Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2025
|
| Online Access: | https://doi.org/10.5281/zenodo.15476935 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866901729038041088 |
|---|---|
| author | Martin, Tyler Sutherland, Duncan McDannald, Austin Kusne, Aaron Gilad Beaucage, Peter |
| author_facet | Martin, Tyler Sutherland, Duncan McDannald, Austin Kusne, Aaron Gilad Beaucage, Peter |
| contents | <h1>Phase Diagram Clustering Analysis</h1> <p>This project performs a series of computational experiments to evaluate different clustering methods and affinity measures on synthetic phase diagram data. It systematically sweeps through various parameters, records the performance (Fowlkes-Mallows score), and stores the results in a SQLite database.</p> <h2>Project Structure</h2> <p>- run.py: The main executable script that orchestrates the parameter sweeps and data processing.<br>- lib.py: A utility library containing helper functions for data manipulation (e.g., label conversion, coordinate transformation, Fowlkes-Mallows score calculation) and plotting.<br>- DataDaemon.py: Implements a daemon process for asynchronous, multiprocessing-safe writing of results to a SQLite database. This is crucial for efficiently handling the large number of results generated by the parameter sweeps.<br>- phase_data/: This directory contains the input phase diagram data used by `run.py`. See `phase_data/README.md` for more details.<br>- db/: This directory will contain the SQLite database (`results.db` by default) where the outcomes of the experiments are stored.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15476935 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Python Framework for Parameter Sweep-based Evaluation of Clustering Algorithms on Synthetic Phase Diagrams Martin, Tyler Sutherland, Duncan McDannald, Austin Kusne, Aaron Gilad Beaucage, Peter <h1>Phase Diagram Clustering Analysis</h1> <p>This project performs a series of computational experiments to evaluate different clustering methods and affinity measures on synthetic phase diagram data. It systematically sweeps through various parameters, records the performance (Fowlkes-Mallows score), and stores the results in a SQLite database.</p> <h2>Project Structure</h2> <p>- run.py: The main executable script that orchestrates the parameter sweeps and data processing.<br>- lib.py: A utility library containing helper functions for data manipulation (e.g., label conversion, coordinate transformation, Fowlkes-Mallows score calculation) and plotting.<br>- DataDaemon.py: Implements a daemon process for asynchronous, multiprocessing-safe writing of results to a SQLite database. This is crucial for efficiently handling the large number of results generated by the parameter sweeps.<br>- phase_data/: This directory contains the input phase diagram data used by `run.py`. See `phase_data/README.md` for more details.<br>- db/: This directory will contain the SQLite database (`results.db` by default) where the outcomes of the experiments are stored.</p> |
| title | A Python Framework for Parameter Sweep-based Evaluation of Clustering Algorithms on Synthetic Phase Diagrams |
| url | https://doi.org/10.5281/zenodo.15476935 |