使用sklearn查找文档中特定单词的tf-idf分数

Question

我有一些代码在文档集合上运行基本的TF-IDF矢量化器,返回DXF的稀疏矩阵,其中D是文档数,F是术语数.没问题.

但是如何在文档中找到特定术语的TF-IDF分数？即在术语(在他们的文本表示中)和它们在结果稀疏矩阵中的位置之间是否存在某种字典？

Answer 1

是.请参阅.vocabulary_拟合/转换的TF-IDF矢量图.

In [1]: from sklearn.datasets import fetch_20newsgroups

In [2]: data = fetch_20newsgroups(categories=['rec.autos'])

In [3]: from sklearn.feature_extraction.text import TfidfVectorizer

In [4]: cv = TfidfVectorizer()

In [5]: X = cv.fit_transform(data.data)

In [6]: cv.vocabulary_

它是一种形式的字典:

{word : column index in array}

Answer 2

这是另一种使用CountVectorizerandTfidfTransformer找到Tfidf给定单词分数的解决方案：

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
# our corpus
data = ['I like dog', 'I love cat', 'I interested in cat']

cv = CountVectorizer()

# convert text data into term-frequency matrix
data = cv.fit_transform(data)

tfidf_transformer = TfidfTransformer()

# convert term-frequency matrix into tf-idf
tfidf_matrix = tfidf_transformer.fit_transform(data)

# create dictionary to find a tfidf word each word
word2tfidf = dict(zip(cv.get_feature_names(), tfidf_transformer.idf_))

for word, score in word2tfidf.items():
    print(word, score)

输出：

(u'love', 1.6931471805599454)
(u'like', 1.6931471805599454)
(u'i', 1.0)
(u'dog', 1.6931471805599454)
(u'cat', 1.2876820724517808)
(u'interested', 1.6931471805599454)
(u'in', 1.6931471805599454)