Go to file
Michael Pilosov 4576088c73 labels: distinguish swiss_roll vs swiss_roll_hole in the UI
- _run_view uses a new _dataset_id(path, kwargs) that matches DATASET_META
  by (path, cleaned kwargs) and returns the catalogue key — so the runs
  list / filter chips show 'swiss_roll' vs 'swiss_roll_hole' rather than
  collapsing both to 'make_swiss_roll'.
- _enrich_with_labels replaces the stem-derived meta.generator with the
  matched DATASET_META key, so the compare panel header + diff-highlight
  also distinguish the two variants.
2026-04-22 17:12:46 -06:00
app labels: distinguish swiss_roll vs swiss_roll_hole in the UI 2026-04-22 17:12:46 -06:00
flows flow: hash user-supplied generator_kwargs, not the merged dict 2026-04-22 17:04:50 -06:00
scripts stems: fold generator_kwargs into the hash; fix swiss_roll vs hole ambiguity 2026-04-22 16:30:42 -06:00
.gitignore some minor upgrades to prefect syntax 2026-04-21 18:02:39 -06:00
clean.sh some minor upgrades to prefect syntax 2026-04-21 18:02:39 -06:00
makefile rename folder 2026-04-21 19:30:45 -06:00
pyproject.toml some minor upgrades to prefect syntax 2026-04-21 18:02:39 -06:00
README.md some minor upgrades to prefect syntax 2026-04-21 18:02:39 -06:00
requirements-frozen.txt some minor upgrades to prefect syntax 2026-04-21 18:02:39 -06:00
uv.lock some minor upgrades to prefect syntax 2026-04-21 18:02:39 -06:00

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.