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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

问题是这段代码将纯字符串作为输入，我想通过删除停用词、词干提取和标记化来准备文档。所以输入将是一个列表。如果我documents = [doc1,doc2]使用标记化文档调用 ，则错误是：

    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'

有什么方法可以更改代码并使其接受列表，还是让我再次将标记化的文档更改为字符串？

4

1 回答 1

5

尝试将预处理跳过为小写并提供您自己的“nop”标记器：

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避免重复您的预处理。

于 2013-08-25T23:06:18.060 回答