awslabs/graphstorm
Mar 2025 – Jul 2025
3 Months active
Languages Used
C++PythonShell
Technical Skills
DGLEnd-to-End TestingFeature EngineeringGraph Neural NetworksMachine LearningModel Development
Oxfordblue7
PROFILE
Oxfordblue7
Over a three-month period, contributed to the awslabs/graphstorm repository by developing three core features focused on graph neural network workflows. Delivered RGAT edge feature support by extending the Relational Graph Attention Network with new modules and comprehensive end-to-end tests, enhancing model flexibility and reliability. Introduced binary classification evaluation metrics, precision_at_recall and recall_at_precision, expanding model assessment capabilities and updating documentation and configuration accordingly. Implemented a fixed-test-size configuration for link prediction training, improving evaluation reproducibility and benchmarking consistency. Work demonstrated strong proficiency in Python, PyTorch, and configuration management, with an emphasis on robust testing and maintainable machine learning infrastructure.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
3,334
Activity Months3
Your Network
1649 peopleWork History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025: Implemented a fixed-test-size configuration for Link Prediction (LP) training in the LM framework within awslabs/graphstorm. This feature passes the fixed-test-size parameter to dataloader classes to enable controlled evaluation, accompanied by a dedicated test case verifying functionality with a specific dataset and configuration. These changes improve evaluation reproducibility, benchmarking consistency, and overall model selection reliability.
1 Commits • 1 Features
Jul 1, 2025July 2025: Implemented a fixed-test-size configuration for Link Prediction (LP) training in the LM framework within awslabs/graphstorm. This feature passes the fixed-test-size parameter to dataloader classes to enable controlled evaluation, accompanied by a dedicated test case verifying functionality with a specific dataset and configuration. These changes improve evaluation reproducibility, benchmarking consistency, and overall model selection reliability.
July 2025
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for awslabs/graphstorm. Delivered two new binary classification evaluation metrics: precision_at_recall and recall_at_precision, expanding model assessment capabilities. Changes include updates to evaluation functions, configuration, documentation, and new tests to validate functionality. Core commit: 76b2f517229a7c0e3019d1c79a2878613f1b5b33. No major bugs reported this month. Impact: enhanced evaluation accuracy for binary tasks, enabling better model selection and reliability. Technologies/skills demonstrated: Python, ML metric design, testing, documentation, and configuration management.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for awslabs/graphstorm. Delivered two new binary classification evaluation metrics: precision_at_recall and recall_at_precision, expanding model assessment capabilities. Changes include updates to evaluation functions, configuration, documentation, and new tests to validate functionality. Core commit: 76b2f517229a7c0e3019d1c79a2878613f1b5b33. No major bugs reported this month. Impact: enhanced evaluation accuracy for binary tasks, enabling better model selection and reliability. Technologies/skills demonstrated: Python, ML metric design, testing, documentation, and configuration management.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for awslabs/graphstorm focused on delivering RGAT edge features and strengthening test coverage. No major bugs reported this month; emphasis on feature delivery, reliability, and cross-task validation.
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for awslabs/graphstorm focused on delivering RGAT edge features and strengthening test coverage. No major bugs reported this month; emphasis on feature delivery, reliability, and cross-task validation.
March 2025
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Quality Metrics
Correctness100.0%
Maintainability92.6%
Architecture92.6%
Performance85.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++PythonShell
Technical Skills
Configuration ManagementDGLData ScienceEnd-to-End TestingFeature EngineeringGraph Neural NetworksLink PredictionMachine LearningModel DevelopmentModel EvaluationPyTorchPython ScriptingShell ScriptingSoftware DevelopmentTesting
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