How do I obtain individual centroids of K mean cluster using nltk (python)

Question

I have used nltk to perform k mean clustering as I would like to change the distance metrics to cosine distance. However, how do I obtain the centroids of all the clusters?

kclusterer = KMeansClusterer(8, distance = nltk.cluster.util.cosine_distance, repeats = 1)
predict = kclusterer.cluster(features, assign_clusters = True)
centroids = kclusterer._centroid
df_clustering['cluster'] = predict
#df_clustering['centroid'] = centroids[df_clustering['cluster'] - 1].tolist()
df_clustering['centroid'] = centroids

I am trying to perform the k mean clustering on a pandas dataframe, and would like to have the coordinates of the centroid of the cluster of each data point to be in the dataframe column 'centroid'.

Thank you in advance!

Answer 1

import pandas as pd
import numpy as np

# created dummy dataframe with 3 feature
df = pd.DataFrame([[1,2,3],[50, 51,52],[2.0,6.0,8.5],[50.11,53.78,52]], columns = ['feature1', 'feature2','feature3'])
print(df)

obj = KMeansClusterer(2, distance = nltk.cluster.util.cosine_distance) #giving number of cluster 2
vectors = [np.array(f) for f in df.values]

df['predicted_cluster'] = obj.cluster(vectors,assign_clusters = True))

print(obj.means())
#OP
[array([50.055, 52.39 , 52.   ]), array([1.5 , 4.  , 5.75])] #which is going to be mean of three feature for 2 cluster, since number of cluster that we passed is 2

 #now if u want the cluster center in pandas dataframe 
 df['centroid'] = df['predicted_cluster'].apply(lambda x: obj.means()[x])