Question

我正在进行文件分类，准确率高达76％。在预测文档类别时，我做了一个

doc_clf.predict(tf_idf.transform((count_vect.transform([r'document']))))

我收到以下错误：

File "/usr/local/lib/python3.5/dist- packages/sklearn/utils/metaestimators.py", line 115, in <lambda>
  out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/sklearn/pipeline.py", line 306, in predict
  Xt = transform.transform(Xt)
File "/usr/local/lib/python3.5/dist-packages/sklearn/feature_extraction/text.py", line 923, in transform
  _, X = self._count_vocab(raw_documents, fixed_vocab=True)
File "/usr/local/lib/python3.5/dist-packages/sklearn/feature_extraction/text.py", line 792, in _count_vocab
  for feature in analyze(doc):
File "/usr/local/lib/python3.5/dist-packages/sklearn/feature_extraction/text.py", line 266, in <lambda>
  tokenize(preprocess(self.decode(doc))), stop_words)
File "/usr/local/lib/python3.5/dist-packages/sklearn/feature_extraction/text.py", line 232, in <lambda>
  return lambda x: strip_accents(x.lower())
File "/usr/local/lib/python3.5/dist-packages/scipy/sparse/base.py", line 647, in __getattr__
  raise AttributeError(attr + " not found")

如何更正此错误？还有其他方法可以进一步提高准确度吗？

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Answer 1

在您的代码中，doc_clf是一个管道。因此，tf_idf.transform()和count_vect.transform()将由管道自动处理。

你应该只打电话

category = doc_clf.predict([r'document'])

当此文档通过管道时，它将由CountVectorizer和TfidfTransformer自动转换。

AttributeError：未找到低位

1 个答案: