根据文本语料库中的出现情况列出词汇表中的单词，Scikit-Learn

问题描述

我为scikit-learn中的某些文档安装了CountVectorizer.我想在文本语料库中查看所有术语及其对应的频率，以便选择停用词.例如

I have fitted a CountVectorizer to some documents in scikit-learn. I would like to see all the terms and their corresponding frequency in the text corpus, in order to select stop-words. For example

'and' 123 times, 'to' 100 times, 'for' 90 times, ... and so on

有内置功能吗?

推荐答案

如果cv是您的CountVectorizer，而X是向量化语料库，则

If cv is your CountVectorizer and X is the vectorized corpus, then

zip(cv.get_feature_names(),
    np.asarray(X.sum(axis=0)).ravel())

为CountVectorizer提取的语料库中的每个不同术语返回一个(term, frequency)对的列表.

returns a list of (term, frequency) pairs for each distinct term in the corpus that the CountVectorizer extracted.

(需要一些asarray + ravel小舞来解决scipy.sparse中的一些怪癖.)

(The little asarray + ravel dance is needed to work around some quirks in scipy.sparse.)

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