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 import StandardScaler

# 1. Preprocess data
scaler = StandardScaler()
scaled_data = scaler.fit_transform(raw_data)

# 2. Create and fit UMAP
reducer = umap.UMAP(
    n_neighbors=15,
    min_dist=0.1,
    n_components=2,
    metric='euclidean',
    random_state=42
)
embedding = reducer.fit_transform(scaled_data)

# 3. Visualize
plt.scatter(embedding[:, 0], embedding[:, 1], c=labels, cmap='Spectral', s=5)
plt.colorbar()
plt.title('UMAP Embedding')
plt.show()

Parameter Tuning Guide

UMAP has four primary parameters that control the embedding behavior. Understanding these is crucial for effective usage.

n_neighbors (default: 15)

Purpose: Balances local versus global structure in the embedding.

How it works: Controls the size of the local neighborhood UMAP examines when learning manifold structure.

Effects by value:

• Low values (2-5): Emphasizes fine local detail but may fragment data into disconnected components
• Medium values (15-20): Balanced view of both local structure and global relationships (recommended starting point)
• High values (50-200): Prioritizes broad topological structure at the expense of fine-grained details

Recommendation: Start with 15 and adjust based on results. Increase for more global structure, decrease for more local detail.

min_dist (default: 0.1)

Purpose: Controls how tightly points cluster in the low-dimensional space.

How it works: Sets the minimum distance apart that points are allowed to be in the output representation.

Effects by value:

• Low values (0.0-0.1): Creates clumped embeddings useful for clustering; reveals fine topological details
• High values (0.5-0.99): Prevents tight packing; emphasizes broad topological preservation over local structure

Recommendation: Use 0.0 for clustering applications, 0.1-0.3 for visualization, 0.5+ for loose structure.

n_components (default: 2)

Purpose: Determines the dimensionality of the embedded output space.

Key feature: Unlike t-SNE, UMAP scales well in the embedding dimension, enabling use beyond visualization.

Common uses:

• 2-3 dimensions: Visualization
• 5-10 dimensions: Clustering preprocessing (better preserves density than 2D)
• 10-50 dimensions: Feature engineering for downstream ML models

Recommendation: Use 2 for visualization, 5-10 for clustering, higher for ML pipelines.

metric (default: 'euclidean')

Purpose: Specifies how distance is calculated between input data points.

Supported metrics:

• Minkowski variants: euclidean, manhattan, chebyshev
• Spatial metrics: canberra, braycurtis, haversine
• Correlation metrics: cosine, correlation (good for text/document embeddings)
• Binary data metrics: hamming, jaccard, dice, russellrao, kulsinski, rogerstanimoto, sokalmichener, sokalsneath, yule
• Custom metrics: User-defined distance functions via Numba

Recommendation: Use euclidean for numeric data, cosine for text/document vectors, hamming for binary data.

Parameter Tuning Example

# For visualization with emphasis on local structure
umap.UMAP(n_neighbors=15, min_dist=0.1, n_components=2, metric='euclidean')

# For clustering preprocessing
umap.UMAP(n_neighbors=30, min_dist=0.0, n_components=10, metric='euclidean')

# For document embeddings
umap.UMAP(n_neighbors=15, min_dist=0.1, n_components=2, metric='cosine')

# For preserving global structure
umap.UMAP(n_neighbors=100, min_dist=0.5, n_components=2, metric='euclidean')

Supervised and Semi-Supervised Dimension Reduction

UMAP supports incorporating label information to guide the embedding process, enabling class separation while preserving internal structure.

Supervised UMAP

Pass target labels via the y parameter when fitting:

# Supervised dimension reduction
embedding = umap.UMAP().fit_transform(data, y=labels)

Key benefits:

• Achieves cleanly separated classes
• Preserves internal structure within each class
• Maintains global relationships between classes

When to use: When you have labeled data and want to separate known classes while keeping meaningful point embeddings.

Semi-Supervised UMAP

For partial labels, mark unlabeled points with -1 following scikit-learn convention:

# Create semi-supervised labels
semi_labels = labels.copy()
semi_labels[unlabeled_indices] = -1

# Fit with partial labels
embedding = umap.UMAP().fit_transform(data, y=semi_labels)

When to use: When labeling is expensive or you have more data than labels available.

Metric Learning with UMAP

Train a supervised embedding on labeled data, then apply to new unlabeled data:

# Train on labeled data
mapper = umap.UMAP().fit(train_data, train_labels)

# Transform unlabeled test data
test_embedding = mapper.transform(test_data)

# Use as feature engineering for downstream classifier
from sklearn.svm import SVC
clf = SVC().fit(mapper.embedding_, train_labels)
predictions = clf.predict(test_embedding)

When to use: For supervised feature engineering in machine learning pipelines.

UMAP for Clustering

UMAP serves as effective preprocessing for density-based clustering algorithms like HDBSCAN, overcoming the curse of dimensionality.

Best Practices for Clustering

Key principle: Configure UMAP differently for clustering than for visualization.

Recommended parameters:

• n_neighbors: Increase to ~30 (default 15 is too local and can create artificial fine-grained clusters)
• min_dist: Set to 0.0 (pack points densely within clusters for clearer boundaries)
• n_components: Use 5-10 dimensions (maintains performance while improving density preservation vs. 2D)

Clustering Workflow

import umap
import hdbscan
from sklearn.preprocessing import StandardScaler

# 1. Preprocess data
scaled_data = StandardScaler().fit_transform(data)

# 2. UMAP with clustering-optimized parameters
reducer = umap.UMAP(
    n_neighbors=30,
    min_dist=0.0,
    n_components=10,  # Higher than 2 for better density preservation
    metric='euclidean',
    random_state=42
)
embedding = reducer.fit_transform(scaled_data)

# 3. Apply HDBSCAN clustering
clusterer = hdbscan.HDBSCAN(
    min_cluster_size=15,
    min_samples=5,
    metric='euclidean'
)
labels = clusterer.fit_predict(embedding)

# 4. Evaluate
from sklearn.metrics import adjusted_rand_score
score = adjusted_rand_score(true_labels, labels)
print(f"Adjusted Rand Score: {score:.3f}")
print(f"Number of clusters: {len(set(labels)) - (1 if -
how to use umap-learn
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use umap-learn on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add umap-learn
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/davila7/claude-code-templates --skill umap-learncopy
The skills CLI fetches umap-learn from GitHub repository davila7/claude-code-templates and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/umap-learn
Reload or restart Cursor to activate umap-learn. Access the skill through slash commands (e.g., /umap-learn) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?