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tf_idf_sklearn.py
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tf_idf_sklearn.py
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from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from visual import show_tfidf # this refers to visual.py in my [repo](https://github.com/MorvanZhou/NLP-Tutorials/)
docs = [
"it is a good day, I like to stay here",
"I am happy to be here",
"I am bob",
"it is sunny today",
"I have a party today",
"it is a dog and that is a cat",
"there are dog and cat on the tree",
"I study hard this morning",
"today is a good day",
"tomorrow will be a good day",
"I like coffee, I like book and I like apple",
"I do not like it",
"I am kitty, I like bob",
"I do not care who like bob, but I like kitty",
"It is coffee time, bring your cup",
]
vectorizer = TfidfVectorizer()
tf_idf = vectorizer.fit_transform(docs)
print("idf: ", [(n, idf) for idf, n in zip(vectorizer.idf_, vectorizer.get_feature_names())])
print("v2i: ", vectorizer.vocabulary_)
q = "I get a coffee cup"
qtf_idf = vectorizer.transform([q])
res = cosine_similarity(tf_idf, qtf_idf)
res = res.ravel().argsort()[-3:]
print("\ntop 3 docs for '{}':\n{}".format(q, [docs[i] for i in res[::-1]]))
i2v = {i: v for v, i in vectorizer.vocabulary_.items()}
dense_tfidf = tf_idf.todense()
show_tfidf(dense_tfidf, [i2v[i] for i in range(dense_tfidf.shape[1])], "tfidf_sklearn_matrix")