TypeError in scikit-learn CountVectorizer

Question

我正在尝试使用scikit-learn进行一些文本分析。但是，当我尝试调用CountVectorizer时，会引发错误。示例代码和引发的错误如下所示：

    >>> from sklearn.feature_extraction.text import CountVectorizer
    >>> corpus = [  'This is the first document.', 'This is the second second document.',  'And    the third one.',  'Is this the first document?', ]
    >>> vectorizer = CountVectorizer(min_df=1)
    >>> X = vectorizer.fit_transform(corpus)
    Traceback (most recent call last):
    File "<stdin>", line 1, in <module>
    File "/Library/Python/2.6/site-packages/sklearn/feature_extraction/text.py", line 789, in fit_transform
    vocabulary, X = self._count_vocab(raw_documents, self.fixed_vocabulary)
    File "/Library/Python/2.6/site-packages/sklearn/feature_extraction/text.py", line 716, in _count_vocab
    vocabulary = defaultdict(None)
    TypeError: first argument must be callable

这是我安装中的错误还是其他问题？其他示例工作正常。

Answer 1

总结一下评论中的讨论：这是 Python 2.6.1 中的一个错误，已在最新版本的 Python 2.6 中得到修复（以及后来的 2.7+、3.2+...）。