我正在尝试使用sklearn的CountVectorizer和给定的词汇表。我的词汇是：
['humanitarian crisis', 'vacations for the anti-cruise crowd', 'school textbook', "b'cruise vacations for the anti-cruise", 'budget deal', "b'public school", 'u.n. announces', 'wrong petrol', 'vacations for the anti-cruise', "b'cruise vacations for the anti-cruise crowd"]
矢量化的输入取自pandas数据帧。我是通过pd.read_csv和encoding='utf8'
29371 b'9 quirky and brilliant paris boutiques'
20525 b'public school textbook filled with muslim bi...
2871 b'congress focuses on averting shutdown, but t...
29902 b'yarmouk siege: u.n. announces trip to syria ...
45596 b'fracking protesters arrested for gluing them...
6266 b'cruise vacations for the anti-cruise crowd'
致电CountVectorizer(vocabulary=vocabulary).fit_transform()后，我得到一个全零的矩阵：
(<6x10 sparse matrix of type '<type 'numpy.int64'>'
with 0 stored elements in Compressed Sparse Row format>, <class 'scipy.sparse.csr.csr_matrix'>)
这是一个问题，因为字符串类型，或者我如何调用CountVectorizer的问题？我不确定如何转换字符串类型;我在python2.7和pandas中尝试了多次对encode和decode的不同调用。任何建议将不胜感激。
答案 0 :(得分：1)
调用CountVectorizer时，请使用“ ngram_range =（min_word_count，max_word_count）”。