scib_rapids.utils.silhouette_samples

scib_rapids.utils.silhouette_samples#

scib_rapids.utils.silhouette_samples(X, labels, chunk_size=256, metric='euclidean', between_cluster_distances='nearest')[source]#

Compute the Silhouette Coefficient for each observation using CuPy.

Parameters:

  • X (ndarray) – Array of shape (n_cells, n_features).

  • labels (ndarray) – Array of shape (n_cells,) representing label values.

  • chunk_size (int (default: 256)) – Number of samples to process at a time for distance computation.

  • metric (Literal['euclidean', 'cosine'] (default: 'euclidean')) – The distance metric: ‘euclidean’ (default) or ‘cosine’.

  • between_cluster_distances (Literal['nearest', 'mean_other', 'furthest'] (default: 'nearest')) – Method for computing inter-cluster distances: ‘nearest’, ‘mean_other’, or ‘furthest’.

Return type:

ndarray

Returns:

silhouette scores array of shape (n_cells,)