def __init__(self, data_dir, domain=None):
self.sys_da_array = []
self.usr_da_array = []
self.goal = {}
self.booked = {}
self.cur_domain = ''
self.complete_domain = []
from config import MultiWozConfig
cfg = MultiWozConfig(domain)
self.belief_domains = cfg.belief_domains
self.mapping = cfg.mapping
db = DBQuery(data_dir, cfg)
self.dbs = db.dbs
return env
def make_env_agenda(data_dir, config):
env = UserAgenda(data_dir, config)
return env
if __name__ == '__main__':
parser = get_parser()
argv = sys.argv[1:]
args, _ = parser.parse_known_args(argv)
if args.config == 'multiwoz':
print("MultiWoz Config")
config = MultiWozConfig()
elif args.config == 'dstcsgds':
print("DSTC Config")
config = DSTCSGDSConfig(args.data_dir)
else:
raise NotImplementedError(
'Config of the dataset {} not implemented'.format(args.config))
init_logging_handler(args.log_dir)
logging.debug(str(args))
try:
mp = mp.get_context('spawn')
except RuntimeError:
pass
def make_env_agenda():
env = UserAgenda(args.data_dir, config)
return env
if __name__ == '__main__':
parser = get_parser()
argv = sys.argv[1:]
args, _ = parser.parse_known_args(argv)
if not args.load_user:
args.load_user = args.save_dir + '/best'
if args.config == 'multiwoz':
config = MultiWozConfig()
config.s_lambda = args.s_lambda
config.temperature = args.temperature
config.argmax_type = args.argmax_type
config.data_ratio = args.data_ratio
else:
raise NotImplementedError(
'Config of the dataset {} not implemented'.format(args.config))
if args.simulator == 'neural':
usermanager = UserDataManager(args.data_dir, config.data_file)
make_env = make_env_neural
elif args.simulator == 'agenda':
make_env = make_env_agenda
else:
raise NotImplementedError('User simulator {} not implemented'.format(
args.simulator))
env = SystemRule(data_dir, config)
return env
def make_env_agenda(data_dir, config):
env = UserAgenda(data_dir, config)
return env
if __name__ == '__main__':
parser = get_parser()
argv = sys.argv[1:]
args, _ = parser.parse_known_args(argv)
if args.config == 'multiwoz':
config = MultiWozConfig(args.domain)
else:
raise NotImplementedError(
'Config of the dataset {} not implemented'.format(args.config))
init_logging_handler(args.log_dir)
logging.debug(str(args))
try:
mp = mp.get_context('spawn')
except RuntimeError:
pass
if args.pretrain:
logging.debug('pretrain')