IBM/materials
Nov 2024 – Nov 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data LoadingData VisualizationMachine LearningMachine Learning ExamplesPythonchemoinformatics
Lisa Hamada
PROFILE
Lisa Hamada
Contributed to the IBM/materials repository by integrating Mordred and MorganFingerprint molecular descriptors, enhancing data handling and initialization for more robust model training and evaluation. Improved preprocessing by excluding rows with NaN values, which increased data cleanliness and model reliability. Updated the FM4M example notebook to include matplotlib-based visualizations and expanded documentation, making workflows and architecture clearer for users. Streamlined asset management by replacing obsolete pickle files with new binary representation model files, reducing repository clutter. Expanded demo usability by enabling additional model options and simplifying parameters. Leveraged Python, pandas, and chemoinformatics techniques to strengthen data pipelines and user-facing features.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
4
Lines of code
3,659
Activity Months1
Your Network
999 peopleSame Organization
@ibm.com988
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Aayush-PanchalMember
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Abhijit DeokarMember
Abhina SreeMember
Abhishek KumarMember
Abhishek Kr SrivastavMember
Abraham Lara GranadosMember
Adapa ChathuryaMember
Work History
November 2024
8 Commits • 4 Features
Nov 1, 2024Concise monthly summary for IBM/materials (2024-11): Key features delivered and improvements: - Mordred and MorganFingerprint descriptor integration with enhanced data handling, initialization, and NaN robustness for model training/evaluation, plus clearer model descriptions. - Representation model assets enhancement: added new binary representation model files and removed obsolete pickle files to streamline assets and reduce clutter. - FM4M notebook documentation and visualization improvements: updated example notebook to include matplotlib usage and documentation sections for Architecture and Workflow with placeholder visuals. - Demo/app usability and model options expansion: removed private-launch parameter share and enabled more models in the demo, improving usability and model selection for users. Major bugs fixed: - Implemented exclusion of rows containing NaN values in preprocessing, improving data cleanliness and model reliability. - General robustness enhancements for Mordred/MorganFingerprint functions and data handling to reduce edge-case failures. Overall impact and accomplishments: - Strengthened data pipeline reliability and model interpretability by integrating robust descriptors and clearer model descriptions. - Reduced asset clutter, improving maintainability and deployment readiness. - Expanded user-facing capabilities (more models in demo) and enhanced documentation for easier adoption and onboarding. Technologies/skills demonstrated: - Molecular descriptors: Mordred, MorganFingerprint - Data preprocessing and NaN handling - Python-based data pipelines and feature engineering - Notebook documentation, visualization (matplotlib) and Workflow/Architecture documentation - Asset management and repo hygiene (replacing old pickle assets with binaries)
8 Commits • 4 Features
Nov 1, 2024Concise monthly summary for IBM/materials (2024-11): Key features delivered and improvements: - Mordred and MorganFingerprint descriptor integration with enhanced data handling, initialization, and NaN robustness for model training/evaluation, plus clearer model descriptions. - Representation model assets enhancement: added new binary representation model files and removed obsolete pickle files to streamline assets and reduce clutter. - FM4M notebook documentation and visualization improvements: updated example notebook to include matplotlib usage and documentation sections for Architecture and Workflow with placeholder visuals. - Demo/app usability and model options expansion: removed private-launch parameter share and enabled more models in the demo, improving usability and model selection for users. Major bugs fixed: - Implemented exclusion of rows containing NaN values in preprocessing, improving data cleanliness and model reliability. - General robustness enhancements for Mordred/MorganFingerprint functions and data handling to reduce edge-case failures. Overall impact and accomplishments: - Strengthened data pipeline reliability and model interpretability by integrating robust descriptors and clearer model descriptions. - Reduced asset clutter, improving maintainability and deployment readiness. - Expanded user-facing capabilities (more models in demo) and enhanced documentation for easier adoption and onboarding. Technologies/skills demonstrated: - Molecular descriptors: Mordred, MorganFingerprint - Data preprocessing and NaN handling - Python-based data pipelines and feature engineering - Notebook documentation, visualization (matplotlib) and Workflow/Architecture documentation - Asset management and repo hygiene (replacing old pickle assets with binaries)
November 2024
Activity
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Quality Metrics
Correctness92.6%
Maintainability87.6%
Architecture87.6%
Performance90.0%
AI Usage70.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data LoadingData VisualizationMachine LearningMachine Learning ExamplesPythonchemoinformaticsdata managementdata modelingdata processingdata representationdata sciencefile handlingmachine learningmodel developmentmodel evaluation
Repositories Contributed To
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