI; updated examples and tests. - Documentation and citation management improvements: reformatting, deduplication, and corrections to ensure accurate citations. - Expanded educational demonstrations: added and updated examples on CPI pitfalls, conditional vs marginal importance, LOCO with non-linear models, Iris classification, Model-X Knockoffs; removed outdated example. Impact: - More reliable analytics and reproducibility, easier adoption due to clearer API semantics, and higher-quality educational material reducing support overhead. - Strengthened technical capabilities in RNG handling, data-structure decisions, and documentation tooling. Technologies/skills demonstrated: - Python, RNG control, array vs list data structures, API design and semantic clarity, documentation tooling, tests, and dataset-driven educational content (model-agnostic feature selection, non-linear LOCO, Model-X Knockoffs).
April 2025
10 Commits • 3 Features
Apr 1, 2025April 2025 monthly summary for lionelkusch/hidimstat (2025-04). Focused on robustness, API clarity, documentation quality, and expanded educational demonstrations for feature importance methods. Key outcomes include fixes that improve reproducibility and data handling, clearer API semantics, richer documentation, and a broader set of educational examples that demonstrate CPI/LOCO/PFI capabilities and caveats. Delivered: - Robust random state and imputation handling in CPI and Permutation Importance to ensure predictable results. - Group IDs data structure fix: switched from NumPy array to Python list to prevent data handling issues. - API clarity: renamed public method score to importance across CPI, LOCO, and PFI; updated examples and tests. - Documentation and citation management improvements: reformatting, deduplication, and corrections to ensure accurate citations. - Expanded educational demonstrations: added and updated examples on CPI pitfalls, conditional vs marginal importance, LOCO with non-linear models, Iris classification, Model-X Knockoffs; removed outdated example. Impact: - More reliable analytics and reproducibility, easier adoption due to clearer API semantics, and higher-quality educational material reducing support overhead. - Strengthened technical capabilities in RNG handling, data-structure decisions, and documentation tooling. Technologies/skills demonstrated: - Python, RNG control, array vs list data structures, API design and semantic clarity, documentation tooling, tests, and dataset-driven educational content (model-agnostic feature selection, non-linear LOCO, Model-X Knockoffs).
March 2025
4 Commits • 3 Features
Mar 1, 2025March 2025 monthly work summary for lionelkusch/hidimstat focusing on delivering enhanced model interpretation tools, improving code quality, and stabilizing documentation. Overall, the month delivered substantial CPI capability upgrades, improved maintainability, and stronger documentation reliability, enabling faster adoption and fewer integration issues for downstream users.
4 Commits • 3 Features
Mar 1, 2025March 2025 monthly work summary for lionelkusch/hidimstat focusing on delivering enhanced model interpretation tools, improving code quality, and stabilizing documentation. Overall, the month delivered substantial CPI capability upgrades, improved maintainability, and stronger documentation reliability, enabling faster adoption and fewer integration issues for downstream users.
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Licensing governance update completed for the hidimstat repository, migrating from BSD 2-Clause to BSD 3-Clause and adding an explicit clause about the use of the copyright holder's name. This change clarifies licensing terms for users and contributors, reduces legal ambiguity, and supports downstream adoption and compliance. No code feature work or bug fixes were released this month; the focus was on policy updates and licensing compliance.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Licensing governance update completed for the hidimstat repository, migrating from BSD 2-Clause to BSD 3-Clause and adding an explicit clause about the use of the copyright holder's name. This change clarifies licensing terms for users and contributors, reduces legal ambiguity, and supports downstream adoption and compliance. No code feature work or bug fixes were released this month; the focus was on policy updates and licensing compliance.
January 2025
2 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for repo lionelkusch/hidimstat focusing on dependency reliability and ML example enhancements. Delivered two core features with explicit dependency management and flexible loss-function support, improving onboarding, reproducibility, and experimentation capabilities. No major bugs fixed this period; emphasis on documentation and configuration for smoother installations and hands-on experimentation.
2 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for repo lionelkusch/hidimstat focusing on dependency reliability and ML example enhancements. Delivered two core features with explicit dependency management and flexible loss-function support, improving onboarding, reproducibility, and experimentation capabilities. No major bugs fixed this period; emphasis on documentation and configuration for smoother installations and hands-on experimentation.
January 2025
November 2024
13 Commits • 2 Features
Nov 1, 2024November 2024: Delivered significant performance, robustness, and documentation improvements for lionelkusch/hidimstat. Optimized permutation-based predictions for faster CPI.predict and PermutationImportance.predict, expanded Variable Importance plotting docs and examples, and strengthened CPI/LOCO workflows with runtime checks and broader test coverage, improving reliability and developer experience across production analyses.
November 2024
13 Commits • 2 Features
Nov 1, 2024November 2024: Delivered significant performance, robustness, and documentation improvements for lionelkusch/hidimstat. Optimized permutation-based predictions for faster CPI.predict and PermutationImportance.predict, expanded Variable Importance plotting docs and examples, and strengthened CPI/LOCO workflows with runtime checks and broader test coverage, improving reliability and developer experience across production analyses.
Activity
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Quality Metrics
Correctness90.0%
Maintainability89.0%
Architecture87.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
BibTeXJupyter NotebookMarkdownMatplotlibNumPyNumpyPythonRSTSQLSciPy
Technical Skills
API DesignBibTeX ManagementBug FixingCI/CDCode CleanupCode FormattingCode MaintenanceCode RefactoringCode RenamingConditional Permutation ImportanceData AnalysisData ScienceData StructuresData VisualizationDeep Learning Frameworks
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