umap-learn

name	umap-learn
description	UMAP dimensionality reduction for visualization, clustering prep, and feature engineering. Fast nonlinear manifold learning preserving local and global structure. Standard UMAP (fit/transform, sklearn-compatible), supervised/semi-supervised, Parametric UMAP (NN encoder/decoder, TensorFlow), DensMAP (density), AlignedUMAP (temporal/batch). 15+ distance metrics, custom Numba metrics, precomputed distances. For linear reduction use PCA; for neighborhood graphs use sklearn NearestNeighbors.
license	BSD-3-Clause

UMAP-Learn

Overview

UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction algorithm for visualization and general non-linear dimensionality reduction. It is faster than t-SNE, scales to larger datasets, preserves both local and global structure, and supports supervised learning and embedding of new data points.

When to Use

• Reducing high-dimensional data to 2D/3D for visualization
• Preprocessing for density-based clustering (HDBSCAN, DBSCAN)
• Feature engineering in ML pipelines (transform new data into learned embedding)
• Supervised/semi-supervised embedding with partial labels
• Tracking embeddings across time points or batches (AlignedUMAP)
• Density-preserving embeddings (DensMAP)
• Neural network-based embedding with custom architectures (Parametric UMAP)
• For linear dimensionality reduction use PCA (scikit-learn)
• For neighborhood-graph construction without embedding use scikit-learn NearestNeighbors

Prerequisites

pip install umap-learn

# For Parametric UMAP (neural network variant)
pip install umap-learn[parametric_umap]  # requires TensorFlow 2.x

Critical: Always standardize features before applying UMAP to ensure equal weighting across dimensions.

Quick Start

import umap
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_digits

# Load and scale data
X, y = load_digits(return_X_y=True)
X_scaled = StandardScaler().fit_transform(X)

# Fit and transform
embedding = umap.UMAP(random_state=42).fit_transform(X_scaled)
print(f"Input: {X_scaled.shape}, Output: {embedding.shape}")
# Input: (1797, 64), Output: (1797, 2)

Core API

1. Standard UMAP

Basic dimensionality reduction following scikit-learn conventions.

import umap
from sklearn.preprocessing import StandardScaler

X_scaled = StandardScaler().fit_transform(data)

# Method 1: fit_transform (single step)
embedding = umap.UMAP(
    n_neighbors=15,     # local neighborhood size (2-200)
    min_dist=0.1,       # min distance between embedded points (0.0-0.99)
    n_components=2,     # output dimensions
    metric='euclidean', # distance metric
    random_state=42,    # reproducibility
).fit_transform(X_scaled)
print(f"Embedding shape: {embedding.shape}")

# Method 2: fit + access (for reuse)
reducer = umap.UMAP(random_state=42)
reducer.fit(X_scaled)
embedding = reducer.embedding_  # trained embedding
graph = reducer.graph_          # fuzzy simplicial set (sparse matrix)

# Visualization
import matplotlib.pyplot as plt

plt.figure(figsize=(8, 6))
plt.scatter(embedding[:, 0], embedding[:, 1], c=labels, cmap='Spectral', s=5)
plt.colorbar()
plt.title('UMAP Embedding')
plt.tight_layout()
plt.savefig('umap_embedding.png', dpi=150)

2. Supervised & Semi-Supervised UMAP

Incorporate label information to guide embedding via the y parameter.

import umap

# Supervised — all labels known
embedding = umap.UMAP(random_state=42).fit_transform(X_scaled, y=labels)

# Semi-supervised — partial labels (mark unlabeled as -1)
semi_labels = labels.copy()
semi_labels[unlabeled_indices] = -1
embedding = umap.UMAP(random_state=42).fit_transform(X_scaled, y=semi_labels)

# Control label influence with target_weight (0.0=unsupervised, 1.0=fully supervised)
reducer = umap.UMAP(
    target_weight=0.7,               # emphasize labels
    target_metric='categorical',     # for classification; use distance metric for regression
    random_state=42
)
embedding = reducer.fit_transform(X_scaled, y=labels)
print(f"Supervised embedding: {embedding.shape}")

3. Transform New Data

Project unseen data into the trained embedding space.

import umap
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Fit on training data
reducer = umap.UMAP(n_components=10, random_state=42)
X_train_emb = reducer.fit_transform(X_train_scaled)

# Transform test data
X_test_emb = reducer.transform(X_test_scaled)
print(f"Train: {X_train_emb.shape}, Test: {X_test_emb.shape}")

# Works in sklearn Pipelines
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('umap', umap.UMAP(n_components=10, random_state=42)),
    ('classifier', SVC())
])
pipeline.fit(X_train, y_train)
accuracy = pipeline.score(X_test, y_test)
print(f"Pipeline accuracy: {accuracy:.3f}")

4. Parametric UMAP

Neural network-based embedding via TensorFlow/Keras. Enables efficient transform, reconstruction, and custom architectures.

from umap.parametric_umap import ParametricUMAP

# Default architecture (3-layer, 100-neuron FC network)
embedder = ParametricUMAP(n_components=2, random_state=42)
embedding = embedder.fit_transform(X_scaled)
new_emb = embedder.transform(new_data)  # fast neural network inference
print(f"Parametric embedding: {embedding.shape}")

import tensorflow as tf
from umap.parametric_umap import ParametricUMAP

# Custom encoder/decoder for autoencoder mode
input_dim = X_scaled.shape[1]
encoder = tf.keras.Sequential([
    tf.keras.layers.InputLayer(input_shape=(input_dim,)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(2),
])
decoder = tf.keras.Sequential([
    tf.keras.layers.InputLayer(input_shape=(2,)),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(input_dim),
])

embedder = ParametricUMAP(
    encoder=encoder, decoder=decoder, dims=(input_dim,),
    parametric_reconstruction=True, autoencoder_loss=True,
    n_training_epochs=10, batch_size=128,
    n_neighbors=15, min_dist=0.1, random_state=42
)
embedding = embedder.fit_transform(X_scaled)
reconstructed = embedder.inverse_transform(embedding)
print(f"Reconstruction error: {np.mean((X_scaled - reconstructed)**2):.4f}")

5. DensMAP

Variant preserving local density information in the embedding.

import umap

reducer = umap.UMAP(
    densmap=True,          # enable DensMAP
    dens_lambda=2.0,       # density preservation weight
    dens_frac=0.3,         # fraction for density estimation
    output_dens=True,      # output density estimates
    n_neighbors=15,
    min_dist=0.1,
    random_state=42
)
embedding = reducer.fit_transform(X_scaled)

# Access density estimates
original_density = reducer.rad_orig_  # density in original space
embedded_density = reducer.rad_emb_   # density in embedded space
print(f"DensMAP embedding: {embedding.shape}")
print(f"Density correlation: {np.corrcoef(original_density, embedded_density)[0,1]:.3f}")

6. AlignedUMAP

Align embeddings across multiple related datasets (time points, batches).

from umap import AlignedUMAP

# Multiple related datasets
datasets = [day1_data, day2_data, day3_data]

mapper = AlignedUMAP(
    n_neighbors=15,
    alignment_regularisation=1e-2,  # alignment strength
    alignment_window_size=2,        # align with N adjacent datasets
    n_components=2,
    random_state=42
)
mapper.fit(datasets)

aligned_embeddings = mapper.embeddings_  # list of aligned embedding arrays
print(f"Aligned {len(aligned_embeddings)} datasets")
for i, emb in enumerate(aligned_embeddings):
    print(f"  Dataset {i}: {emb.shape}")

Key Concepts

Parameter Tuning Guide

Parameter	Low	Medium (default)	High	Effect
n_neighbors	2-5	15	50-200	Local detail vs global structure
min_dist	0.0	0.1	0.5-0.99	Tight clusters vs spread out
n_components	2	2	5-50	Visualization vs ML/clustering
spread	0.5	1.0	2.0	Embedding scale (with min_dist)

Configuration by Use-Case

Use-Case	n_neighbors	min_dist	n_components	metric
Visualization	15	0.1	2	euclidean
Clustering (HDBSCAN)	30	0.0	5-10	euclidean
Text/document embedding	15	0.1	2	cosine
Global structure	100	0.5	2	euclidean
ML feature engineering	15-30	0.1	10-50	euclidean
Binary/set data	15	0.1	2	hamming/jaccard

Supported Metrics

Minkowski family: euclidean, manhattan, chebyshev, minkowski. Spatial: canberra, braycurtis, haversine. Correlation: cosine, correlation. Binary: hamming, jaccard, dice, russellrao, rogerstanimoto, sokalmichener, sokalsneath, yule. Special: precomputed (distance matrix), custom Numba-compiled callables.

Standard UMAP vs Parametric UMAP

Feature	Standard	Parametric
Backend	Direct optimization	TensorFlow neural network
Transform speed	Moderate	Fast (neural net inference)
Inverse transform	Approximate, expensive	Decoder network, fast
Custom architecture	No	Yes (CNNs, RNNs, etc.)
Requirements	umap-learn	umap-learn + TensorFlow 2.x
Best for	Quick exploration	Production pipelines, reconstruction

Common Workflows

Workflow 1: UMAP + HDBSCAN Clustering Pipeline

import umap
import hdbscan
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import adjusted_rand_score

# Step 1: Preprocess
X_scaled = StandardScaler().fit_transform(data)
print(f"Input shape: {X_scaled.shape}")

# Step 2: UMAP for clustering (NOT visualization parameters)
reducer = umap.UMAP(
    n_neighbors=30,     # more global structure for clustering
    min_dist=0.0,       # allow tight packing
    n_components=10,    # higher dims preserve density better than 2D
    metric='euclidean',
    random_state=42
)
embedding = reducer.fit_transform(X_scaled)

# Step 3: HDBSCAN clustering
clusterer = hdbscan.HDBSCAN(min_cluster_size=15, min_samples=5)
cluster_labels = clusterer.fit_predict(embedding)

n_clusters = len(set(cluster_labels)) - (1 if -1 in cluster_labels else 0)
noise = sum(cluster_labels == -1)
print(f"Clusters: {n_clusters}, Noise: {noise}")

# Step 4: Separate 2D embedding for visualization
vis_emb = umap.UMAP(n_neighbors=15, min_dist=0.1, random_state=42).fit_transform(X_scaled)
plt.scatter(vis_emb[:, ], vis_emb[:, ], c=cluster_labels, cmap=, s=)
plt.colorbar()
plt.title()
plt.tight_layout()
plt.savefig(, dpi=)

Workflow 2: Supervised Embedding for Classification

import umap
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import classification_report

# Split and scale
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_s = scaler.fit_transform(X_train)
X_test_s = scaler.transform(X_test)

# Supervised UMAP for feature engineering
reducer = umap.UMAP(n_components=10, random_state=42)
X_train_emb = reducer.fit_transform(X_train_s, y=y_train)
X_test_emb = reducer.transform(X_test_s)

# Downstream classifier
clf = SVC(kernel='rbf')
clf.fit(X_train_emb, y_train)
y_pred = clf.predict(X_test_emb)

print(classification_report(y_test, y_pred))

Workflow 3: Exploring Embedding Space with Inverse Transform

Text-only — combines Core API modules 1 and 3 (inverse_transform on standard UMAP):

1. Fit standard UMAP on data (Core API: Standard UMAP)
2. Create a grid of points spanning the embedding space
3. Apply reducer.inverse_transform(grid_points) to reconstruct high-dimensional data
4. Visualize reconstructed samples to understand embedding regions

Note: inverse transform is approximate; works poorly outside the convex hull of the training embedding.

Key Parameters

Parameter	Module	Default	Range	Effect
n_neighbors	UMAP	15	2-200	Local vs global structure balance
min_dist	UMAP	0.1	0.0-0.99	Cluster tightness
n_components	UMAP	2	2-100	Output dimensionality
metric	UMAP	'euclidean'	See metrics list	Distance calculation method
spread	UMAP	1.0	>0	Embedding scale (with min_dist)
n_epochs	UMAP	None (auto)	50-500+	Training iterations
learning_rate	UMAP	1.0	>0	SGD step size
init	UMAP	'spectral'	spectral/random/pca	Embedding initialization
random_state	UMAP	None	int	Reproducibility seed
target_weight	UMAP	0.5	0.0-1.0	Label influence (supervised)
densmap	UMAP	False	bool	Enable DensMAP
dens_lambda	UMAP	2.0	>0	DensMAP density weight
low_memory	UMAP	True	bool	Memory-efficient mode
encoder	ParametricUMAP	None	Keras model	Custom encoder network
decoder

Best Practices

1. Always standardize features: Use StandardScaler before UMAP — unscaled features with different ranges will dominate the embedding.

2. Set random_state for reproducibility: UMAP uses stochastic optimization; results vary between runs without a fixed seed.

3. Use different parameters for clustering vs visualization: Clustering needs n_neighbors=30, min_dist=0.0, n_components=5-10. Visualization needs n_neighbors=15, min_dist=0.1, n_components=2.

4. Anti-pattern — interpreting distances literally: UMAP preserves topology, not precise distances. Cluster separations and point distances in the embedding are not proportional to original distances.

5. Anti-pattern — using 2D embeddings for clustering: 2D projections lose density information. Use 5-10 components for HDBSCAN input.

6. Consider PCA preprocessing for very high dimensions: For data with >1000 features, reducing to 50-100 PCA components first can speed up UMAP without losing quality.

7. Use Parametric UMAP for production: When you need fast transform on new data or reconstruction capabilities, Parametric UMAP's neural network provides consistent, fast inference.

Common Recipes

Recipe: Custom Numba Distance Metric

from numba import njit
import umap

@njit()
def weighted_euclidean(x, y):
    """Custom distance with feature weights."""
    result = 0.0
    for i in range(x.shape[0]):
        result += (x[i] - y[i]) ** 2 * (1.0 + i * 0.01)  # increasing weight
    return np.sqrt(result)

embedding = umap.UMAP(metric=weighted_euclidean, random_state=42).fit_transform(data)

Recipe: Precomputed Distance Matrix

import umap
from scipy.spatial.distance import pdist, squareform

# Compute custom distance matrix
dist_matrix = squareform(pdist(data, metric='correlation'))

# Use precomputed distances
embedding = umap.UMAP(
    metric='precomputed', random_state=42
).fit_transform(dist_matrix)
print(f"Embedding from precomputed: {embedding.shape}")

Recipe: Metric Learning Pipeline

import umap
from sklearn.svm import SVC

# Train supervised embedding on labeled data
mapper = umap.UMAP(n_components=10, random_state=42)
train_emb = mapper.fit_transform(X_train, y=y_train)

# Transform unlabeled test data using learned metric
test_emb = mapper.transform(X_test)

# Downstream classifier
clf = SVC().fit(train_emb, y_train)
predictions = clf.predict(test_emb)
print(f"Accuracy: {(predictions == y_test).mean():.3f}")

Troubleshooting

Problem	Cause	Solution
Disconnected/fragmented clusters	n_neighbors too low	Increase n_neighbors (try 30-50)
Clusters too spread out	min_dist too high	Decrease min_dist (try 0.0-0.05)
All points collapsed	Bad preprocessing or min_dist too low	Check StandardScaler; increase min_dist
Poor clustering results	Using visualization parameters for clustering	Set n_neighbors=30, min_dist=0.0, n_components=5-10
Transform results differ from training	Distribution shift	Ensure test data matches training distribution; use Parametric UMAP
Slow on large datasets (>100k)	Default settings	Set low_memory=True; preprocess with PCA to 50-100 dims
First run very slow	Numba JIT compilation	Expected — subsequent runs are fast (compiled cache)
ImportError: umap	Name conflict with umap package	pip install umap-learn (not pip install umap)
Parametric UMAP import error	Missing TensorFlow	pip install umap-learn[parametric_umap]
Non-reproducible results	Missing random_state	Always set random_state=42 (or any int)

Bundled Resources

references/api_reference.md

Complete UMAP constructor parameter reference (60+ parameters organized by category: core, training, advanced structural, supervised, transform, performance, DensMAP), all methods and attributes, ParametricUMAP class with autoencoder parameters, AlignedUMAP class, utility functions (nearest_neighbors, fuzzy_simplicial_set). Core parameter tuning guidance was relocated to SKILL.md Key Concepts and Core API modules. Usage examples duplicating SKILL.md workflows omitted.

Related Skills

• scikit-learn-machine-learning — ML classifiers, preprocessing, pipelines for downstream tasks
• matplotlib-scientific-plotting — Visualization of UMAP embeddings
• scikit-bio — Biological distance matrices that can feed into UMAP via metric='precomputed'

References

• McInnes L, Healy J, Melville J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426
• Sainburg T, McInnes L, Gentner TQ. Parametric UMAP Embeddings for Representation and Semisupervised Learning. Neural Computation (2021)
• Narayan A, Berger B, Cho H. Assessing single-cell transcriptomic variability through density-preserving data visualization. Nature Biotechnology (2021) — DensMAP
• Official docs: https://umap-learn.readthedocs.io/
• GitHub: https://github.com/lmcinnes/umap