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python - TidfVectorizer - TypeError：TypeError：预期的字符串或类似字节的对象

问题描述

我正在尝试使 TfidfVectorizer 对象适合视频游戏评论列表，但由于某种原因，我收到了错误消息。

这是我的代码：

from sklearn.feature_extraction.text import TfidfVectorizer

tfidf_vectorizer = TfidfVectorizer(max_features = 50000, use_idf = True, ngram_range=(1,3),
                                   preprocessor = data_preprocessor.preprocess_tokenized_review)

print(train_set_x[0])
%time tfidf_matrix = tfidf_vectorizer.fit_transform(train_set_x)

这是错误消息：

I haven't gotten around to playing the campaign but the multiplayer is solid and pretty fun. Includes Zero Dark Thirty pack, an Online Pass, and the all powerful Battlefield 4 Beta access.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<timed exec> in <module>()

~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in fit_transform(self, raw_documents, y)
   1379             Tf-idf-weighted document-term matrix.
   1380         """
-> 1381         X = super(TfidfVectorizer, self).fit_transform(raw_documents)
   1382         self._tfidf.fit(X)
   1383         # X is already a transformed view of raw_documents so

~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in fit_transform(self, raw_documents, y)
    867 
    868         vocabulary, X = self._count_vocab(raw_documents,
--> 869                                           self.fixed_vocabulary_)
    870 
    871         if self.binary:

~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in _count_vocab(self, raw_documents, fixed_vocab)
    790         for doc in raw_documents:
    791             feature_counter = {}
--> 792             for feature in analyze(doc):
    793                 try:
    794                     feature_idx = vocabulary[feature]

~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in <lambda>(doc)
    264 
    265             return lambda doc: self._word_ngrams(
--> 266                 tokenize(preprocess(self.decode(doc))), stop_words)
    267 
    268         else:

~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in <lambda>(doc)
    239             return self.tokenizer
    240         token_pattern = re.compile(self.token_pattern)
--> 241         return lambda doc: token_pattern.findall(doc)
    242 
    243     def get_stop_words(self):

TypeError: expected string or bytes-like object

请注意，输出的第一部分代表我的视频游戏数据集中的一条评论。如果有人知道发生了什么，我将不胜感激。先感谢您！

标签： pythonscikit-learn

解决方案

我认为这个问题是由data_preprocessor.preprocess_tokenized_review函数（你没有分享）引起的。

证明（使用默认值preprocessor=None）：

In [19]: from sklearn.feature_extraction.text import TfidfVectorizer

In [20]: X = ["I haven't gotten around to playing the campaign but the multiplayer is solid and pretty fun. Includes Zero Dark Thirty pack, an Onlin
    ...: e Pass, and the all powerful Battlefield 4 Beta access."]

In [21]: tfidf_vectorizer = TfidfVectorizer(max_features=50000, use_idf=True, ngram_range=(1,3))

In [22]: r = tfidf_vectorizer.fit_transform(X)

In [25]: r
Out[25]:
<1x84 sparse matrix of type '<class 'numpy.float64'>'
        with 84 stored elements in Compressed Sparse Row format>

所以当我们不为preprocessor参数传递任何值时它工作得很好。

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