Rust-based UMAP

Performs UMAP dimensionality reduction on the input data. This function provides a user-friendly interface with input validation before calling the Rust implementation.

Usage

umap(
  data,
  knn = NULL,
  n_dim = 2L,
  k = 15L,
  min_dist = 0.5,
  spread = 1,
  knn_method = c("balltree", "hnsw", "annoy", "nndescent", "exhaustive"),
  nn_params = params_nn(),
  umap_params = params_umap(),
  seed = 42L,
  .verbose = TRUE
)

Arguments

data

Numerical matrix or data frame. The data to embed of shape samples x features. Will be coerced to a matrix.

knn

Optional NearestNeighbours class. If provided, UMAP will skip the k-nearest neighbour graph generation and use this one. Defaults to NULL.

n_dim

Integer. Number of dimensions in the embedding space. Defaults to 2L.

k

Integer. Number of nearest neighbours to consider for manifold approximation. Larger values result in more global structure being preserved. Defaults to 15L.

min_dist

Numeric. Minimum distance between points in the embedding. Controls how tightly points are packed. Smaller values result in more clustered embeddings. Must be >= 0. Defaults to 0.5. If you use SGD, consider reducing this!

spread

Numeric. Effective scale of embedded points. Determines the scale at which embedded points will be spread out. Defaults to 1.0.

knn_method

Character. Approximate nearest neighbour algorithm to use. One of "hnsw", "annoy", "nndescent", "balltree", or "exhaustive". Defaults to "balltree".

nn_params

Named list. Nearest neighbour search parameters, see params_nn().

umap_params

Named list. UMAP algorithm parameters, see params_umap().

seed

Integer. Random seed for reproducibility. Defaults to 42L.

.verbose

Logical. Controls verbosity. Defaults to TRUE.

Value

A numerical matrix with dimensions samples x n_dim containing the UMAP embedding.