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Default parameters for GPU fast Louvain clustering

params_sc_fast_cluster_gpu.Rd

GPU counterpart to bixverse::params_sc_fast_cluster(). The mini-batch k-means knobs are gone (the GPU k-means is full-batch Lloyd's) and the k-means block comes from the GPU parameters instead. Two knobs the CPU wrapper never exposed, same_weight and multi_level_louvain, are available here. The k-means distance is taken from knn$ann_dist, so the coarsening and the centroid graph agree on the geometry. There is no separate metric argument, and "manhattan" is not supported by the GPU k-means.

Usage

params_sc_fast_cluster_gpu(
  k_means_iter = 50L,
  k_means_init = NULL,
  fixed = TRUE,
  quantise = FALSE,
  same_weight = FALSE,
  full_snn = FALSE,
  pruning = NULL,
  snn_similarity = c("jaccard", "rank"),
  louvain_iters = 10L,
  multi_level_louvain = TRUE,
  knn = list(k = 5L)
)

Arguments

k_means_iter

Integer. Maximum number of k-means iterations. Defaults to 50L.

k_means_init

String or NULL. Initialisation method. One of "random", "parallel" or "plusplus". If NULL, picked on the Rust side based on the number of centroids. Defaults to NULL.

fixed

Boolean. Shall k-means run for a fixed number of iterations, without checking for convergence. Defaults to TRUE.

quantise

Boolean. Shall the data buffer be held at fp16 on the GPU. Halves the buffer and helps when the assignment kernels are memory bound. Defaults to FALSE.

same_weight

Boolean. If TRUE, all kNN edges get weight 1.0. Otherwise edges with a reverse counterpart are double counted. Defaults to FALSE.

full_snn

Boolean. Shall the full shared nearest neighbour graph be generated, including edges between centroids that are not neighbours. Defaults to FALSE.

pruning

Numeric or NULL. Weights below this threshold are set to 0 when generating the sNN graph. If NULL, defaults to 1 / ceiling(k * 0.8). Defaults to NULL.

snn_similarity

String. Jaccard computes the Jaccard index between neighbour sets; rank weights edges by the best combined rank of a shared neighbour. Both are normalised to [0, 1]. One of c("jaccard", "rank"). Defaults to "jaccard".

louvain_iters

Integer. Number of Louvain iterations. Defaults to 10L.

multi_level_louvain

Boolean. Shall multi-level Louvain be applied. Defaults to TRUE.

knn

List. Optional overrides for the kNN parameters applied to the centroids. See bixverse::params_knn_defaults() for the available parameters. Defaults to list(k = 5L).

Value

A named list with the following elements:

  • k_means_iter - Integer. Maximum number of k-means iterations. Defaults to 50L.

  • k_means_init - String or NULL. Initialisation method. One of "random", "parallel" or "plusplus". If NULL, picked on the Rust side based on the number of centroids. Defaults to NULL.

  • fixed - Boolean. Shall k-means run for a fixed number of iterations, without checking for convergence. Defaults to TRUE.

  • quantise - Boolean. Shall the data buffer be held at fp16 on the GPU. Halves the buffer and helps when the assignment kernels are memory bound. Defaults to FALSE.

  • same_weight - Boolean. If TRUE, all kNN edges get weight 1.0. Otherwise edges with a reverse counterpart are double counted. Defaults to FALSE.

  • full_snn - Boolean. Shall the full shared nearest neighbour graph be generated, including edges between centroids that are not neighbours. Defaults to FALSE.

  • pruning - Numeric or NULL. Weights below this threshold are set to 0 when generating the sNN graph. If NULL, defaults to 1 / ceiling(k * 0.8). Defaults to NULL.

  • snn_similarity - String. Jaccard computes the Jaccard index between neighbour sets; rank weights edges by the best combined rank of a shared neighbour. Both are normalised to [0, 1]. One of c("jaccard", "rank"). Defaults to "jaccard".

  • louvain_iters - Integer. Number of Louvain iterations. Defaults to 10L.

  • multi_level_louvain - Boolean. Shall multi-level Louvain be applied. Defaults to TRUE.

  • The elements of the base list, overridden by knn, spliced in at this position.