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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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