python之TfidfVectorizer : ValueError: not a built-in stop list: russian

我尝试将 TfidfVectorizer 与俄语停用词一起应用

Tfidf = sklearn.feature_extraction.text.TfidfVectorizer(stop_words='russian' ) 
Z = Tfidf.fit_transform(X) 

我明白了

ValueError: not a built-in stop list: russian 

当我使用正确的英语停用词时

Tfidf = sklearn.feature_extraction.text.TfidfVectorizer(stop_words='english' ) 
Z = Tfidf.fit_transform(X) 

如何改进？ 完整追溯

<ipython-input-118-e787bf15d612> in <module>() 
      1 Tfidf = sklearn.feature_extraction.text.TfidfVectorizer(stop_words='russian' ) 
----> 2 Z = Tfidf.fit_transform(X) 
 
C:\Program Files\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in fit_transform(self, raw_documents, y) 
   1303             Tf-idf-weighted document-term matrix. 
   1304         """ 
-> 1305         X = super(TfidfVectorizer, self).fit_transform(raw_documents) 
   1306         self._tfidf.fit(X) 
   1307         # X is already a transformed view of raw_documents so 
 
C:\Program Files\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in fit_transform(self, raw_documents, y) 
    815  
    816         vocabulary, X = self._count_vocab(raw_documents, 
--> 817                                           self.fixed_vocabulary_) 
    818  
    819         if self.binary: 
 
C:\Program Files\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in _count_vocab(self, raw_documents, fixed_vocab) 
    745             vocabulary.default_factory = vocabulary.__len__ 
    746  
--> 747         analyze = self.build_analyzer() 
    748         j_indices = _make_int_array() 
    749         indptr = _make_int_array() 
 
C:\Program Files\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in build_analyzer(self) 
    232  
    233         elif self.analyzer == 'word': 
--> 234             stop_words = self.get_stop_words() 
    235             tokenize = self.build_tokenizer() 
    236  
 
C:\Program Files\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in get_stop_words(self) 
    215     def get_stop_words(self): 
    216         """Build or fetch the effective stop words list""" 
--> 217         return _check_stop_list(self.stop_words) 
    218  
    219     def build_analyzer(self): 
 
C:\Program Files\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in _check_stop_list(stop) 
     88         return ENGLISH_STOP_WORDS 
     89     elif isinstance(stop, six.string_types): 
---> 90         raise ValueError("not a built-in stop list: %s" % stop) 
     91     elif stop is None: 
     92         return None 
 
ValueError: not a built-in stop list: russian 

请您参考如下方法:

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stop_words : string {‘english’}, list, or None (default)

If a string, it is passed to _check_stop_list and the appropriate stop list is returned. ‘english’ is currently the only supported string value.

If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if analyzer == 'word'.

If None, no stop words will be used. max_df can be set to a value in the range [0.7, 1.0) to automatically detect and filter stop words based on intra corpus document frequency of terms.