sklearn agglomerative clustering: dynamically updating the number of clusters

Question

The documentation for sklearn.cluster.AgglomerativeClustering mentions that,

when varying the number of clusters and using caching, it may be advant

Answer 1

You set a cacheing directory with the paramater memory = 'mycachedir' and then if you set compute_full_tree=True, when you rerun fit with different values of n_clusters, it will used the cached tree rather than recomputing each time. To give you an example of how to do this with sklearn's gridsearch API:

from sklearn.cluster import AgglomerativeClustering
from sklearn.grid_search import GridSearchCV

ac = AgglomerativeClustering(memory='mycachedir', 
                             compute_full_tree=True)
classifier = GridSearchCV(ac, 
                          {n_clusters: range(2,6)}, 
                          scoring = 'adjusted_rand_score', 
                          n_jobs=-1, verbose=2)
classifier.fit(X,y)

Answer 2

I know it's an old question, however the solution below might turn out helpful

# scores = input matrix

from scipy.cluster.hierarchy import linkage
from scipy.cluster.hierarchy import cut_tree
from sklearn.metrics import silhouette_score
from sklearn.metrics.pairwise import euclidean_distances

linkage_mat = linkage(scores, method="ward")
euc_scores = euclidean_distances(scores)

n_l = 2
n_h = scores.shape[0]

silh_score = -2
# Selecting the best number of clusters based on the silhouette score
for i in range(n_l, n_h):
    local_labels = list(cut_tree(linkage_mat, n_clusters=i).flatten())
    sc = silhouette_score(
        euc_scores,
        metric="precomputed",
        labels=local_labels,
        random_state=42)
    if silh_score < sc:
        silh_score = sc
        labels = local_labels

n_clusters = len(set(labels))
print(f"Optimal number of clusters: {n_clusters}")
print(f"Best silhouette score: {silh_score}")
# ...