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import marisa_trie | |
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer | |
# hack to store vocabulary in MARISA Trie | |
class _MarisaVocabularyMixin(object): | |
def fit_transform(self, raw_documents, y=None): | |
super(_MarisaVocabularyMixin, self).fit_transform(raw_documents) | |
self._freeze_vocabulary() | |
return super(_MarisaVocabularyMixin, self).fit_transform(raw_documents, y) | |
def _freeze_vocabulary(self): | |
if not self.fixed_vocabulary_: | |
self.vocabulary_ = marisa_trie.Trie(self.vocabulary_.keys()) | |
self.fixed_vocabulary_ = True | |
del self.stop_words_ | |
class MarisaCountVectorizer(_MarisaVocabularyMixin, CountVectorizer): | |
pass | |
class MarisaTfidfVectorizer(_MarisaVocabularyMixin, TfidfVectorizer): | |
def fit(self, raw_documents, y=None): | |
super(MarisaTfidfVectorizer, self).fit(raw_documents) | |
self._freeze_vocabulary() | |
return super(MarisaTfidfVectorizer, self).fit(raw_documents, y) |
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