def main(argv):
try:
long_flags = ["help", "save", "test", "verbose"]
opts, args = getopt.getopt(argv, "hs:tv", long_flags)
except:
usage()
sys.exit(2)
model_name = None
testing = False
verbose = False
for opt, arg in opts:
if opt in ("-h", "--help"):
usage()
sys.exit()
elif opt in ("-s", "--save"):
print('Saving model to %s' % arg)
model_name = arg
elif opt in ("-t", "--test"):
testing = True
elif opt in ("-v", "--verbose"):
verbose = True
output_format = '%(asctime)s : %(levelname)s : %(message)s'
logging.basicConfig(format=output_format, level=logging.INFO)
# Prevents user from running script without saving
# or testing the model
if not (model_name or testing):
logging.critical("Sentiment140_Pipeline script is neither saving or testing the model built")
sys.exit()
logging.info("Opening CSV file...")
all_data = sentiment140.load_data(verbose=verbose)
model = train_d2v_model(all_data, epoch_num=10)
# Saves memory
model.init_sims(replace=True)
if model_name:
model.save(model_name)
if testing:
test_model(model)
def main(argv):
# Initial local path for Stanford Twitter Data Features is None
FEAT_PATH = None
verbose = False
# Parse command line arguments
try:
long_flags = ["help", "bernoulli", "multinomial", "gaussian"]
opts, args = getopt.getopt(argv, "hf:v", long_flags)
except getopt.GetoptError:
usage()
sys.exit(2)
# Classifier variable. Used for training on tweet features below
classifier = NaiveBayesClassifier
for opt, arg in opts:
if opt in ('-h', '--help'):
usage()
sys.exit()
elif opt == '-f':
FEAT_PATH = arg
elif opt == '-v':
verbose = True
output_format = '%(asctime)s : %(levelname)s : %(message)s'
logging.basicConfig(format=output_format, level=logging.INFO)
elif opt in ("bernoulli", "multinomial", "gaussian"):
''' This section allows you to use scikit-learn packages for
text classification.
NLTKs SklearnClassifier makes the process much easier,
since you dont have to convert feature dictionaries to
numpy arrays yourself, or keep track of all known features.
The Scikits classifiers also tend to be more memory efficient
than the standard NLTK classifiers, due to their use of sparse
arrays.
Credit to "Jacob" and his post on Steamhacker.com
'''
pipeline = None
if opt == "bernoulli":
pipeline = Pipeline([('nb', BernoulliNB())])
elif opt == "multinomial":
pipeline = Pipeline([('nb', MultinomialNB())])
elif opt == "gaussian":
pipeline = Pipeline([('nb', GaussianNB())])
classifier = SklearnClassifier(pipeline)
# Perform tweet parsing and learning
logging.info("Opening CSV file...")
logging.info("Extracting Features...")
all_data = list()
# Checks if all_data has already been set
if FEAT_PATH is not None:
tweet_feats = open(FEAT_PATH, 'r')
all_data = [eval(line) for line in tweet_feats]
else:
all_data = sentiment140.load_data(feat_extractor=word_feats,
verbose=verbose)
logging.info("CSV file opened and features extracted")
train_set, dev_set, test_set = split_data(all_data, train=.9,
dev=0, test=.1, shuffle=True)
logging.info("Data split into sets")
classifier = classifier.train(train_set)
logging.info("Classifier trained")
logging.info("Evaluating accuracy and other features\n")
test_model(classifier, test_set)
def main(argv):
# Initial local path for Stanford Twitter Data Features is None
FEAT_PATH = None
verbose = False
# Parse command line arguments
try:
long_flags = ["help", "bernoulli", "multinomial", "gaussian"]
opts, args = getopt.getopt(argv, "hf:v", long_flags)
except getopt.GetoptError:
usage()
sys.exit(2)
# Classifier variable. Used for training on tweet features below
classifier = NaiveBayesClassifier
for opt, arg in opts:
if opt in ('-h', '--help'):
usage()
sys.exit()
elif opt == '-f':
FEAT_PATH = arg
elif opt == '-v':
verbose = True
output_format = '%(asctime)s : %(levelname)s : %(message)s'
logging.basicConfig(format=output_format, level=logging.INFO)
elif opt in ("bernoulli", "multinomial", "gaussian"):
''' This section allows you to use scikit-learn packages for
text classification.
NLTKs SklearnClassifier makes the process much easier,
since you dont have to convert feature dictionaries to
numpy arrays yourself, or keep track of all known features.
The Scikits classifiers also tend to be more memory efficient
than the standard NLTK classifiers, due to their use of sparse
arrays.
Credit to "Jacob" and his post on Steamhacker.com
'''
pipeline = None
if opt == "bernoulli":
pipeline = Pipeline([('nb', BernoulliNB())])
elif opt == "multinomial":
pipeline = Pipeline([('nb', MultinomialNB())])
elif opt == "gaussian":
pipeline = Pipeline([('nb', GaussianNB())])
classifier = SklearnClassifier(pipeline)
# Perform tweet parsing and learning
logging.info("Opening CSV file...")
logging.info("Extracting Features...")
all_data = list()
# Checks if all_data has already been set
if FEAT_PATH is not None:
tweet_feats = open(FEAT_PATH, 'r')
all_data = [eval(line) for line in tweet_feats]
else:
all_data = sentiment140.load_data(feat_extractor=word_feats,
verbose=verbose)
logging.info("CSV file opened and features extracted")
train_set, dev_set, test_set = split_data(all_data,
train=.9,
dev=0,
test=.1,
shuffle=True)
logging.info("Data split into sets")
classifier = classifier.train(train_set)
logging.info("Classifier trained")
logging.info("Evaluating accuracy and other features\n")
test_model(classifier, test_set)
