Parametric UMAP

Performs parametric UMAP dimensionality reduction using a neural network encoder trained on the GPU via wgpu.

Usage

parametric_umap(
  data,
  n_dim = 2L,
  k = 15L,
  min_dist = 0.1,
  spread = 1,
  knn_method = c("hnsw", "annoy", "nndescent", "balltree", "exhaustive"),
  nn_params = manifoldsR::params_nn(),
  parametric_umap_params = params_parametric_umap(),
  use_gpu = TRUE,
  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.

n_dim

Integer. Number of embedding dimensions. Defaults to 2L.

k

Integer. Number of nearest neighbours. Defaults to 15L.

min_dist

Numeric. Minimum distance between embedded points. Defaults to 0.1.

spread

Numeric. Effective scale of embedded points. Defaults to 1.0.

knn_method

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

nn_params

Named list. Nearest neighbour parameters, see manifoldsR::params_nn().

parametric_umap_params

Named list. Parametric UMAP parameters, see params_parametric_umap().

use_gpu

Boolean. Shall the neural net be trained on GPU via the wgpu backend. On smaller datasets, the CPU can be faster (via the ndarray) backend due to kernel launch overhead. data sets, the CPU will be faster via the Ndarray.

seed

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

.verbose

Logical. Controls verbosity. Defaults to TRUE.

Value

A ParametricUmapModel object containing the embedding matrix and the trained encoder model.