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prepare_yelp_as_text.py
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"""
prepare-yelp.py
description: prepare the Yelp data for training in DNNs
"""
import cPickle as pickle
import logging
import numpy as np
from nlpdatahandlers import YelpDataHandler
from textclf.wordvectors.glove import GloVeBox
from sklearn.feature_extraction.text import HashingVectorizer
LOGGER_PREFIX = ' %s'
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def log(msg, logger=logger):
logger.info(LOGGER_PREFIX % msg)
YELP_USEFUL_TRAIN = '../yelp-dataset/TrainSet_useful_185292'
YELP_USEFUL_DEV = '../yelp-dataset/DevSet_useful_185292'
YELP_USEFUL_TEST = '../yelp-dataset/TestSet_useful_185292'
YELP_FUNNY_TRAIN = '../yelp-dataset/TrainSet_funny_75064'
YELP_FUNNY_DEV = '../yelp-dataset/DevSet_funny_75064'
YELP_FUNNY_TEST = '../yelp-dataset/TestSet_funny_75064'
YELP_COOL_TRAIN = '../yelp-dataset/TrainSet_cool_88698'
YELP_COOL_DEV = '../yelp-dataset/DevSet_cool_88698'
YELP_COOL_TEST = '../yelp-dataset/TestSet_cool_88698'
GLOBAL_WV_FILE = './embeddings/wv/glove.42B.300d.120000.txt'
YELP_WV_FILE = './embeddings/wv/Yelp-GloVe-300dim.txt'
WORDS_PER_TEXT = 300
BOW_HASH_DIMENSION = 2500
if __name__ == '__main__':
log('Building global word vectors from {}'.format(GLOBAL_WV_FILE))
global_gb = GloVeBox(GLOBAL_WV_FILE)
global_gb.build(zero_token=True, normalize_variance=False, normalize_norm=True)
log('writing GloVeBox pickle...')
pickle.dump(global_gb, open(GLOBAL_WV_FILE.replace('.txt', '-glovebox.pkl'), 'wb'), pickle.HIGHEST_PROTOCOL)
log('Building global word vectors from {}'.format(YELP_WV_FILE))
yelp_gb = GloVeBox(YELP_WV_FILE)
yelp_gb.build(zero_token=True, normalize_variance=False, normalize_norm=True)
log('writing GloVeBox pickle...')
pickle.dump(yelp_gb, open(YELP_WV_FILE.replace('.txt', '-glovebox.pkl'), 'wb'), pickle.HIGHEST_PROTOCOL)
yelp = YelpDataHandler()
##################################
### YELP USEFUL
##################################
log('Saving "useful" votes data')
(train_reviews, train_labels, test_reviews, test_labels) = \
yelp.get_data(YELP_USEFUL_TRAIN, YELP_USEFUL_DEV, YELP_USEFUL_TEST)
reviews_wvs_train = yelp.to_word_level_idx(train_reviews, global_gb, WORDS_PER_TEXT)
# -- training data save
np.save('Yelp_useful_train_fulltext_glove_300_X.npy', reviews_wvs_train)
np.save('Yelp_useful_train_fulltext_glove_300_y.npy', train_labels)
reviews_wvs_train = yelp.to_word_level_idx(train_reviews, yelp_gb, WORDS_PER_TEXT)
# -- training data save
np.save('Yelp_useful_train_fulltext_Yelp_glove_300_X.npy', reviews_wvs_train)
np.save('Yelp_useful_train_fulltext_Yelp_glove_300_y.npy', train_labels)
del reviews_wvs_train
reviews_wvs_test = yelp.to_word_level_idx(test_reviews, global_gb, WORDS_PER_TEXT)
# -- testing data save
np.save('Yelp_useful_test_fulltext_glove_300_X.npy', reviews_wvs_test)
np.save('Yelp_useful_test_fulltext_glove_300_y.npy', test_labels)
reviews_wvs_test = yelp.to_word_level_idx(test_reviews, yelp_gb, WORDS_PER_TEXT)
# -- testing data save
np.save('Yelp_useful_test_fulltext_Yelp_glove_300_X.npy', reviews_wvs_test)
np.save('Yelp_useful_test_fulltext_Yelp_glove_300_y.npy', test_labels)
del reviews_wvs_test
log('Hashing BOW features, might be used by some NN models')
hv = HashingVectorizer(n_features=BOW_HASH_DIMENSION) # Int: maybe try without normalization
train_bow_hash = hv.transform(train_reviews)
test_bow_hash = hv.transform(test_reviews)
np.save('Yelp_useful_train_hashbow.npy', train_bow_hash.todense())
np.save('Yelp_useful_test_hashbow.npy', test_bow_hash.todense())
##################################
### YELP FUNNY
##################################
log('Saving "funny" votes data')
(train_reviews, train_labels, test_reviews, test_labels) = \
yelp.get_data(YELP_FUNNY_TRAIN, YELP_FUNNY_DEV, YELP_FUNNY_TEST)
reviews_wvs_train = yelp.to_word_level_idx(train_reviews, global_gb, WORDS_PER_TEXT)
# -- training data save
np.save('Yelp_funny_train_fulltext_glove_300_X.npy', reviews_wvs_train)
np.save('Yelp_funny_train_fulltext_glove_300_y.npy', train_labels)
reviews_wvs_train = yelp.to_word_level_idx(train_reviews, yelp_gb, WORDS_PER_TEXT)
# -- training data save
np.save('Yelp_funny_train_fulltext_Yelp_glove_300_X.npy', reviews_wvs_train)
np.save('Yelp_funny_train_fulltext_Yelp_glove_300_y.npy', train_labels)
del reviews_wvs_train
reviews_wvs_test = yelp.to_word_level_idx(test_reviews, global_gb, WORDS_PER_TEXT)
# -- testing data save
np.save('Yelp_funny_test_fulltext_glove_300_X.npy', reviews_wvs_test)
np.save('Yelp_funny_test_fulltext_glove_300_y.npy', test_labels)
reviews_wvs_test = yelp.to_word_level_idx(test_reviews, yelp_gb, WORDS_PER_TEXT)
# -- testing data save
np.save('Yelp_funny_test_fulltext_Yelp_glove_300_X.npy', reviews_wvs_test)
np.save('Yelp_funny_test_fulltext_Yelp_glove_300_y.npy', test_labels)
del reviews_wvs_test
log('Hashing BOW features, might be used by some NN models')
hv = HashingVectorizer(n_features=BOW_HASH_DIMENSION) # Int: maybe try without normalization
train_bow_hash = hv.transform(train_reviews)
test_bow_hash = hv.transform(test_reviews)
np.save('Yelp_funny_train_hashbow.npy', train_bow_hash.todense())
np.save('Yelp_funny_test_hashbow.npy', test_bow_hash.todense())
##################################
### YELP COOL
##################################
log('Saving "cool" votes data')
(train_reviews, train_labels, test_reviews, test_labels) = \
yelp.get_data(YELP_COOL_TRAIN, YELP_COOL_DEV, YELP_COOL_TEST)
reviews_wvs_train = yelp.to_word_level_idx(train_reviews, global_gb, WORDS_PER_TEXT)
# -- training data save
np.save('Yelp_cool_train_fulltext_glove_300_X.npy', reviews_wvs_train)
np.save('Yelp_cool_train_fulltext_glove_300_y.npy', train_labels)
reviews_wvs_train = yelp.to_word_level_idx(train_reviews, yelp_gb, WORDS_PER_TEXT)
# -- training data save
np.save('Yelp_cool_train_fulltext_Yelp_glove_300_X.npy', reviews_wvs_train)
np.save('Yelp_cool_train_fulltext_Yelp_glove_300_y.npy', train_labels)
del reviews_wvs_train
reviews_wvs_test = yelp.to_word_level_idx(test_reviews, global_gb, WORDS_PER_TEXT)
# -- testing data save
np.save('Yelp_cool_test_fulltext_glove_300_X.npy', reviews_wvs_test)
np.save('Yelp_cool_test_fulltext_glove_300_y.npy', test_labels)
reviews_wvs_test = yelp.to_word_level_idx(test_reviews, yelp_gb, WORDS_PER_TEXT)
# -- testing data save
np.save('Yelp_cool_test_fulltext_Yelp_glove_300_X.npy', reviews_wvs_test)
np.save('Yelp_cool_test_fulltext_Yelp_glove_300_y.npy', test_labels)
del reviews_wvs_test
log('Hashing BOW features, might be used by some NN models')
hv = HashingVectorizer(n_features=BOW_HASH_DIMENSION) # Int: maybe try without normalization
train_bow_hash = hv.transform(train_reviews)
test_bow_hash = hv.transform(test_reviews)
np.save('Yelp_cool_train_hashbow.npy', train_bow_hash.todense())
np.save('Yelp_cool_test_hashbow.npy', test_bow_hash.todense())