使用 scikit-learn 对文本进行标记

Question

我有以下代码从一组文件（文件夹名称是类别名称）中提取特征进行文本分类。

import sklearn.datasets
from sklearn.feature_extraction.text import TfidfVectorizer

train = sklearn.datasets.load_files('./train', description=None, categories=None, load_content=True, shuffle=True, encoding=None, decode_error='strict', random_state=0)
print len(train.data)
print train.target_names

vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(train.data)

它抛出以下堆栈跟踪：

Traceback (most recent call last):
  File "C:\EclipseWorkspace\TextClassifier\main.py", line 16, in <module>
    X_train = vectorizer.fit_transform(train.data)
  File "C:\Python27\lib\site-packages\sklearn\feature_extraction\text.py", line 1285, in fit_transform
    X = super(TfidfVectorizer, self).fit_transform(raw_documents)
  File "C:\Python27\lib\site-packages\sklearn\feature_extraction\text.py", line 804, in fit_transform
    self.fixed_vocabulary_)
  File "C:\Python27\lib\site-packages\sklearn\feature_extraction\text.py", line 739, in _count_vocab
    for feature in analyze(doc):
  File "C:\Python27\lib\site-packages\sklearn\feature_extraction\text.py", line 236, in <lambda>
    tokenize(preprocess(self.decode(doc))), stop_words)
  File "C:\Python27\lib\site-packages\sklearn\feature_extraction\text.py", line 113, in decode
    doc = doc.decode(self.encoding, self.decode_error)
  File "C:\Python27\lib\encodings\utf_8.py", line 16, in decode
    return codecs.utf_8_decode(input, errors, True)
UnicodeDecodeError: 'utf8' codec can't decode byte 0xff in position 32054: invalid start byte

我运行 Python 2.7。我怎样才能让它工作？

编辑： 我刚刚发现这对于具有

utf-8

编码的文件非常有效（我的文件是

ANSI

编码的）。有什么办法可以让

sklearn.datasets.load_files()

与

ANSI

编码一起工作吗？

Answer 1

ANSI 是 UTF-8 的严格子集，因此它应该可以正常工作。但是，从堆栈跟踪来看，您的输入似乎在某处包含字节 0xFF，这不是有效的 ANSI 字符。

Answer 2

我通过将错误设置从“严格”更改为“忽略”来解决问题

vectorizer = CountVectorizer(binary = True, decode_error = u'ignore')
word_tokenizer = vectorizer.build_tokenizer()
doc_terms_list_train = [word_tokenizer(str(doc_str, encoding = 'utf-8', errors = 'ignore')) for doc_str in doc_str_list_train]
doc_train_vec = vectorizer.fit_transform(doc_str_list_train)

这里是countvectorizer function的详细解释