Question

我有以下代码段，其中我正在尝试列出术语频率，其中first_text和second_text是.tex个文档：

from sklearn.feature_extraction.text import CountVectorizer
training_documents = (first_text, second_text)  
vectorizer = CountVectorizer()
vectorizer.fit_transform(training_documents)
print "Vocabulary:", vectorizer.vocabulary

当我运行脚本时，我得到以下内容：

File "test.py", line 19, in <module>
    vectorizer.fit_transform(training_documents)
  File "/usr/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 817, in fit_transform
    self.fixed_vocabulary_)
  File "/usr/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 752, in _count_vocab
    for feature in analyze(doc):
  File "/usr/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 238, in <lambda>
    tokenize(preprocess(self.decode(doc))), stop_words)
  File "/usr/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 115, in decode
    doc = doc.decode(self.encoding, self.decode_error)
  File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/encodings/utf_8.py", line 16, in decode
    return codecs.utf_8_decode(input, errors, True)
UnicodeDecodeError: 'utf8' codec can't decode byte 0xa2 in position 200086: invalid start byte

如何解决此问题？

感谢。

Answer 1

如果您可以弄清楚文档的编码是什么（可能是latin-1），您可以使用

将其传递给CountVectorizer

vectorizer = CountVectorizer(encoding='latin-1')

否则，您可以使用

跳过包含有问题字节的标记

vectorizer = CountVectorizer(decode_error='ignore')

scikit-learn CountVectorizer UnicodeDecodeError

1 个答案: