def main():
parser = argparse.ArgumentParser(
description='Bandit Experiment of TS-Gaussian-Sicq-Prior-With-Startup')
# setting of experiment
parser.add_argument('--exp_num',
type=int,
default=1,
help='Number of experiments')
parser.add_argument('--play_num',
type=int,
default=20000,
help='Number Playing arms in each experiment.')
parser.add_argument('--not_run_exp',
action='store_true',
help='Whether to run experiment')
parser.add_argument('--save_log',
action='store_true',
help='Whether to save log')
parser.add_argument('--show_log',
action='store_true',
help='Whether to show log')
parser.add_argument('--summarize_log',
action='store_true',
help='Whether to summarize log')
parser.set_defaults(not_run_exp=False)
parser.set_defaults(save_log=False)
parser.set_defaults(show_log=False)
parser.set_defaults(summarize_log=False)
args = parser.parse_args()
# define arms
arms = [NormalDistributionArm(1.0, 3.0), NormalDistributionArm(0.0, 0.3)]
# define bandit algorithm
algorithm = ThompsonSamplingGaussianSicqPriorWithStartup(
len(arms), args.save_log)
arm_name = ''
for i in range(len(arms)):
arm_name += arms[i].name()
root_folder_name = '../data/' + arm_name + '/' + algorithm.__class__.__name__
core = BanditCore(arms, algorithm, args)
# run experiment
if not args.not_run_exp:
print('----------Run Exp----------')
for i in range(args.exp_num):
print('Run Exp' + str(i))
# define bandit algorithm
folder_name = root_folder_name + '/Exp' + str(i)
core.experiment(folder_name)
print('Finish Exp' + str(i))
print('')
# calculate mean values of log
if args.summarize_log:
print('----------Calc Mean of Log----------')
calc_mean_data(root_folder_name)
def main():
parser = argparse.ArgumentParser(
description='Bandit Experiment of HyperOpt')
# setting of experiment
parser.add_argument('--exp_num',
type=int,
default=1,
help='Number of experiments')
parser.add_argument('--play_num',
type=int,
default=20000,
help='Number Playing arms in each experiment.')
parser.add_argument('--not_run_exp',
action='store_true',
help='Whether to run experiment')
parser.add_argument('--save_log',
action='store_true',
help='Whether to save log')
parser.add_argument('--show_log',
action='store_true',
help='Whether to show log')
parser.add_argument('--summarize_log',
action='store_true',
help='Whether to summarize log')
parser.set_defaults(not_run_exp=False)
parser.set_defaults(save_log=False)
parser.set_defaults(show_log=False)
parser.set_defaults(summarize_log=False)
args = parser.parse_args()
# define arms
arms = [NormalDistributionArm(1.0, 3.0), NormalDistributionArm(0.0, 0.3)]
# define hyper parameters
hyper_params = {'arm': hp.choice('arm', [i for i in range(len(arms))])}
# define bandit algorithm
algorithm = HyperOpt(arms, hyper_params, args.play_num, args.save_log,
args.show_log)
arm_name = ''
for i in range(len(arms)):
arm_name += arms[i].name()
root_folder_name = '../data/' + arm_name + '/' + algorithm.__class__.__name__
# run experiment
if not args.not_run_exp:
print('----------Run Exp----------')
for i in range(args.exp_num):
print('Run Exp' + str(i))
# define bandit algorithm
folder_name = root_folder_name + '/Exp' + str(i)
algorithm.experiment(folder_name)
print('Finish Exp' + str(i))
print('')
# calculate mean values of log
if args.summarize_log:
print('----------Calc Mean of Log----------')
calc_mean_data(root_folder_name)