automl/neps
Apr 2025 – Feb 2026
5 Months active
Languages Used
PythonTorchTOMLYAML
Technical Skills
Backend DevelopmentCode AnnotationDebuggingDomain Specific LanguagesError HandlingNumerical Computation
Sohambasu07
PROFILE
Sohambasu07
Soham Basu contributed to the automl/neps repository by developing and enhancing multi-objective optimization workflows, focusing on robust backend and API design using Python and PyTorch. Over five months, he delivered features such as PriMO integration for multi-objective priors, mixed-space optimization with a WrappedAcquisition class, and improved fidelity management for text processing. His work addressed reliability through targeted bug fixes, expanded test coverage, and compatibility upgrades, including support for NumPy 2.0+. Soham’s approach combined algorithm design, code refactoring, and continuous integration, resulting in scalable, production-ready optimization paths that improved decision-making and experimentation efficiency for machine learning workflows.
Overall Statistics
Feature vs Bugs
62%Features
Repository Contributions
35Total
Bugs
5
Commits
35
Features
8
Lines of code
23,177
Activity Months5
Your Network
10 peopleShared Repositories
10
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 highlights focused delivery in automl/neps, delivering a mixed-space optimization workflow and preparing for scalable hyperparameter search across heterogeneous parameter types. Key work centered on introducing a WrappedAcquisition class to support fixed numerical dimensions during categorical optimization, and updating the acquisition workflow to enable a two-step optimization process for mixed spaces. Additional dependencies were added to enable these functionalities and ensure smooth integration with existing components. No major bugs reported this month; primary value came from architectural and workflow improvements that position the project for faster, more scalable optimization.
1 Commits • 1 Features
Feb 1, 2026February 2026 highlights focused delivery in automl/neps, delivering a mixed-space optimization workflow and preparing for scalable hyperparameter search across heterogeneous parameter types. Key work centered on introducing a WrappedAcquisition class to support fixed numerical dimensions during categorical optimization, and updating the acquisition workflow to enable a two-step optimization process for mixed spaces. Additional dependencies were added to enable these functionalities and ensure smooth integration with existing components. No major bugs reported this month; primary value came from architectural and workflow improvements that position the project for faster, more scalable optimization.
February 2026
August 2025
5 Commits • 2 Features
Aug 1, 2025August 2025 monthly summary for automl/neps focusing on stability, compatibility, and fidelity improvements. Key changes included Hyperband bracket reliability fixes, NumPy 2.0+ compatibility upgrade, reduction of decoding clipping warnings, and fidelity stopping criteria enhancements for text processing.
August 2025
5 Commits • 2 Features
Aug 1, 2025August 2025 monthly summary for automl/neps focusing on stability, compatibility, and fidelity improvements. Key changes included Hyperband bracket reliability fixes, NumPy 2.0+ compatibility upgrade, reduction of decoding clipping warnings, and fidelity stopping criteria enhancements for text processing.
July 2025
14 Commits • 2 Features
Jul 1, 2025July 2025 summary for automl/neps focusing on business value and technical accomplishments. Delivered PriMO multi-objective priors and optimizer integration, enabling MO priors and related API updates. Enhanced the Multi-Objective Optimization Framework with MO API support and multi-fidelity runtime, accompanied by tests validating fidelity, prior handling, and multiple objectives. Improved stability and code quality by reducing log noise (disabling info_dict logging) and addressing formatting and test maintenance to support MO/primo features. These changes establish a production-friendly MO path and lay groundwork for faster MO experimentation and decision-making across products.
14 Commits • 2 Features
Jul 1, 2025July 2025 summary for automl/neps focusing on business value and technical accomplishments. Delivered PriMO multi-objective priors and optimizer integration, enabling MO priors and related API updates. Enhanced the Multi-Objective Optimization Framework with MO API support and multi-fidelity runtime, accompanied by tests validating fidelity, prior handling, and multiple objectives. Improved stability and code quality by reducing log noise (disabling info_dict logging) and addressing formatting and test maintenance to support MO/primo features. These changes establish a production-friendly MO path and lay groundwork for faster MO experimentation and decision-making across products.
July 2025
May 2025
11 Commits • 3 Features
May 1, 2025May 2025 monthly summary for automl/neps: Delivered end-to-end multi-objective optimization enhancements across NEPS, enabling robust multi-objective promotion and optimization workflows. Introduced core MO components including MOASHA (multi-objective promotion and state), MO_Hyperband, and multi-objective Bayesian optimization via qLNEHVI, with epsnet MO promotion strategy. Expanded and stabilized testing infrastructure for MO-related optimizers and priors/configs to ensure reliability. Performed licensing and attribution updates to align with current open-source compliance. Impact: empowers data-driven, multi-objective decision making for promotions and resource allocation, improves search efficiency and solution quality, and reduces risk through validated tests and compliant licensing.
May 2025
11 Commits • 3 Features
May 1, 2025May 2025 monthly summary for automl/neps: Delivered end-to-end multi-objective optimization enhancements across NEPS, enabling robust multi-objective promotion and optimization workflows. Introduced core MO components including MOASHA (multi-objective promotion and state), MO_Hyperband, and multi-objective Bayesian optimization via qLNEHVI, with epsnet MO promotion strategy. Expanded and stabilized testing infrastructure for MO-related optimizers and priors/configs to ensure reliability. Performed licensing and attribution updates to align with current open-source compliance. Impact: empowers data-driven, multi-objective decision making for promotions and resource allocation, improves search efficiency and solution quality, and reduces risk through validated tests and compliant licensing.
April 2025
4 Commits
Apr 1, 2025In April 2025, the automl/neps project focused on reliability and data integrity, delivering two high-impact bug fixes with targeted tests and observability improvements.
4 Commits
Apr 1, 2025In April 2025, the automl/neps project focused on reliability and data integrity, delivering two high-impact bug fixes with targeted tests and observability improvements.
April 2025
Activity
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Quality Metrics
Correctness85.4%
Maintainability85.6%
Architecture82.6%
Performance78.2%
AI Usage24.0%
Skills & Technologies
Programming Languages
PythonTOMLTorchYAML
Technical Skills
API DesignAPI DevelopmentAlgorithm DesignAlgorithm ImplementationAlgorithm OptimizationAsynchronous ProgrammingAttributionBackend DevelopmentBayesian OptimizationCode AnnotationCode FormattingCode RefactoringContinuous IntegrationData ScienceDebugging
Repositories Contributed To
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