c12d2cda6c
The flow previously merged _DEFAULT_GENERATOR_KWARGS={random_state:0} and
n_samples=num_points into generator_kwargs BEFORE hashing. Prefect only
records the user-supplied form, so the web app's synth_output_paths
disagreed with the flow's output name — a plain swiss_roll run showed
'embedding: n/a' in the runs list despite completing, because the web
looked for the hash that excluded those defaults.
Now we keep the user-supplied generator_kwargs around for hashing +
metadata, and use the merged dict only for the actual generator call.
n_samples is already captured in the stem as 'N<n>', and random_state=0
is a flow constant — neither belongs in the semantic identity.
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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.