umap-learn

name	umap-learn
description	UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

UMAP-Learn

Overview

UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique for visualization and general non-linear dimensionality reduction. Apply this skill for fast, scalable embeddings that preserve local and global structure, supervised learning, and clustering preprocessing.

Quick Start

Installation

uv pip install umap-learn

Basic Usage

UMAP follows scikit-learn conventions and can be used as a drop-in replacement for t-SNE or PCA.

import umap
from sklearn.preprocessing import StandardScaler

# Prepare data (standardization is essential)
scaled_data = StandardScaler().fit_transform(data)

# Method 1: Single step (fit and transform)
embedding = umap.UMAP().fit_transform(scaled_data)

# Method 2: Separate steps (for reusing trained model)
reducer = umap.UMAP(random_state=42)
reducer.fit(scaled_data)
embedding = reducer.embedding_  # Access the trained embedding

Critical preprocessing requirement: Always standardize features to comparable scales before applying UMAP to ensure equal weighting across dimensions.

Typical Workflow

import umap
import matplotlib.pyplot as plt
from sklearn.preprocessing  StandardScaler


scaler = StandardScaler()
scaled_data = scaler.fit_transform(raw_data)


reducer = umap.UMAP(
    n_neighbors=,
    min_dist=,
    n_components=,
    metric=,
    random_state=
)
embedding = reducer.fit_transform(scaled_data)


plt.scatter(embedding[:, ], embedding[:, ], c=labels, cmap=, s=)
plt.colorbar()
plt.title()
plt.show()