scikit cosine_similarity vs pairwise_distances
<p>What is the difference between Scikit-learn's sklearn.metrics.pairwise.cosine_similarity and sklearn.metrics.pairwise.pairwise_distances(.. metric="cosine")?</p>

<pre><code>from sklearn.feature_extraction.text import TfidfVectorizer

documents = (
    "Macbook Pro 15' Silver Gray with Nvidia GPU",
    "Macbook GPU"    
)

tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(documents)

from sklearn.metrics.pairwise import cosine_similarity
print(cosine_similarity(tfidf_matrix[0:1], tfidf_matrix)[0,1])
</code></pre>

<p>0.37997836</p>

<pre><code>from sklearn.metrics.pairwise import pairwise_distances
print(pairwise_distances(tfidf_matrix[0:1], tfidf_matrix, metric='cosine')[0,1])
</code></pre>

<p>0.62002164</p>

<p>Why are these different?</p>