TypeError in scikit-学习CountVectorizer

Question

我正在尝试做一些文本分析与科学学习。但是，当我试图调用CountVectorizer时，会引发错误。示例代码和引发的错误如下：

    >>> 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

这是我安装的窃听器还是什么东西？其他的例子也很好。

Answer 1

总结一下注释中的讨论:这是Python2.6.1中的一个bug，它已经修复了Python2.6的最新版本(后来作为2.7+，3.2+.)。