probabl-ai/skore
Jan 2025 – Oct 2025
8 Months active
Languages Used
CSSPythonreStructuredTextMakefileRSTcssrstJupyter Notebook
Technical Skills
Code ConsistencyData PersistenceDeprecation ManagementDocumentationFront-end DevelopmentNumPy
Auguste Baum
PROFILE
Auguste Baum
Auguste contributed to the probabl-ai/skore repository by developing and refining features that enhance model evaluation, reporting, and user experience. Over eight months, Auguste delivered robust cross-validation reporting, advanced data visualization tools, and improved data persistence across platforms. Using Python, Pandas, and Scikit-learn, Auguste implemented efficient caching, type-safe APIs, and CI/CD workflows to ensure reliability and maintainability. The work included refactoring internal APIs for clarity, extending compatibility to Python 3.13, and strengthening data integrity through protocol updates. Auguste’s engineering approach emphasized test-driven development, clear documentation, and cross-version support, resulting in a codebase that is both reliable and extensible.
Overall Statistics
Feature vs Bugs
77%Features
Repository Contributions
59Total
Bugs
7
Commits
59
Features
23
Lines of code
15,716
Activity Months8
Your Network
28 people
Work History
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 (2025-10) focused on user experience, data persistence, and data integrity in probabl-ai/skore. Delivered three key contributions: (1) improved Skore Hub login UX with clearer feedback and robust handling when auto-launch fails; (2) migrated the default local project workspace to the user data directory to ensure persistence across platforms, with accompanying docs/tests updates; (3) enhanced data integrity and efficiency by conditionally uploading permutation data only when computed and by updating the EstimatorReport protocol to include a _hash field. These changes reduce onboarding friction, improve cross-OS reliability, and strengthen reproducibility and traceability in model evaluation.
3 Commits • 2 Features
Oct 1, 2025October 2025 (2025-10) focused on user experience, data persistence, and data integrity in probabl-ai/skore. Delivered three key contributions: (1) improved Skore Hub login UX with clearer feedback and robust handling when auto-launch fails; (2) migrated the default local project workspace to the user data directory to ensure persistence across platforms, with accompanying docs/tests updates; (3) enhanced data integrity and efficiency by conditionally uploading permutation data only when computed and by updating the EstimatorReport protocol to include a _hash field. These changes reduce onboarding friction, improve cross-OS reliability, and strengthen reproducibility and traceability in model evaluation.
October 2025
September 2025
8 Commits • 3 Features
Sep 1, 2025September 2025 monthly summary for probabl-ai/skore focusing on delivering API clarity, robust data handling, enhanced observability, and CI efficiency. Highlights include internal API naming and reporting structure refactors to improve maintainability and reliability, robust data handling and input normalization to ensure consistent train-test splits, observability enhancements for external services via added logging around retry behavior, and CI workflow optimization to reduce test runtime by running pytest serially. Minor documentation improvement also completed to align with code changes.
September 2025
8 Commits • 3 Features
Sep 1, 2025September 2025 monthly summary for probabl-ai/skore focusing on delivering API clarity, robust data handling, enhanced observability, and CI efficiency. Highlights include internal API naming and reporting structure refactors to improve maintainability and reliability, robust data handling and input normalization to ensure consistent train-test splits, observability enhancements for external services via added logging around retry behavior, and CI workflow optimization to reduce test runtime by running pytest serially. Minor documentation improvement also completed to align with code changes.
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for probabl-ai/skore. Focused on feature deliveries that strengthen model evaluation workflows and CI reliability in production-like environments. Delivered Cross-Validation Prediction Error Visualization, including refactoring of PredictionErrorDisplay to support aggregated data from multiple estimators or CV splits, plus extensive tests to ensure robustness. Implemented CI compatibility upgrades to add Python 3.13 support and refined inclusions/exclusions for specific scikit-learn versions to maintain compatibility. No explicit major bug fixes were recorded this month; emphasis was on delivering robust features and improving CI stability. Overall impact: improved evaluation diagnostics for CV experiments, smoother integration of latest Python/scikit-learn stacks, and stronger test coverage driving reliability. Technologies/skills demonstrated: Python, test-driven development, refactoring for multi-estimator data, data visualization integration, CI workflow management, and cross-version compatibility.
2 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for probabl-ai/skore. Focused on feature deliveries that strengthen model evaluation workflows and CI reliability in production-like environments. Delivered Cross-Validation Prediction Error Visualization, including refactoring of PredictionErrorDisplay to support aggregated data from multiple estimators or CV splits, plus extensive tests to ensure robustness. Implemented CI compatibility upgrades to add Python 3.13 support and refined inclusions/exclusions for specific scikit-learn versions to maintain compatibility. No explicit major bug fixes were recorded this month; emphasis was on delivering robust features and improving CI stability. Overall impact: improved evaluation diagnostics for CV experiments, smoother integration of latest Python/scikit-learn stacks, and stronger test coverage driving reliability. Technologies/skills demonstrated: Python, test-driven development, refactoring for multi-estimator data, data visualization integration, CI workflow management, and cross-version compatibility.
June 2025
May 2025
10 Commits • 2 Features
May 1, 2025May 2025: Enhanced cross-validation reporting and plotting in probabl-ai/skore. Implemented ROC and precision-recall curve plotting for cross-validation comparison reports with per-estimator curves, improved plotting API, and ensured chance level is displayed in legends. Refactored plotting internals to non-static methods and removed the ax parameter to simplify usage. Also delivered maintenance work: fixes to documentation links, prevented in-place mutations of cached DataFrames, and corrected a readability typo in the metrics error message. Extended Python compatibility to 3.13 by removing the upper bound in package configs, positioning the project for future Python versions and smoother onboarding.
May 2025
10 Commits • 2 Features
May 1, 2025May 2025: Enhanced cross-validation reporting and plotting in probabl-ai/skore. Implemented ROC and precision-recall curve plotting for cross-validation comparison reports with per-estimator curves, improved plotting API, and ensured chance level is displayed in legends. Refactored plotting internals to non-static methods and removed the ax parameter to simplify usage. Also delivered maintenance work: fixes to documentation links, prevented in-place mutations of cached DataFrames, and corrected a readability typo in the metrics error message. Extended Python compatibility to 3.13 by removing the upper bound in package configs, positioning the project for future Python versions and smoother onboarding.
April 2025
11 Commits • 3 Features
Apr 1, 2025April 2025 focused on strengthening reporting, typing discipline, and CI visibility in probabl-ai/skore. Delivered cross-report feature parity in Cross-Validation reporting, standardized type hints for positive labels and aggregation, hardened prediction-time casting to address pandas deprecations, stabilized progress bar behavior for nested reports, and updated CI to emit XML coverage reports. These changes improve evaluation consistency, developer experience, and CI feedback loops, enabling safer feature launches and clearer user guidance.
11 Commits • 3 Features
Apr 1, 2025April 2025 focused on strengthening reporting, typing discipline, and CI visibility in probabl-ai/skore. Delivered cross-report feature parity in Cross-Validation reporting, standardized type hints for positive labels and aggregation, hardened prediction-time casting to address pandas deprecations, stabilized progress bar behavior for nested reports, and updated CI to emit XML coverage reports. These changes improve evaluation consistency, developer experience, and CI feedback loops, enabling safer feature launches and clearer user guidance.
April 2025
March 2025
11 Commits • 3 Features
Mar 1, 2025March 2025 for probabl-ai/skore focused on delivering model interpretability enhancements, documentation and UX improvements, and developer experience refinements. Implemented EstimatorReport enhancements including feature permutation importance, mean decrease impurity metrics, and timing metrics, providing deeper insights into model behavior and performance. Strengthened product quality through documentation and API clarity: improved docs, docstrings, error messages, and consistent structure across config/API docs. Invested in tooling and testing to improve reproducibility and developer experience: refined pre-commit, deterministic plotting behavior (avoid caching when random_state is None), added xdoctest support, and implemented a robust __repr__ for project objects. These changes collectively reduce troubleshooting time, accelerate onboarding, and empower users with actionable insights while improving the maintainability and reliability of the codebase.
March 2025
11 Commits • 3 Features
Mar 1, 2025March 2025 for probabl-ai/skore focused on delivering model interpretability enhancements, documentation and UX improvements, and developer experience refinements. Implemented EstimatorReport enhancements including feature permutation importance, mean decrease impurity metrics, and timing metrics, providing deeper insights into model behavior and performance. Strengthened product quality through documentation and API clarity: improved docs, docstrings, error messages, and consistent structure across config/API docs. Invested in tooling and testing to improve reproducibility and developer experience: refined pre-commit, deterministic plotting behavior (avoid caching when random_state is None), added xdoctest support, and implemented a robust __repr__ for project objects. These changes collectively reduce troubleshooting time, accelerate onboarding, and empower users with actionable insights while improving the maintainability and reliability of the codebase.
February 2025
8 Commits • 5 Features
Feb 1, 2025February 2025 (Month: 2025-02) – Focused on reliability, performance, and developer productivity for the probabl-ai/skore repository. Delivered robust cross-validation reporting with graceful interruption handling and unit tests, enabling uninterrupted model evaluation and progress visibility. Extended ML task detection to support multi-output targets and non-numpy arrays, broadening applicability of automated task classification and reducing edge-case failures. Improved performance and repeatability with a caching mechanism for sub-estimator predictions in ComparisonReport, including a clear cache invalidation path. Added safe project deletion via Project.clear delete_project parameter, reducing operational risk when cleaning up projects. Invested in documentation tooling to speed builds and improve API navigation, accelerating onboarding and collaboration.
8 Commits • 5 Features
Feb 1, 2025February 2025 (Month: 2025-02) – Focused on reliability, performance, and developer productivity for the probabl-ai/skore repository. Delivered robust cross-validation reporting with graceful interruption handling and unit tests, enabling uninterrupted model evaluation and progress visibility. Extended ML task detection to support multi-output targets and non-numpy arrays, broadening applicability of automated task classification and reducing edge-case failures. Improved performance and repeatability with a caching mechanism for sub-estimator predictions in ComparisonReport, including a clear cache invalidation path. Added safe project deletion via Project.clear delete_project parameter, reducing operational risk when cleaning up projects. Invested in documentation tooling to speed builds and improve API navigation, accelerating onboarding and collaboration.
February 2025
January 2025
6 Commits • 3 Features
Jan 1, 2025January 2025 monthly summary for probabl-ai/skore: Restored default non-HTML display behavior by reverting the HTML representation of Items across environments (Jupyter compatibility restored). Cleaned up and standardized the CrossValidation reporting API to reduce user confusion by promoting EstimatorReport/CrossValidationReport under a unified 'report' terminology and deprecating CrossValidationReporter. Improved documentation UX by reducing clutter (narrower left sidebar) and removing type hints from API docs for cleaner presentation. Implemented a storage optimization for NumpyArrayItem by switching from array.tolist() to numpy.save while preserving allow_pickle=False. These changes reduce maintenance burden, improve developer experience, and enhance performance and API clarity.
January 2025
6 Commits • 3 Features
Jan 1, 2025January 2025 monthly summary for probabl-ai/skore: Restored default non-HTML display behavior by reverting the HTML representation of Items across environments (Jupyter compatibility restored). Cleaned up and standardized the CrossValidation reporting API to reduce user confusion by promoting EstimatorReport/CrossValidationReport under a unified 'report' terminology and deprecating CrossValidationReporter. Improved documentation UX by reducing clutter (narrower left sidebar) and removing type hints from API docs for cleaner presentation. Implemented a storage optimization for NumpyArrayItem by switching from array.tolist() to numpy.save while preserving allow_pickle=False. These changes reduce maintenance burden, improve developer experience, and enhance performance and API clarity.
Activity
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Quality Metrics
Correctness93.6%
Maintainability92.8%
Architecture90.4%
Performance85.2%
AI Usage20.6%
Skills & Technologies
Programming Languages
CSSJupyter NotebookMakefileMarkdownPythonRSTTOMLYAMLcssreStructuredText
Technical Skills
API DesignAPI DevelopmentBackend DevelopmentBug FixBuild SystemsCI/CDCSSCachingClient DevelopmentCode ConsistencyCode RefactoringConfiguration ManagementCross-ValidationData AccessData Persistence
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