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name: umap-learn description: "Use the umap-learn package for UMAP dimensionality reduction, supervised and density workflows, aligned embeddings, plotting, and ParametricUMAP." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

umap-learn Repo Skill

Use this repo skill when a task asks how to use umap-learn / umap for Uniform Manifold Approximation and Projection: dimensionality reduction, visualization embeddings, transform workflows, supervised UMAP, densMAP, aligned embeddings, optional plotting, or optional ParametricUMAP.

Install and Import

pip install umap-learn

import umap
mapper = umap.UMAP()

Package facts:

• Distribution name: umap-learn.
• Import package: umap.
• Python requirement: >=3.10 in package metadata.
• Base dependencies: numpy, scipy, scikit-learn, numba, pynndescent, and tqdm.
• No package console entry points are exposed; workflows are Python API based.

Optional extras:

pip install "umap-learn[plot]"           # umap.plot helpers
pip install "umap-learn[parametric_umap]" # TensorFlow/Keras ParametricUMAP
pip install "umap-learn[tbb]"            # optional CPU optimization

Run scripts/check_umap_environment.py to inspect a target environment and optional extras:

python scripts/check_umap_environment.py --json

Route Map

Task	Read
Fit a standard UMAP embedding, tune base parameters, transform new rows, inverse transform, use sparse/precomputed inputs, or debug core API errors	core-embedding
Use supervised labels, semi-supervised targets, densMAP, density outputs, clustering interpretation, or outlier analysis	supervised-density
Align related slices/time periods, update aligned embeddings, or combine fitted UMAP models with intersection/union/contrast operators	aligned-composition
Use umap.plot points/connectivity/diagnostic/interactive helpers or fix optional plotting dependency failures	plotting-diagnostics
Use optional TensorFlow/Keras ParametricUMAP, neural encoders/decoders, reconstruction, save/load, callbacks, landmarks, or ONNX export	parametric-umap

Fast Decision Guide

• Start with umap.UMAP(random_state=42) on a sample while debugging; remove the seed later if speed matters more than reproducibility.
• Use n_neighbors for local/global neighbourhood balance and min_dist for embedding compactness.
• Use fit/fit_transform on training data and transform on held-out data; check precomputed-distance shape contracts before using metric="precomputed".
• Use fit(X, y) only when labels should influence the manifold; validate with a downstream metric rather than only a visually separated plot.
• Use densMAP only when density preservation is part of the question; some transform/inverse paths are intentionally unavailable for densMAP.
• Treat plotting and ParametricUMAP as optional extras. Missing optional dependencies should produce actionable install guidance, not block base UMAP.
• Do not claim CUDA/GPU acceleration for base umap-learn. Visible hardware is irrelevant unless a separate optional neural backend is installed and verified.

Shared References

• Read installation and environment before setting up package dependencies or optional extras.
• Read cross-cutting troubleshooting for install/import, optional extras, performance, and staleness checks.
• Read repository provenance before deciding whether this generated skill is current for a checkout.

Minimal Import Check

import umap
print(umap.__version__)
print(umap.UMAP(n_neighbors=5, n_epochs=10, random_state=42))

For a runnable toy-data check, use the core sub-skill smoke script:

python sub-skills/core-embedding/scripts/umap_core_smoke.py --transform --json