9b178dad38
- /compare accepts ?stem=…&stem=… (repeated) for 2-8 runs; legacy ?a=&b= still works. compare.js parses multi-stem; template drops stem_a/_b data attrs that were unused. - compare-select.js: MAX bumped to 8, button enables at 2-8 selected. URL emitted as ?stem=… per selection. - runs list gets a dataset/algorithm chip filter bar above #runs-slot (pattern ported from metrics.js). Chips reflect the union of values in the current list; selection state persists across htmx swaps. Non- matching rows get .filtered-out (display:none). - _runs.html li now carries data-embedder/data-generator so the filter can key on them. |
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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.