def timed_dataload(loader, data, args, embedder, values, labels):
# use separate counter to account for invalid input along the way
counter = 0
for text, sentiment in data:
try:
if counter % 10000 == 0:
print("Loading at {}".format(counter))
# normalize and tokenize if necessary
if args.has_key("normalize"):
text_normalized = data_utils.normalize(text, **args["normalize"])
else:
text_normalized = text
# tokenize
if args.get("load", {}).get("form", None) == "hanzi":
tokens = data_utils.tokenize_hanzi(text_normalized)
elif args.get("load", {}).get("form", None) == "arabic":
text_stripped = loader.twitter_strip(text_normalized)
tokens = loader.tokenize_arabic(text_stripped)
else:
tokens = data_utils.tokenize(text_normalized)
# choose embedding type
vector = None
if args["embed"]["type"] == "concatenated":
vector = embedder.embed_words_into_vectors_concatenated(tokens, **self.args["embed"])
elif args["embed"]["type"] == "averaged":
vector = embedder.embed_words_into_vectors_averaged(tokens)
else:
pass
# data labeled by sentiment score (thread-safe with lock)
if vector is not None:
values.append(vector)
labels.append(sentiment)
counter += 1
except TextTooShortException as e:
pass
def timed_dataload(data, args, values, labels):
# use separate counter to account for invalid input along the way
counter = 0
for text,sentiment in data:
try:
if (counter % 10000 == 0):
print("Loading at {}".format(counter))
# normalize and tokenize if necessary
if args.has_key('normalize'):
text_normalized = data_utils.normalize(text, **args['normalize'])
else:
text_normalized = text
# tokenize
if data_args.get('load', {}).get('form', None) == 'hanzi':
tokens = data_utils.tokenize_hanzi(text_normalized)
else:
tokens = data_utils.tokenize(text_normalized)
# choose embedding type
vector = None
if args['embed']['type'] == 'concatenated':
vector = embedder.embed_words_into_vectors_concatenated(tokens, **self.args['embed'])
elif args['embed']['type'] == 'averaged':
vector = embedder.embed_words_into_vectors_averaged(tokens)
else:
pass
# data labeled by sentiment score (thread-safe with lock)
if vector is not None:
values.append(vector)
labels.append(sentiment)
counter += 1
except TextTooShortException as e:
pass
def run(self):
# process valid entries
if self.data[0]:
# process valid data
text, sentiment = self.data
if text:
try:
# normalize and tokenize if necessary
if self.args.has_key('normalize'):
text_normalized = data_utils.normalize(
text, **self.args['normalize'])
else:
text_normalized = text
# tokenize
tokens = data_utils.tokenize(text_normalized)
# choose embedding type
vector = None
if self.args['embed']['type'] == 'concatenated':
vector = embedder.embed_words_into_vectors_concatenated(
tokens, **self.args['embed'])
elif self.args['embed']['type'] == 'averaged':
vector = embedder.embed_words_into_vectors_averaged(
tokens)
else:
pass
# data labeled by sentiment score (thread-safe with lock)
if vector is not None:
threadLock.acquire()
values.append(vector)
labels.append(sentiment)
threadLock.release()
except TextTooShortException as e:
pass
def run(self):
# process valid entries
if self.data[0]:
# process valid data
text, sentiment = self.data
if text:
try:
# normalize and tokenize if necessary
if self.args.has_key('normalize'):
text_normalized = data_utils.normalize(text, **self.args['normalize'])
else:
text_normalized = text
# tokenize
tokens = data_utils.tokenize(text_normalized)
# choose embedding type
vector = None
if self.args['embed']['type'] == 'concatenated':
vector = embedder.embed_words_into_vectors_concatenated(tokens, **self.args['embed'])
elif self.args['embed']['type'] == 'averaged':
vector = embedder.embed_words_into_vectors_averaged(tokens)
else:
pass
# data labeled by sentiment score (thread-safe with lock)
if vector is not None:
threadLock.acquire()
values.append(vector)
labels.append(sentiment)
threadLock.release()
except TextTooShortException as e:
pass
def normalize_imdb(txt):
return data_utils.normalize(txt, encoding=None)
def normalize_tweet(txt):
return data_utils.normalize(txt, min_length=70, max_length=150)
def normalize_imdb(txt):
return data_utils.normalize(txt, encoding=None)
def normalize_tweet(txt):
return data_utils.normalize(txt, min_length=70, max_length=150)