cluster_score.py

from sklearn.cluster import MiniBatchKMeans
from sklearn.metrics import calinski_harabaz_score

num_clusters = range(10, 600, 10)
scores = []

for num_cluster in num_clusters:

    km = MiniBatchKMeans(n_clusters=num_cluster, init_size=max(300, 3*num_cluster)).fit(X)
    labels = km.labels_

    scores.append(calinski_harabaz_score(X, labels))
    
fig, ax = plt.subplots()
ax.plot(num_clusters, scores)

ax.set(xlabel='number of clusters', ylabel='score',
       title='Calinski Harabaz Score vs number of clusters')
ax.grid()