Wrapper function to generate UMAP parameters

Usage

params_umap(
  local_connectivity = 1,
  bandwidth = 1e-05,
  mix_weight = 1,
  lr = 1,
  n_epochs = NULL,
  neg_sample_rate = 5L,
  gamma = 1,
  optimiser = c("adam_parallel", "sgd", "adam"),
  init = c("spectral", "pca", "random"),
  randomised = TRUE
)

Arguments

local_connectivity: Numeric. Number of nearest neighbours assumed to be at distance zero. Defaults to 1.0.
bandwidth: Numeric. Convergence tolerance for smooth kNN distance binary search. Defaults to 1e-05.
mix_weight: Numeric. Balance between fuzzy union and directed graph during symmetrisation. Defaults to 1.0.
lr: Numeric. Learning rate. Defaults to 1.0.
n_epochs: Integer or NULL. Number of optimisation epochs. If NULL, resolved downstream based on data size. Defaults to NULL.
neg_sample_rate: Integer. Number of negative samples per positive sample. Defaults to 5L.
gamma: Numeric. Repulsion strength. Defaults to 1.0.
optimiser: String. The optimiser. One of c("adam_parallel", "sgd", "adam"). Defaults to "adam_parallel".
init: String. Embedding initialisation method. One of c("spectral", "pca", "random"). Defaults to "spectral".
randomised: Boolean. Use randomised SVD for PCA initialisation. Defaults to TRUE.

Value

A named list with the following elements:

local_connectivity - Numeric. Number of nearest neighbours assumed to be at distance zero. Defaults to 1.0.
bandwidth - Numeric. Convergence tolerance for smooth kNN distance binary search. Defaults to 1e-05.
mix_weight - Numeric. Balance between fuzzy union and directed graph during symmetrisation. Defaults to 1.0.
lr - Numeric. Learning rate. Defaults to 1.0.
n_epochs - Integer or NULL. Number of optimisation epochs. If NULL, resolved downstream based on data size. Defaults to NULL.
neg_sample_rate - Integer. Number of negative samples per positive sample. Defaults to 5L.
gamma - Numeric. Repulsion strength. Defaults to 1.0.
optimiser - String. The optimiser. One of c("adam_parallel", "sgd", "adam"). Defaults to "adam_parallel".
init - String. Embedding initialisation method. One of c("spectral", "pca", "random"). Defaults to "spectral".
randomised - Boolean. Use randomised SVD for PCA initialisation. Defaults to TRUE.