using silhouette score for each sample of an array with each cluster

[alex80]

I want to use the silhouette index to examine each item in the array (X) with each cluster (0,1,2). I take array (X) as an example but my dataset is far bigger. I tried with this code

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from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score from sklearn.metrics import silhouette_samples import pandas as pd import numpy as np from sklearn_extra.cluster import KMedoids from sklearn.metrics.pairwise import euclidean_distances   X = np.array([0.85142858,0.85566274,0.85364912,0.81536489,0.84929932]) X=X.reshape(-1, 1)   kmedoids = KMedoids(n_clusters=3, random_state=0).fit(X) cluster_labels = kmedoids.predict(X) df = pd.DataFrame({'label': kmedoids.labels_[kmedoids.medoid_indices_],                        'medoid': np.squeeze(X[kmedoids.medoid_indices_]),                        'index': kmedoids.medoid_indices_})   for i in range(len(X)):     print()     print("item", i+1, X[i])     for n_clusters in clusters:         silhouette_samples = silhouette_samples(X[i], n_clusters)         print("For clusters =", n_clusters, " The average silhouette_score is :", silhouette_samples)

the results I looking for, look like (calculating silhouette score for each sample of a dataset with each cluster)

Output:
item 1 [0.85142858]
For clusters = 0  The average silhouette_score is : ???
For clusters = 1  The average silhouette_score is : ??? 
For clusters = 2  The average silhouette_score is : ???

item 2 [0.85566274]
For clusters = 0  The average silhouette_score is : ???
For clusters = 1  The average silhouette_score is : ???
For clusters = 2  The average silhouette_score is : ???

item 3 [0.85364912]
For clusters = 0  The average silhouette_score is : ???
For clusters = 1  The average silhouette_score is : ???
For clusters = 2  The average silhouette_score is : ???

item 4 [0.81536489]
For clusters = 0  The average silhouette_score is : ???
For clusters = 1  The average silhouette_score is : ???
For clusters = 2  The average silhouette_score is : ???

item 5 [0.84929932]
For clusters = 0  The average silhouette_score is : ???
For clusters = 1  The average silhouette_score is : ???
For clusters = 2  The average silhouette_score is : ???

[scidam]

calculating silhouette score for each sample of a dataset with each cluster

You need at least 2 class labels to compute silhouette score (see docs).

You probably want to compute scores for each sample, i.e.

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from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score from sklearn.metrics import silhouette_samples import numpy as np from sklearn.metrics.pairwise import euclidean_distances    X = np.array([0.85142858,0.85566274,0.85364912,0.81536489,0.84929932]) X=X.reshape(-1, 1)    kmedoids = KMeans(n_clusters=3, random_state=0).fit(X) cluster_labels = kmedoids.predict(X)   print(silhouette_samples(X, cluster_labels)) print(silhouette_samples(X, cluster_labels).mean()) print(silhouette_score(X, cluster_labels))