python的tfidf算法

Question

我有这个代码来计算文本的相似性与tf-以色列国防军。

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，这段代码接受作为输入的普通字符串，我希望通过删除停止词、词干和标记来准备文档。所以输入将是一个列表。如果我用标记化的文档调用documents = [doc1,doc2]的错误是：

    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'

是否有任何方法更改代码并使其接受列表，或者让我再次将标记化文档更改为字符串？

Answer 1

尝试跳过预处理到小写，并提供您自己的"nop“令牌程序：

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

您还应该检查其他参数，如stop_words，以避免重复预处理。