3a951b387a
- dataset-picker.js writes a compact query string (?ds=&n=&f=&j= plus intro=1/picker=0 when non-default) on every change and reads it on init. Refresh restores the page; the URL also works as a shareable deep-link. - To avoid a first-paint flicker of the <details> elements, the index route pre-resolves intro_open / picker_open from the query and renders the <details open> attribute accordingly. |
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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.