sklearn:TFIDF 转换器:如何获取文档中给定单词的 tf-idf 值

问题描述

我使用 sklearn 为使用命令计算文档的 TFIDF(词频逆文档频率)值:

I used sklearn for calculating TFIDF (Term frequency inverse document frequency) values for 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 是 (2257, 35788) 形状的 scipy.sparse 矩阵.

X_train_tf is a scipy.sparse matrix of shape (2257, 35788).

如何为特定文档中的单词获取 TF-IDF?更具体地说，如何获取给定文档中具有最大 TF-IDF 值的单词?

How can I get TF-IDF for words in a particular document? More specific, how to get words with maximum TF-IDF values in a given document?

推荐答案

你可以使用 sklean 的 TfidfVectorizer

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)

上面的tfidf_matix有语料库中所有文档的TF-IDF值.这是一个很大的稀疏矩阵.现在，

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()

这为您提供了所有标记或 n-gram 或单词的列表.对于语料库中的第一个文档，

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])

让我们打印它们，

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

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