def experiment(balance_seed, split_num):
logger = initialize_logger(this_script_name, balance_seed, other=split_num)
for pct_usage in np.arange(0.1, 1.1, 0.1):
pct_usage = round(pct_usage, 2)
agent = RnnAgent(device,
logger,
'subj',
25,
num_epochs,
lr,
None,
'dev',
128,
pct_usage=pct_usage,
balance_seed=balance_seed,
split_num=split_num)
agent.run()
for geo in np.arange(0.1, 1.0, 0.1):
geo = round(geo, 2)
agent = RnnAgent(device,
logger,
'subj',
25,
num_epochs,
lr,
aug_mode,
'dev',
128,
pct_usage=pct_usage,
balance_seed=balance_seed,
split_num=split_num,
geo=geo)
agent.run()
def experiment(balance_seed, split_num): logger = initialize_logger( this_script_name, balance_seed, other=split_num) for small_prop in np.arange(0.1, 1.0, 0.1): small_prop = round(small_prop, 2) for small_label in [0, 1]: for undersample in [False, True]: agent = RnnAgent(device, logger, 'subj', 25, num_epochs, lr, None, 'dev', 128, small_label=small_label, small_prop=small_prop, balance_seed=balance_seed, split_num=split_num, undersample=undersample) agent.run() for geo in np.arange(0.1, 1.0, 0.1): geo = round(geo, 2) agent = RnnAgent(device, logger, 'subj', 25, num_epochs, lr, aug_mode, 'dev', 128, small_label=small_label, small_prop=small_prop, balance_seed=balance_seed, split_num=split_num, geo=geo) agent.run()
def experiment(balance_seed, split_num):
logger = initialize_logger(this_script_name, balance_seed, other=split_num)
for small_prop in np.arange(0.2, 1.0, 0.2):
small_prop = round(small_prop, 2)
param_prop_map = param_map[small_prop]
for small_label in [0, 1]:
for undersample in [False, True]:
if undersample:
num_epochs = param_prop_map['under']
else:
num_epochs = param_prop_map['over']
agent = RnnAgent(device,
logger,
'sfu',
input_length,
num_epochs,
lr,
None,
'dev',
128,
small_label=small_label,
small_prop=small_prop,
balance_seed=balance_seed,
split_num=split_num,
undersample=undersample)
agent.run()
geo, num_epochs = param_prop_map['aug']
geo = round(geo, 2)
agent = RnnAgent(device,
logger,
'sfu',
input_length,
num_epochs,
lr,
aug_mode,
'dev',
128,
small_label=small_label,
small_prop=small_prop,
balance_seed=balance_seed,
split_num=split_num,
geo=geo)
agent.run()
def experiment(balance_seed): logger = initialize_logger(this_script_name, balance_seed) for pct_usage in np.arange(0.1, 1.1, 0.1): pct_usage = round(pct_usage, 2) param_pct_map = param_map[pct_usage] geo, num_epochs = param_pct_map['aug'] agent = RnnAgent(device, logger, 'sst', 25, num_epochs, lr, 'synonym', 'test', 128, pct_usage=pct_usage, balance_seed=balance_seed, geo=geo) agent.run() num_epochs = param_pct_map['no'] agent = RnnAgent(device, logger, 'sst', 25, num_epochs, lr, None, 'test', 128, pct_usage=pct_usage, balance_seed=balance_seed) agent.run()
def experiment(balance_seed):
logger = initialize_logger(this_script_name, balance_seed)
for small_prop in np.arange(0.1, 1.0, 0.1):
small_prop = round(small_prop, 2)
param_prop_map = param_map[small_prop]
for small_label in [0, 1]:
geo, num_epochs = param_prop_map['aug']
agent = RnnAgent(device,
logger,
'sst',
25,
num_epochs + 1,
lr,
'synonym',
'test',
128,
small_label=small_label,
small_prop=small_prop,
balance_seed=balance_seed,
geo=geo)
agent.run()
for undersample in [False, True]:
if undersample:
num_epochs = param_prop_map['under']
else:
num_epochs = param_prop_map['over']
agent = RnnAgent(device,
logger,
'sst',
25,
num_epochs + 1,
lr,
None,
'test',
128,
small_label=small_label,
small_prop=small_prop,
balance_seed=balance_seed,
undersample=undersample)
agent.run()
def experiment(balance_seed, split_num): logger = initialize_logger( this_script_name, balance_seed, other=split_num) for pct_usage in np.arange(0.2, 1.1, 0.2): pct_usage = round(pct_usage, 2) param_pct_map = param_map[pct_usage] num_epochs = param_pct_map['no'] agent = RnnAgent(device, logger, 'sfu', input_length, num_epochs, lr, None, 'dev', 128, pct_usage=pct_usage, balance_seed=balance_seed, split_num=split_num) agent.run() geo, num_epochs = param_pct_map['aug'] agent = RnnAgent(device, logger, 'sfu', input_length, num_epochs, lr, aug_mode, 'dev', 128, pct_usage=pct_usage, balance_seed=balance_seed, split_num=split_num, geo=geo) agent.run()
for geo in geos:
agent = BiLSTMAgent(config, pct_usage, frac, geo)
agent.run()
def estimate_optimal_epochs(pct_usage):
'''
Estimate optimal number of epochs for a pct_usage,
using midrange frac and geo parameters
'''
frac = 1 / 4
geo = 0.5
agent = BiLSTMAgent(config, pct_usage, frac, geo)
agent.run()
def grid_search_pcts():
percentages = ([0.02, 0.04, 0.06, 0.08] +
[round(0.1 * i, 2) for i in range(1, 11)])
for pct_usage in percentages:
grid_search(pct_usage)
if __name__ == "__main__":
config = get_config()
initialize_logger()
grid_search(1)
# agent = BiLSTMAgent(config, pct_usage)
# agent.run()