如何使用 CountVectorizer 提取 TF？

Question

如何获取 sklearn.feature_extraction.text.CountVectorizer 创建的词汇表中每个术语的词频 (TF) 并将它们放入列表或字典中？

词汇表中key对应的值似乎都是小于我在初始化CountVectorizer时手动设置的max_features的int数，而不是TF——应该是float数。谁能帮帮我？

CV=CountVectorizer(ngram_range(ngram_min_file_opcode,ngram_max_file_opcode), 
                   decode_error="ignore", max_features=max_features_file_re,
                   token_pattern=r'\b\w+\b', min_df=1, max_df=1.0) 
x = CV.fit_transform(x).toarray()

Answer 1

如果您需要浮点值，您可能正在寻找 TFIDF. In that case, use either sklearn.feature_extraction.text.TfidfVectorizer or sklearn.feature_extraction.text.CountVectorizer followed by sklearn.feature_extraction.text.TfidfTransformer、

如果你真的只是想要TF，你仍然可以使用TfidfVectorizer or CountVectorizer followed by TfidfTransformer，只需确保将TfidfVectorizer/Transformer的use_idf参数设置为False 和 norm（规范化）参数到 'l1' 或 'l2'。这使 TF 计数正常化。

来自 SKLearn 文档：

>>> from sklearn.feature_extraction.text import CountVectorizer
>>> corpus = [
...     'This is the first document.',
...     'This document is the second document.',
...     'And this is the third one.',
...     'Is this the first document?',
... ]
>>> vectorizer = CountVectorizer()
>>> X = vectorizer.fit_transform(corpus)
>>> print(vectorizer.get_feature_names())
['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']
>>> print(X.toarray())  
[[0 1 1 1 0 0 1 0 1]
 [0 2 0 1 0 1 1 0 1]
 [1 0 0 1 1 0 1 1 1]
 [0 1 1 1 0 0 1 0 1]]

第[0 1 1 1 0 0 1 0 1]行对应第一个文档。第一个元素对应文档中出现了多少次and，第二个document，第三个first，等等

如何使用 CountVectorizer 提取 TF？

How to extract TF using CountVectorizer?

python

machine-learning

scikit-learn

nlp

tfidfvectorizer