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DS-1000 / 873 / My goal is to input some queries and find out which query is most similar to a…

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Problem:

My goal is to input some queries and find out which query is most similar to a set of documents.

So far I have calculated the tf-idf of the documents doing the following:

from sklearn.feature_extraction.text import TfidfVectorizer

def get_term_frequency_inverse_data_frequency(documents):
    vectorizer = TfidfVectorizer()
    matrix = vectorizer.fit_transform(documents)
    return matrix

def get_tf_idf_query_similarity(documents, query):
    tfidf = get_term_frequency_inverse_data_frequency(documents)
The problem I am having is now that I have tf-idf of the documents what operations do I perform on the query so I can find the cosine similarity to the documents? The answer should be like a 3*5 matrix of the similarities.

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
queries, documents = load_data()
assert type(queries) == list
assert type(documents) == list
tfidf = TfidfVectorizer()
tfidf.fit_transform(documents)
</code>
cosine_similarities_of_queries = ... # put solution in this variable
BEGIN SOLUTION
<code>

library

Sklearn

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Source and history

upstream_id / Original problem ID
873
task_id
08d44688-fb42-555a-9e21-c9ddd1400001
task_key
default--test--873
task_revision_id
1
config / split
default / test

Official source

initial import