In the sklearn library, there are many other functions you can use, to find cosine similarities between documents. You can directly use TfidfVectorizer in the sklearn’s feature_extraction.text class to Vectorize the words. It will calculate TF_IDF normalization and row-wise euclidean normalization.
>>> from sklearn.feature_extraction.text import TfidfVectorizer
>>> from sklearn.datasets import fetch_20newsgroups
>>> twenty = fetch_20newsgroups()
>>> tfidf = TfidfVectorizer().fit_transform(twenty.data)
<11314x130088 sparse matrix of type '<type 'numpy.float64'>'
with 1787553 stored elements in Compressed Sparse Row format>
After TFIDF-Vectorization, you need to slice the matrix row-wise to get a submatrix with a single row:
<1x130088 sparse matrix of type '<type 'numpy.float64'>'
with 89 stored elements in Compressed Sparse Row format>
In sklearn, we can perform the dot product of the vector by using a linear kernel.
>>> from sklearn.metrics.pairwise import linear_kernel
>>> cosine_similarities = linear_kernel(tfidf[0:1], tfidf).flatten()
array([ 1. , 0.04405952, 0.11016969, ..., 0.04433602,
To find the most related documents, we can use cosine_similarites.argsort() to get the most related document similarities values.
>>> related_docs_indices = cosine_similarities.argsort()[:-5:-1]
array([ 0, 958, 10576, 3277])
array([ 1. , 0.54967926, 0.32902194, 0.2825788 ])
This output shows the cosine values of the top 5 most related documents stored in the array.
>>> print twenty.data
From: [email protected] (where's my thing)
Subject: WHAT car is this!?
Organization: University of Maryland, College Park
I was wondering if anyone out there could enlighten me on this car I saw
the other day. It was a 2-door sports car, looked to be from the late 60s/
early 70s. It was called a Bricklin. The doors were really small. In addition,
the front bumper was separate from the rest of the body. This is
all I know. If anyone can tellme a model name, engine specs, years
of production, where this car is made, history, or whatever info you
have on this funky looking car, please e-mail.
---- brought to you by your neighborhood Lerxst ----
Hope this answer helps.
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