ease/feature_extractor.py
def gen_bag_feats(self, e_set):
        """
        Generates bag of words features from an input essay set and trained FeatureExtractor
        Generally called by gen_feats
        Returns an array of features
        e_set - EssaySet object
        """
        if(hasattr(self, '_stem_dict')):
            sfeats = self._stem_dict.transform(e_set._clean_stem_text)
            nfeats = self._normal_dict.transform(e_set._text)
            bag_feats = numpy.concatenate((sfeats.toarray(), nfeats.toarray()), axis=1)
        else:
            raise util_functions.InputError(self, "Dictionaries must be initialized prior to generating bag features.")
        return bag_feats.copy()
def gen_bag_feats(self, e_set):
        """
        Generates bag of words features from an input essay set and trained FeatureExtractor
        Generally called by gen_feats
        Returns an array of features
        e_set - EssaySet object
        """
        if(hasattr(self, '_stem_dict')):
            sfeats = self._stem_dict.transform(e_set._clean_stem_text)
            nfeats = self._normal_dict.transform(e_set._text)
            bag_feats = numpy.concatenate((sfeats.toarray(), nfeats.toarray()), axis=1)
        else:
            raise util_functions.InputError(self, "Dictionaries must be initialized prior to generating bag features.")
        return bag_feats.copy()
def gen_bag_feats ( self , e_set ) : if ( hasattr ( self , '_stem_dict' ) ) : sfeats = self . _stem_dict . transform ( e_set . _clean_stem_text ) nfeats = self . _normal_dict . transform ( e_set . _text ) bag_feats = numpy . concatenate ( ( sfeats . toarray ( ) , nfeats . toarray ( ) ) , axis = 1 ) else : raise util_functions . InputError ( self , "Dictionaries must be initialized prior to generating bag features." ) return bag_feats . copy ( )
Generates bag of words features from an input essay set and trained FeatureExtractor
        Generally called by gen_feats
        Returns an array of features
        e_set - EssaySet object
Generates bag of words features from an input essay set and trained FeatureExtractor Generally called by gen_feats Returns an array of features e_set - EssaySet object