umap-learn

Name: umap-learn
Availability: InStock
Author: vini

Find low-dimensional embeddings of high-dimensional data using UMAP.

Updated Apr 2, 2026

Search Tags:#visualization #clustering #dimensionality-reduction #umap #manifold-learning #parametric-umap

Ready to run

npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill umap-learn-viniruggeri

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: umap-learn
Source: https://github.com/viniruggeri/applied-dynamical-systems/tree/main/.agents/skills/umap-learn
Command: npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill umap-learn-viniruggeri

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

UMAP provides fast, scalable dimensionality reduction to generate meaningful low-dimensional embeddings from high-dimensional data for visualization, clustering preprocessing, and feature engineering.

Core Features & Use Cases

Supports visualization, clustering preprocessing (e.g., with HDBSCAN), and supervised/parametric variants.
Offers Parametric UMAP, density-preserving DensMAP, and alignment for related datasets.
Provides transform and inverse_transform to project new data into the learned embedding space.

Quick Start

Train a UMAP model on your dataset to obtain a 2D embedding for visualization.

Frequently Asked Questions about umap-learn

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reduce high-dimensional embeddings for visualization?▼

UMAP provides fast dimensionality reduction to generate low-dimensional embeddings from high-dimensional data for visualization. You train a model on your dataset to obtain 2D coordinates that preserve local manifold structure.

What's the best way to preprocess data for clustering with UMAP?▼

UMAP serves as clustering preprocessing by reducing data to a low-dimensional manifold representation. The resulting embeddings can be fed into clustering algorithms to group similar data points more effectively.

Can I use parametric UMAP and DensMAP for supervised embedding tasks?▼

Yes, UMAP supports parametric, DensMAP, and supervised variants. Parametric UMAP uses neural networks for embeddings, DensMAP preserves local density, and supervised mode incorporates labels for task-specific dimensionality reduction.

Does UMAP support transform and inverse_transform for new data?▼

Yes, UMAP provides transform and inverse_transform functions. Transform projects new data into the learned embedding space, while inverse_transform maps low-dimensional coordinates back to the original high-dimensional space.

How do I align embeddings across related datasets using UMAP?▼

UMAP offers an aligned UMAP feature to find coherent low-dimensional embeddings across related datasets. This ensures that shared structures remain consistent when projecting multiple related high-dimensional sources.

What are the core UMAP parameters for manifold learning?▼

Core UMAP parameters include n_neighbors, n_components, metric, and min_dist. These control local neighborhood size, output dimensionality, distance measurement, and minimum embedding distance for manifold learning.