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import numpy as np
import marisa_trie
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.externals import six
class MarisaCountVectorizer(CountVectorizer):
# ``CountVectorizer.fit`` method calls ``fit_transform`` so
# ``fit`` is not provided
def fit_transform(self, raw_documents, y=None):
X = super(MarisaCountVectorizer, self).fit_transform(raw_documents)
X = self._freeze_vocabulary(X)
return X
def _freeze_vocabulary(self, X=None):
if not self.fixed_vocabulary_:
frozen = marisa_trie.Trie(six.iterkeys(self.vocabulary_))
if X is not None:
X = self._reorder_features(X, self.vocabulary_, frozen)
self.vocabulary_ = frozen
self.fixed_vocabulary_ = True
del self.stop_words_
return X
def _reorder_features(self, X, old_vocabulary, new_vocabulary):
map_index = np.empty(len(old_vocabulary), dtype=np.int32)
for term, new_val in six.iteritems(new_vocabulary):
map_index[new_val] = old_vocabulary[term]
return X[:, map_index]
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