sklearn : TFIDF Transformer : How to get tf-idf va

问题:

I used sklean for calculating TFIDF values for terms in documents using command as

from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(documents)
from sklearn.feature_extraction.text import TfidfTransformer
tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)
X_train_tf = tf_transformer.transform(X_train_counts)

X_train_tf is scipy sparse matrix of shape

X_train_tf.shape

has output as (2257, 35788). How can I get TF-IDF for words in a perticular document? More specific, how to get words with maximum TF-IDF values in a given document?

回答1:

You can use TfidfVectorizer from sklean

from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from scipy.sparse.csr import csr_matrix #need this if you want to save tfidf_matrix

tf = TfidfVectorizer(input='filename', analyzer='word', ngram_range=(1,6),
                     min_df = 0, stop_words = 'english', sublinear_tf=True)
tfidf_matrix =  tf.fit_transform(corpus)

The above tfidf_matix has the TF-IDF values of all the documents in the corpus. This is a big sparse matrix. Now,

feature_names = tf.get_feature_names()

this gives you the list of all the tokens or n-grams or words. For the first document in your corpus,

doc = 0
feature_index = tfidf_matrix[doc,:].nonzero()[1]
tfidf_scores = zip(feature_index, [tfidf_matrix[doc, x] for x in feature_index])

Lets print them,

for w, s in [(feature_names[i], s) for (i, s) in tfidf_scores]:
  print w, s