def main():
parser = argparse.ArgumentParser()
parser.add_argument('--alpha', type=float, default=0.01)
parser.add_argument('--beta', type=float, default=0.0001)
parser.add_argument('--K', type=int, default=100)
parser.add_argument('--batch_size', type=int, default=50)
parser.add_argument('--epsilon', type=float, default=0.01)
parser.add_argument('--tau0', type=float, default=1000)
parser.add_argument('--kappa', type=float, default=0.6)
parser.add_argument('--samples_per_update', type=int, default=200)
parser.add_argument('--num_updates', type=int, default=10**3)
parser.add_argument('--output_dir', type=str, default='.')
parser.add_argument('--tuning', type=int, default=None)
args = parser.parse_args()
step_size_params = (args.epsilon, args.tau0, args.kappa)
D = 317008
# If tuning argument exists tune appropriately
if args.tuning != None:
seed = range(5)
step_size_params = (args.epsilon, args.tau0, \
args.kappa, seed[args.tuning])
random.seed(step_size_params[-1])
vocab = pw.create_vocab('wiki.vocab')
W = len(vocab)
# Data is a list of form [(article name, article word cts)]
data = pw.parse_docs(pw.online_wiki_docs(), vocab)
# Use first 1000 docs as held out test set
test_data = itertools.islice(data, 1000)
(test_names, holdout_train_cts, holdout_test_cts) = zip(*list(test_data))
holdout_train_cts = list(holdout_train_cts)
holdout_train_cts = list(holdout_train_cts)
batched_cts = pw.take_every(args.batch_size, data)
print "Running LD Sampler, h = %g, samples per update = %d" % (
step_size_params[0], args.samples_per_update)
theta0 = np.random.gamma(1, 1, (args.K, W))
ld = rsgld.LDSampler(D, args.K, W, args.alpha, args.beta, theta0,
step_size_params, args.output_dir)
ld_args = (args.num_updates, args.samples_per_update, batched_cts,
holdout_train_cts, holdout_test_cts)
ld.run_online(*ld_args)
if args.tuning != None:
np.savetxt('./cluster/tuning/perplex_rsgld{0}'.format(args.tuning),
np.array(ld.ho_log_preds))
else:
np.savetxt('output/perplex_rsgld{0}'.format(args.tuning),
np.array(ld.ho_log_preds))
def test_full_set():
V = len(pw.create_vocab('wiki.vocab'))
for i in xrange(20, 21):
a = list(load_list('./corpus/stored%i.pkl' % i))
try:
(names, train_cts) = zip(*a)
except:
(names, train_cts, test_cts) = zip(*a)
train_cts = list(train_cts)
num_words = []
for cnt in train_cts:
num_words += [sum(cnt.values())]
# num_words = [sum(cnt.values) for cnt in train_cts]
print sum(num_words[:])
def main():
parser = argparse.ArgumentParser()
parser.add_argument('alpha', type=float, default=0.01)
parser.add_argument('beta', type=float, default=0.0001)
parser.add_argument('K', type=int, default=50)
parser.add_argument('batch_size', type=int, default=50)
parser.add_argument('epsilon', type=float, default=0.01)
parser.add_argument('tau0', type=float, default=1000)
parser.add_argument('kappa', type=float, default=0.55)
parser.add_argument('samples_per_update', type=int, default=50)
parser.add_argument('num_updates', type=int, default=1000)
parser.add_argument('output_dir', type=str, default='.')
args = parser.parse_args()
step_size_params = (args.epsilon, args.tau0, args.kappa)
D = 3.3e6 # Approx number of documents in wikipedia
vocab = pw.create_vocab('wiki.vocab')
W = len(vocab)
# Data is a list of form [(article name, article word cts)]
data = pw.parse_docs(pw.online_wiki_docs(), vocab)
# Can save data to a file like this
#pw.dump_list(itertools.islice(data, 1000), 'stored.pkl')
# And load like this
#data = pw.load_list('stored.pkl')
# Use first 1000 docs as held out test set
test_data = itertools.islice(data, 1000)
(test_names, holdout_train_cts, holdout_test_cts) = zip(*list(test_data))
holdout_train_cts = list(holdout_train_cts)
holdout_train_cts = list(holdout_train_cts)
batched_cts = pw.take_every(args.batch_size, data)
print "Running LD Sampler, epsilon = %g, samples per update = %d" % (
args.epsilon, args.samples_per_update)
theta0 = np.random.gamma(1, 1, (args.K, W))
ld_func = samplers.LDSampler(D, args.K, W, args.alpha, args.beta, theta0,
step_size_params, args.output_dir).run_online
ld_args = (args.num_updates, args.samples_per_update, batched_cts,
holdout_train_cts, holdout_test_cts)
ld_func(*ld_args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('alpha', type=float, default=0.01)
parser.add_argument('beta', type=float, default=0.0001)
parser.add_argument('K', type=int, default=50)
parser.add_argument('batch_size', type=int, default=50)
parser.add_argument('epsilon', type=float, default=0.01)
parser.add_argument('tau0', type=float, default=1000)
parser.add_argument('kappa', type=float, default=0.55)
parser.add_argument('samples_per_update', type=int, default=50)
parser.add_argument('num_updates', type=int, default=1000)
parser.add_argument('output_dir', type=str,default='.')
args = parser.parse_args()
step_size_params = (args.epsilon, args.tau0, args.kappa)
D = 3.3e6 # Approx number of documents in wikipedia
vocab = pw.create_vocab('wiki.vocab')
W = len(vocab)
# Data is a list of form [(article name, article word cts)]
data = pw.parse_docs(pw.online_wiki_docs(), vocab)
# Can save data to a file like this
#pw.dump_list(itertools.islice(data, 1000), 'stored.pkl')
# And load like this
#data = pw.load_list('stored.pkl')
# Use first 1000 docs as held out test set
test_data = itertools.islice(data, 1000)
(test_names, holdout_train_cts, holdout_test_cts) = zip(*list(test_data))
holdout_train_cts = list(holdout_train_cts)
holdout_train_cts = list(holdout_train_cts)
batched_cts = pw.take_every(args.batch_size, data)
print "Running LD Sampler, epsilon = %g, samples per update = %d" % (args.epsilon,
args.samples_per_update)
theta0 = np.random.gamma(1,1,(args.K,W))
ld_func = samplers.LDSampler(D, args.K, W, args.alpha, args.beta, theta0,
step_size_params, args.output_dir).run_online
ld_args = (args.num_updates, args.samples_per_update, batched_cts,
holdout_train_cts, holdout_test_cts)
ld_func(*ld_args)
