How to check the cluster details of a given vector in k-means in sklearn

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I am using the following code to cluster my word vectors using k-means clustering algorithm.

from sklearn import cluster
model = word2vec.Word2Vec.load("word2vec_model")
X = model[model.wv.vocab]
clusterer = cluster.KMeans (n_clusters=6)
preds = clusterer.fit_predict(X)
centers = clusterer.cluster_centers_

Given a word in the word2vec vocabulary (e.g., word_vector = model['jeep']) I want to get its cluster ID and cosine distance to its cluster center.

I tried the following approach.

for i,j in enumerate(set(preds)):
    positions = X[np.where(preds == i)]
    print(positions)

However, it returns all the vectors in each cluster ID and not exactly what I am looking for.

I am happy to provide more details if needed.

word2vec scikit-learn k-means cluster-analysis python
2 Answers

After clustering you get the labels_ for all of your input data (in the same order as your input data), i.e. clusterer.labels_[model.wv.vocab['jeep'].index] would give you the cluster to which jeep belongs.

You can calculate the cosine distance with with scipy.spatial.distance.cosine

cluster_index = clusterer.labels_[model.wv.vocab['jeep'].index]
print(distance.cosine(model['jeep'], centers[cluster_index]))
>> 0.6935321390628815

Full code

I don't know how your model looks like but let's use GoogleNews-vectors-negative300.bin.

from gensim.models import KeyedVectors
from sklearn import cluster
from scipy.spatial import distance

model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)

# let's use a subset to accelerate clustering
X = model[model.wv.vocab][:40000]

clusterer = cluster.KMeans (n_clusters=6)
preds = clusterer.fit_predict(X)
centers = clusterer.cluster_centers_

cluster_index = clusterer.labels_[model.wv.vocab['jeep'].index]
print(cluster_index, distance.cosine(model['jeep'], centers[cluster_index]))
user_27763760

Here is my attempt!

from gensim.test.utils import common_texts
from gensim.models import Word2Vec

model = Word2Vec(common_texts, size=100, window=5, min_count=1, workers=4)

from sklearn.cluster import KMeans

clustering_model = KMeans(n_clusters=2)

preds = clustering_model.fit_predict([model.wv.get_vector(w) for w in model.wv.vocab])

To get the prediction for cluster ID

>>> clustering_model.predict([model.wv.get_vector('computer')])
# array([1], dtype=int32)

To get cosine similarity between given word and cluster centers

>>> from sklearn.metrics.pairwise import cosine_similarity
>>> cosine_similarity(clustering_model.cluster_centers_, [model.wv.get_vector('computer')])

# array([[-0.07410881],
       [ 0.34881588]])
user_63476290
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