umap-learn
Category: data-science-and-ml · License: BSD-3-Clause license · Version: 1.3
Tags: UMAP, dimensionality reduction, embeddings, data visualization
When to use it
Reduces and embeds high-dimensional data with umap-learn (UMAP) in Python, including 2D/3D visualization, supervised and semi-supervised UMAP, DensMAP, AlignedUMAP, Parametric UMAP (Keras), transform() on new data, and inverse transforms. Use when visualizing high-dimensional data as a 2D or 3D embedding, preprocessing features for HDBSCAN clustering, using partial labels to guide an embedding, aligning embeddings across time points or batches, or projecting unseen samples into a trained embedding. Tune n_neighbors, min_dist, n_components, and metric. Not for linear PCA or t-SNE-specific workflows.Example requests
Ask your agent in plain language, for example:- “How do I get a 2D UMAP plot of my 5000x300 feature matrix?”
- “I’m analysing scRNA-seq from mouse cortex in cells.h5ad, with 20k cells after PCA to 50 components. I want to cluster with HDBSCAN. I ran UMAP with defaults to 2D and got many tiny fragmented clusters. What should I change? Please write the code.”
- “I have mass-spec proteomics profiles for 10,000 samples, and only about 300 have a confirmed disease subtype label. I’d like a low-dimensional map that uses those few labels to pull similar samples together. Later I need to place new patients’ samples onto the same map without refitting. How should I do this?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.