python - python的tfidf算法

我有这段代码用于计算与tf-idf的文本相似度。

from sklearn.feature_extraction.text import TfidfVectorizer

documents = [doc1,doc2]
tfidf = TfidfVectorizer().fit_transform(documents)
pairwise_similarity = tfidf * tfidf.T
print pairwise_similarity.A

问题是该代码将纯字符串作为输入，我想通过删除停用词，词干和tokkenize来准备文档。因此，输入将是一个列表。如果我用已分类的文档调用documents = [doc1,doc2]，则错误为:

    Traceback (most recent call last):
  File "C:\Users\tasos\Desktop\my thesis\beta\similarity.py", line 18, in <module>
    tfidf = TfidfVectorizer().fit_transform(documents)
  File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\feature_extraction\text.py", line 1219, in fit_transform
    X = super(TfidfVectorizer, self).fit_transform(raw_documents)
  File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\feature_extraction\text.py", line 780, in fit_transform
    vocabulary, X = self._count_vocab(raw_documents, self.fixed_vocabulary)
  File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\feature_extraction\text.py", line 715, in _count_vocab
    for feature in analyze(doc):
  File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\feature_extraction\text.py", line 229, in <lambda>
    tokenize(preprocess(self.decode(doc))), stop_words)
  File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\feature_extraction\text.py", line 195, in <lambda>
    return lambda x: strip_accents(x.lower())
AttributeError: 'unicode' object has no attribute 'apply_freq_filter'

有什么办法可以更改代码并使其接受列表，还是我可以将已分类的文档再次更改为字符串？

最佳答案

尝试跳过预处理为小写形式，并提供自己的“nop” token 生成器:

tfidf = TfidfVectorizer(tokenizer=lambda doc: doc, lowercase=False).fit_transform(documents)

您还应该检查其他参数，例如stop_words，以避免重复您的预处理。

关于python - python的tfidf算法，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/18432289/