问题描述
我在 scikit-learn 中的某些文档中安装了 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 是矢量化语料库，然后
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(项，频率)对的列表.
returns a list of (term, frequency) pairs for each distinct term in the corpus that the CountVectorizer extracted.
(需要小的 asarray + ravel 舞蹈来解决 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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