tools-only UMAP API Reference
UMAP Class
umap.UMAP(n_neighbors=15, n_components=2, metric='euclidean', n_epochs=None, learning_rate=1.0, init='spectral', min_dist=0.1, spread=1.0, low_memory=True, set_op_mix_ratio=1.0, local_connectivity=1.0, repulsion_strength=1.0, negative_sample_rate=5, transform_queue_size=4.0, a=None, b=None, random_state=None, metric_kwds=None, angular_rp_forest=False, target_n_neighbors=-1, target_metric='categorical', target_metric_kwds=None, target_weight=0.5, transform_seed=42, transform_mode='embedding', force_approximation_algorithm=False, verbose=False, unique=False, densmap=False, dens_lambda=2.0, dens_frac=0.3, dens_var_shift=0.1, output_dens=False, disconnection_distance=None, precomputed_knn=(None, None, None))
Find low-dimensional embedding that approximates the underlying manifold of the data.
Core Parameters
n_neighbors (int, default: 15)
Size of the local neighborhood used for manifold approximation. Larger values result in more global views of the manifold, while smaller values preserve more local structure. Generally in the range 2 to 100.
Tuning guidance:
- Use 2-5 for very local structure
- Use 10-20 for balanced local/global structure (typical)
- Use 50-200 for emphasizing global structure
n_components (int, default: 2)
Dimension of the embedding space. Unlike t-SNE, UMAP scales well with increasing embedding dimensions.
Common values:
- 2-3: Visualization
- 5-10: Clustering preprocessing
- 10-100: Feature engineering for downstream ML
metric (str or callable, default: 'euclidean')
Distance metric to use. Accepts:
- Any metric from scipy.spatial.distance
- Any metric from sklearn.metrics
- Custom callable distance functions (must be compiled with Numba)
Common metrics:
'euclidean': Standard Euclidean distance (default)'manhattan': L1 distance'cosine': Cosine distance (good for text/document vectors)'correlation': Correlation distance'hamming': Hamming distance (for binary data)'jaccard': Jaccard distance (for binary/set data)'dice': Dice distance'canberra': Canberra distance'braycurtis': Bray-Curtis distance'chebyshev': Chebyshev distance'minkowski': Minkowski distance (specify p with metric_kwds)'precomputed': Use precomputed distance matrix
min_dist (float, default: 0.1)
Effective minimum distance between embedded points. Controls how tightly points are packed together. Smaller values result in clumpier embeddings.
Tuning guidance:
- Use 0.0 for clustering applications
- Use 0.1-0.3 for visualization (balanced)
- Use 0.5-0.99 for loose structure preservation
spread (float, default: 1.0)
Effective scale of embedded points. Combined with min_dist to control clumped vs. spread-out embeddings. Determines how spread out the clusters are in the embedding space.
Training Parameters
n_epochs (int, default: None)
Number of training epochs. If None, automatically determined based on dataset size (typically 200-500 epochs).
Manual tuning:
- Smaller datasets may need 500+ epochs
- Larger datasets may converge with 200 epochs
- More epochs = better optimization but slower training
learning_rate (float, default: 1.0)
Initial learning rate for the SGD optimizer. Higher values lead to faster convergence but may overshoot optimal solutions.
init (str or np.ndarray, default: 'spectral')
Initialization method for the embedding:
'spectral': Use spectral embedding (default, usually best)'random': Random initialization'pca': Initialize with PCA- numpy array: Custom initialization (shape: (n_samples, n_components))
Advanced Structural Parameters
local_connectivity (int, default: 1.0)
Number of nearest neighbors assumed to be locally connected. Higher values give more connected manifolds.
set_op_mix_ratio (float, default: 1.0)
Interpolation between union and intersection when constructing fuzzy set unions. Value of 1.0 uses pure union, 0.0 uses pure intersection.
repulsion_strength (float, default: 1.0)
Weighting applied to negative samples in low-dimensional embedding optimization. Higher values push embedded points further apart.
negative_sample_rate (int, default: 5)
Number of negative samples to select per positive sample. Higher values lead to greater repulsion between points and more spread-out embeddings but increase computational cost.
Supervised Learning Parameters
target_n_neighbors (int, default: -1)
Number of nearest neighbors to use when constructing target simplicial set. If -1, uses n_neighbors value.
target_metric (str, default: 'categorical')
Distance metric for target values (labels):
'categorical': For classification tasks- Any other metric for regression tasks
target_weight (float, default: 0.5)
Weight applied to target information vs. data structure. Range 0.0 to 1.0:
- 0.0: Pure unsupervised embedding (ignores labels)
- 0.5: Balanced (default)
- 1.0: Pure supervised embedding (only considers labels)
Transform Parameters
transform_queue_size (float, default: 4.0)
Size of the nearest neighbor search queue for transform operations. Larger values improve transform accuracy but increase memory usage and computation time.
transform_seed (int, default: 42)
Random seed for transform operations. Ensures reproducibility of transform results.
transform_mode (str, default: 'embedding')
Method for transforming new data:
'embedding': Standard approach (default)'graph': Use nearest neighbor graph
Performance Parameters
low_memory (bool, default: True)
Whether to use a memory-efficient implementation. Set to False only if memory is not a constraint and you want faster performance.
verbose (bool, default: False)
Whether to print progress messages during fitting.
unique (bool, default: False)
Whether to consider only unique data points. Set to True if you know your data contains many duplicates to improve performance.
force_approximation_algorithm (bool, default: False)
Force use of approximate nearest neighbor search even for small datasets. Can improve performance on large datasets.
angular_rp_forest (bool, default: False)
Whether to use angular random projection forest for nearest neighbor search. Can improve performance for normalized data in high dimensions.
DensMAP Parameters
DensMAP is a variant that preserves local density information.
densmap (bool, default: False)
Whether to use the DensMAP algorithm instead of standard UMAP. Preserves local density in addition to topological structure.
dens_lambda (float, default: 2.0)
Weight of density preservation term in DensMAP optimization. Higher values emphasize density preservation.
dens_frac (float, default: 0.3)
Fraction of dataset used for density estimation in DensMAP.
dens_var_shift (float, default: 0.1)
Regularization parameter for density estimation in DensMAP.
output_dens (bool, default: False)
Whether to output local density estimates in addition to the embedding. Results stored in rad_orig_ and rad_emb_ attributes.
Other Parameters
a (float, default: None)
Parameter controlling embedding. If None, determined automatically from min_dist and spread.
b (float, default: None)
Parameter controlling embedding. If None, determined automatically from min_dist and spread.
random_state (int, RandomState instance, or None, default: None)
Random state for reproducibility. Set to an integer for reproducible results.
metric_kwds (dict, default: None)
Additional keyword arguments for the distance metric.
disconnection_distance (float, default: None)
Distance threshold for considering points disconnected. If None, uses max distance in the graph.
precomputed_knn (tuple, default: (None, None, None))
Precomputed k-nearest neighbors as (knn_indices, knn_dists, knn_search_index). Useful for reusing expensive computations.
Methods
fit(X, y=None)
Fit the UMAP model to the data.
Parameters:
X: array-like, shape (n_samples, n_features) - Training datay: array-like, shape (n_samples,), optional - Target values for supervised dimension reduction
Returns:
self: Fitted UMAP object
Attributes set:
embedding_: The embedded representation of training datagraph_: Fuzzy simplicial set approximation to the manifold_raw_data: Copy of the training data_small_data: Whether the dataset is considered small_metric_kwds: Processed metric keyword arguments_n_neighbors: Actual n_neighbors used_initial_alpha: Initial learning rate_a,_b: Curve parameters
fit_transform(X, y=None)
Fit the model and return the embedded representation.
Parameters:
X: array-like, shape (n_samples, n_features) - Training datay: array-like, shape (n_samples,), optional - Target values for supervised dimension reduction
Returns:
X_new: array, shape (n_samples, n_components) - Embedded data
transform(X)
Transform new data into the existing embedded space.
Parameters:
X: array-like, shape (n_samples, n_features) - New data to transform
Returns:
X_new: array, shape (n_samples, n_components) - Embedded representation of new data
Important notes:
- The model must be fitted before calling transform
- Transform quality depends on similarity between training and test distributions
- For significantly different data distributions, consider Parametric UMAP
inverse_transform(X)
Transform data from the embedded space back to the original data space.
Parameters:
X: array-like, shape (n_samples, n_components) - Embedded data points
Returns:
X_new: array, shape (n_samples, n_features) - Reconstructed data in original space
Important notes:
- Computationally expensive operation
- Works poorly outside the convex hull of the training embedding
- Reconstruction quality varies by region
update(X)
Update the model with new data. Allows incremental fitting.
Parameters:
X: array-like, shape (n_samples, n_features) - New data to incorporate
Returns:
self: Updated UMAP object
Note: Experimental feature, may not preserve all properties of batch training.
Attributes
embedding_
array, shape (n_samples, n_components) - The embedded representation of the training data.
graph_
scipy.sparse.csr_matrix - The weighted adjacency matrix of the fuzzy simplicial set approximation to the manifold.
_raw_data
array - Copy of the raw training data.
_sparse_data
bool - Whether the training data was sparse.
_small_data
bool - Whether the dataset was considered small (uses different algorithm for small datasets).
_input_hash
str - Hash of the input data for caching purposes.
_knn_indices
array - Indices of k-nearest neighbors for each training point.
_knn_dists
array - Distances to k-nearest neighbors for each training point.
_rp_forest
list - Random projection forest used for approximate nearest neighbor search.
ParametricUMAP Class
umap.ParametricUMAP(encoder=None, decoder=None, parametric_reconstruction=False, autoencoder_loss=False, reconstruction_validation=None, dims=None, batch_size=None, n_training_epochs=1, loss_report_frequency=10, optimizer=None, keras_fit_kwargs={}, **kwargs)
Parametric UMAP using neural networks to learn the embedding function.
Additional Parameters (beyond UMAP)
encoder (tensorflow.keras.Model, default: None)
Keras model for encoding data to embeddings. If None, uses default 3-layer architecture with 100 neurons per layer.
decoder (tensorflow.keras.Model, default: None)
Keras model for decoding embeddings back to data space. Only used if parametric_reconstruction=True.
parametric_reconstruction (bool, default: False)
Whether to use parametric reconstruction. Requires decoder model.
autoencoder_loss (bool, default: False)
Whether to include reconstruction loss in the optimization. Requires decoder model.
reconstruction_validation (tuple, default: None)
Validation data (X_val, y_val) for monitoring reconstruction loss during training.
dims (tuple, default: None)
Input dimensions for the encoder network. Required if providing custom encoder.
batch_size (int, default: None)
Batch size for neural network training. If None, determined automatically.
n_training_epochs (int, default: 1)
Number of training epochs for the neural networks. More epochs improve quality but increase training time.
loss_report_frequency (int, default: 10)
How often to report loss during training.
optimizer (tensorflow.keras.optimizers.Optimizer, default: None)
Keras optimizer for training. If None, uses Adam with learning_rate parameter.
keras_fit_kwargs (dict, default: {})
Additional keyword arguments passed to the Keras fit() method.
Methods
Same as UMAP class, but transform() and inverse_transform() use learned neural networks for faster inference.
Utility Functions
umap.nearest_neighbors(X, n_neighbors, metric, metric_kwds={}, angular=False, random_state=None)
Compute k-nearest neighbors for the data.
Returns: (knn_indices, knn_dists, rp_forest)
umap.fuzzy_simplicial_set(X, n_neighbors, random_state, metric, metric_kwds={}, knn_indices=None, knn_dists=None, angular=False, set_op_mix_ratio=1.0, local_connectivity=1.0, apply_set_operations=True, verbose=False, return_dists=None)
Construct fuzzy simplicial set representation of the data.
Returns: Fuzzy simplicial set as sparse matrix
umap.simplicial_set_embedding(data, graph, n_components, initial_alpha, a, b, gamma, negative_sample_rate, n_epochs, init, random_state, metric, metric_kwds, densmap, densmap_kwds, output_dens, output_metric, output_metric_kwds, euclidean_output, parallel=False, verbose=False)
Perform the optimization to find a low-dimensional embedding.
Returns: Embedding array
umap.find_ab_params(spread, min_dist)
Fit a, b params for the UMAP curve from spread and min_dist.
Returns: (a, b) tuple
AlignedUMAP Class
umap.AlignedUMAP(n_neighbors=15, n_components=2, metric='euclidean', alignment_regularisation=1e-2, alignment_window_size=3, **kwargs)
UMAP variant for aligning multiple related datasets.
Additional Parameters
alignment_regularisation (float, default: 1e-2)
Strength of alignment regularization between datasets.
alignment_window_size (int, default: 3)
Number of adjacent datasets to align.
Methods
fit(X)
Fit model to multiple datasets.
Parameters:
X: list of arrays - List of datasets to align
Returns:
self: Fitted model
Attributes
embeddings_
list of arrays - List of aligned embeddings, one per input dataset.
Usage Examples
Basic Usage with All Common Parameters
import umap
# Standard 2D visualization embedding
reducer = umap.UMAP(
n_neighbors=15, # Balance local/global structure
n_components=2, # Output dimensions
metric='euclidean', # Distance metric
min_dist=0.1, # Minimum distance between points
spread=1.0, # Scale of embedded points
random_state=42, # Reproducibility
n_epochs=200, # Training iterations (None = auto)
learning_rate=1.0, # SGD learning rate
init='spectral', # Initialization method
low_memory=True, # Memory-efficient mode
verbose=True # Print progress
)
embedding = reducer.fit_transform(data)
Supervised Learning
# Train with labels for class separation
reducer = umap.UMAP(
n_neighbors=15,
target_weight=0.5, # Balance data structure vs labels
target_metric='categorical', # Metric for labels
random_state=42
)
embedding = reducer.fit_transform(data, y=labels)
Clustering Preprocessing
# Optimized for clustering
reducer = umap.UMAP(
n_neighbors=30, # More global structure
min_dist=0.0, # Allow tight packing
n_components=10, # Higher dimensions for density
metric='euclidean',
random_state=42
)
embedding = reducer.fit_transform(data)
Custom Distance Metric
from numba import njit
@njit()
def custom_distance(x, y):
"""Custom distance function (must be Numba-compatible)"""
result = 0.0
for i in range(x.shape[0]):
result += abs(x[i] - y[i])
return result
reducer = umap.UMAP(metric=custom_distance)
embedding = reducer.fit_transform(data)
Parametric UMAP with Custom Architecture
import tensorflow as tf
from umap.parametric_umap import ParametricUMAP
# Define custom encoder
encoder = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=(input_dim,)),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(2) # Output dimension
])
# Define decoder for reconstruction
decoder = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=(2,)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(input_dim)
])
# Train parametric UMAP with autoencoder
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(data)
new_embedding = embedder.transform(new_data)
reconstructed = embedder.inverse_transform(embedding)
DensMAP for Density Preservation
# Preserve local density information
reducer = umap.UMAP(
densmap=True, # Enable DensMAP
dens_lambda=2.0, # Weight of density preservation
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(data)
# Access density estimates
original_density = reducer.rad_orig_ # Density in original space
embedded_density = reducer.rad_emb_ # Density in embedded space
Aligned UMAP for Time Series
from umap import AlignedUMAP
# Multiple related datasets (e.g., different time points)
datasets = [day1_data, day2_data, day3_data, day4_data]
# Align embeddings
mapper = AlignedUMAP(
n_neighbors=15,
alignment_regularisation=1e-2, # Alignment strength
alignment_window_size=2, # Align with adjacent datasets
n_components=2,
random_state=42
)
mapper.fit(datasets)
# Access aligned embeddings
aligned_embeddings = mapper.embeddings_
# aligned_embeddings[0] is day1 embedding
# aligned_embeddings[1] is day2 embedding, etc.