BAITS.st.tl.ClusterAutoK

BAITS.st.tl.ClusterAutoK#

class BAITS.st.tl.ClusterAutoK(n_clusters, max_runs=5, convergence_tol=0.01, model_class=None)#

Identify the best candidates for the number of clusters.

__init__(n_clusters, max_runs=5, convergence_tol=0.01, model_class=None)#

Methods

__init__(n_clusters[, max_runs, ...])

fit(adata[, use_rep, verbose])

Fit the clustering model with a range of cluster numbers and calculate silhouette scores.

predict(adata[, use_rep, k, store_labels, ...])

Predict cluster labels for the data in the given representation and optionally store the labels in adata.obs.

Attributes

best_k

The number of clusters with the highest silhouette_scores.

silhouette_scores

Contents