IMDB_DATA = './datasets/aclImdb/aclImdb'
CHARACTERS_PER_WORD = 15
WORDS_PER_DOCUMENT = 300
PREPEND = False
if __name__ == '__main__':
log('Initializing CharMapper')
cm = CharMapper()
log('Load data from original source')
imdb = ImdbDataHandler(source=IMDB_DATA)
(train_reviews,
train_labels) = imdb.get_data(type=ImdbDataHandler.DATA_TRAIN)
(test_reviews, test_labels) = imdb.get_data(type=ImdbDataHandler.DATA_TEST)
log('Converting to character level representations')
train_global_wvs_reviews = imdb.to_char_level_idx(
train_reviews,
char_container=cm,
chars_per_word=CHARACTERS_PER_WORD,
words_per_document=WORDS_PER_DOCUMENT,
prepend=PREPEND)
test_global_wvs_reviews = imdb.to_char_level_idx(
test_reviews,
char_container=cm,
chars_per_word=CHARACTERS_PER_WORD,
words_per_document=WORDS_PER_DOCUMENT,
from cervantes.language import OneLevelEmbedding
from cervantes.nn.models import RNNClassifier
YELP_FUNNY_TRAIN = '../yelp-dataset/TrainSet_funny_75064'
YELP_FUNNY_DEV = '../yelp-dataset/DevSet_funny_75064'
YELP_FUNNY_TEST = '../yelp-dataset/TestSet_funny_75064'
IMDB_DATA = '../deep-text/datasets/aclImdb/aclImdb'
WV_FILE = '../deep-text/embeddings/wv/glove.42B.300d.120000.txt'
if __name__ == '__main__':
print "Getting data in format texts / labels"
imdb = ImdbDataHandler(source=IMDB_DATA)
(train_reviews, train_labels) = imdb.get_data(type=ImdbDataHandler.DATA_TRAIN)
(test_reviews, test_labels) = imdb.get_data(type=ImdbDataHandler.DATA_TEST)
train_reviews = train_reviews[:5000]
test_reviews = test_reviews[:1000]
train_labels = list(train_labels)[:5000]
test_labels = list(test_labels)[:1000]
#yelp = YelpDataHandler()
#(train_reviews, train_labels, test_reviews, test_labels) = \
# yelp.get_data(YELP_FUNNY_TRAIN, YELP_FUNNY_DEV, YELP_FUNNY_TEST)
print "Building language embeddings. This requires parsing text so it might " \
"be pretty slow "
# Compute text embeddings, containing the processed text tokens together with a vector-to-index
# translation object (the vector box), should be pickled in order to be efficiently used with
# different models. Hence, we can save time once we have precomputed a language embedding
from nlpdatahandlers import ImdbDataHandler
import sys
IMDB_DATA_DEFAULT = '../deep-text/datasets/aclImdb/aclImdb'
if __name__ == '__main__':
if len(sys.argv) > 1 and sys.argv[1] != "":
source = sys.argv[1]
else:
source = IMDB_DATA_DEFAULT
print "Loading data from original source"
imdb = ImdbDataHandler(source=source)
(train_reviews, train_labels) = imdb.get_data(type=ImdbDataHandler.DATA_TRAIN, shuffle=True)
(test_reviews, test_labels) = imdb.get_data(type=ImdbDataHandler.DATA_TEST, shuffle=True)
# Simple bag of words with SGD
sgd = SGDTextClassifier(train_reviews, train_labels,
test_texts=test_reviews, test_labels=test_labels,
compute_features=True)
sgd.grid_search_cv(verbose=5, n_jobs=4)
test_error = sgd.get_test_error()
print "Test error in held out set: " + str(test_error)
print "=" * 20
# Now with bigrams too
sgd = SGDTextClassifier(train_reviews, train_labels, ngram_range=(1,2),
test_texts=test_reviews, test_labels=test_labels,
compute_features=True)
import sys
IMDB_DATA_DEFAULT = '../deep-text/datasets/aclImdb/aclImdb'
if __name__ == '__main__':
if len(sys.argv) > 1 and sys.argv[1] != "":
source = sys.argv[1]
else:
source = IMDB_DATA_DEFAULT
print "Loading data from original source"
imdb = ImdbDataHandler(source=source)
(train_reviews,
train_labels) = imdb.get_data(type=ImdbDataHandler.DATA_TRAIN,
shuffle=True)
(test_reviews, test_labels) = imdb.get_data(type=ImdbDataHandler.DATA_TEST,
shuffle=True)
# Simple bag of words with SGD
sgd = SGDTextClassifier(train_reviews,
train_labels,
test_texts=test_reviews,
test_labels=test_labels,
compute_features=True)
sgd.grid_search_cv(verbose=5, n_jobs=4)
test_error = sgd.get_test_error()
print "Test error in held out set: " + str(test_error)
print "=" * 20
# Now with bigrams too
