e94d28b8fc
Periods in filenames are avoidable and the Prefect UI dislikes them in run names. Uses a shared sci_notation helper in main.py mirrored in the flow. Stem regex (main + parser) now matches J<digits.Ee+-> to accept both old decimal-J and new sci-J filenames so the two transition together. J tag in Prefect tag list also uses the sci form, so chip filters stay consistent. Backfill script extended to find pre-transition (decimal-J) files on disk via a second base-stem variant, then rename them to the sci form. backfill_tags re-patches existing runs so their J tag matches the new canonical form. All 13 existing figs + runs renamed / retagged in-place. |
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| app | ||
| flows | ||
| scripts | ||
| .gitignore | ||
| clean.sh | ||
| makefile | ||
| pyproject.toml | ||
| README.md | ||
| requirements-frozen.txt | ||
| uv.lock | ||
Dimension Reduction Lab
A Python project exploring various dimension reduction techniques using Prefect for workflow orchestration.
Overview
This project serves as an experimental sandbox for studying dimensionality reduction and embedding algorithms within a reproducible environment. The primary goal is to evaluate and compare different techniques (like UMAP, t-SNE, PaCMAP, and TriMap) while focusing on their stability characteristics, particularly in the context of changing or drifting data distributions. By leveraging Prefect's workflow management capabilities, we can systematically analyze how these algorithms perform across arbitrary datasets, track their behavior over time, and measure their sensitivity to various hyperparameters and data perturbations.
Requirements
The project uses several key dependencies (as seen in requirements.frozen.txt):
Package Management
This project uses UV (μv) as its package manager, a fast Python package installer and resolver written in Rust. The requirements.frozen.txt file was generated using UV to ensure reproducible dependencies.
To update dependencies:
uv pip compile pyproject.toml (--all-extras) -o requirements.frozen.txt
Modifying --all-extras to include either an individual optional dependency group or all of them. See the pyproject.toml file for more information.
This project uses Prefect for workflow orchestration, for it's lightweight approach to running experiments from a UI and compatibility with single-node deployments.