ValueError: Number of labels is 1. Valid values are 2 to n_samples - 1 (inclusive) when using silhouette_score

The error is produced because you have a loop for different number of clusters n. During the first iteration, n_clusters is 1 and this leads to all(km.labels_ == 0)to be True.

In other words, you have only one cluster with label 0 (thus, np.unique(km.labels_) prints array([0], dtype=int32)).

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silhouette_score requires more than 1 cluster labels. This causes the error. The error message is clear.

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Example:

from sklearn import datasets
from sklearn.cluster import KMeans
import numpy as np

iris = datasets.load_iris()
X = iris.data
y = iris.target

km = KMeans(n_clusters=3)
km.fit(X,y)

# check how many unique labels do you have
np.unique(km.labels_)
#array([0, 1, 2], dtype=int32)

We have 3 different clusters/cluster labels.

silhouette_score(X, km.labels_, metric='euclidean')
0.38788915189699597

The function works fine.

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Now, let's cause the error:

km2 = KMeans(n_clusters=1)
km2.fit(X,y)

silhouette_score(X, km2.labels_, metric='euclidean')

ValueError: Number of labels is 1. Valid values are 2 to n_samples - 1 (inclusive)

From the documentation,

Note that Silhouette Coefficient is only defined if number of labels is 2 <= n_labels <= n_samples - 1

So one way to solve this problem is instead of using for k in range(1,15), try to start iteration from k = 2, which is for k in range(2,15). That works for me.