def __init__(
self,
archive_file=DEFAULT_ARCHIVE_FILE,
model_file='https://tatk-data.s3-ap-northeast-1.amazonaws.com/vhus_simulator_camrest.zip'
):
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
config = json.load(f)
manager = UserDataManager()
voc_goal_size, voc_usr_size, voc_sys_size = manager.get_voc_size()
self.user = VHUS(config, voc_goal_size, voc_usr_size,
voc_sys_size).to(device=DEVICE)
self.goal_gen = GoalGenerator()
self.manager = manager
self.user.eval()
if not os.path.isfile(archive_file):
if not model_file:
raise Exception("No model for VHUS Policy is specified!")
archive_file = cached_path(model_file)
model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'save')
if not os.path.exists(model_dir):
os.mkdir(model_dir)
if not os.path.exists(os.path.join(model_dir, 'best_simulator.mdl')):
archive = zipfile.ZipFile(archive_file, 'r')
archive.extractall(model_dir)
self.load(config['load'])
def __init__(self):
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
config = json.load(f)
manager = UserDataManager()
voc_goal_size, voc_usr_size, voc_sys_size = manager.get_voc_size()
self.user = VHUS(config, voc_goal_size, voc_usr_size,
voc_sys_size).to(device=DEVICE)
self.goal_gen = GoalGenerator()
self.manager = manager
self.user.eval()
self.load(config['load'])
def __init__(self, config):
manager = UserDataManager()
voc_goal_size, voc_usr_size, voc_sys_size = manager.get_voc_size()
self.user = VHUS(config, voc_goal_size, voc_usr_size,
voc_sys_size).to(device=DEVICE)
self.goal_gen = GoalGenerator()
self.manager = manager
self.print_per_batch = config['print_per_batch']
self.save_dir = config['save_dir']
self.save_per_epoch = config['save_per_epoch']
seq_goals, seq_usr_dass, seq_sys_dass = manager.data_loader_seg()
train_goals, train_usrdas, train_sysdas, \
test_goals, test_usrdas, test_sysdas, \
val_goals, val_usrdas, val_sysdas = manager.train_test_val_split_seg(
seq_goals, seq_usr_dass, seq_sys_dass)
self.data_train = (train_goals, train_usrdas, train_sysdas,
config['batchsz'])
self.data_valid = (val_goals, val_usrdas, val_sysdas,
config['batchsz'])
self.data_test = (test_goals, test_usrdas, test_sysdas,
config['batchsz'])
self.alpha = config['alpha']
self.optim = torch.optim.Adam(self.user.parameters(), lr=config['lr'])
self.nll_loss = nn.NLLLoss(ignore_index=0) # PAD=0
self.bce_loss = nn.BCEWithLogitsLoss()
def __init__(self, goal_generator: GoalGenerator, seed=None):
"""
create new Goal by random
Args:
goal_generator (GoalGenerator): Goal Gernerator.
"""
self.domain_goals = goal_generator.get_user_goal(seed)
if 'reqt' in self.domain_goals.keys():
self.domain_goals['reqt'] = {
slot: DEF_VAL_UNK
for slot in self.domain_goals['reqt']
}
def __init__(self,
archive_file=DEFAULT_ARCHIVE_FILE,
model_file='https://tatk-data.s3-ap-northeast-1.amazonaws.com/vhus_simulator_camrest.zip'):
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.json'), 'r') as f:
config = json.load(f)
manager = UserDataManager()
voc_goal_size, voc_usr_size, voc_sys_size = manager.get_voc_size()
self.user = VHUS(config, voc_goal_size, voc_usr_size, voc_sys_size).to(device=DEVICE)
self.goal_gen = GoalGenerator()
self.manager = manager
self.user.eval()
self.load(archive_file, model_file, config['load'])
def __init__(self):
"""
Constructor for User_Policy_Agenda class.
"""
self.max_turn = 40
self.max_initiative = 4
self.goal_generator = GoalGenerator(corpus_path=os.path.join(
os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(
__file__))))), 'data/camrest/CamRest676_v2.json'))
self.__turn = 0
self.goal = None
self.agenda = None
Policy.__init__(self)
def __init__(self, max_goal_num=100, seed=2019):
"""
Constructor for User_Policy_Agenda class.
"""
self.max_turn = 40
self.max_initiative = 4
self.goal_generator = GoalGenerator(corpus_path=os.path.join(
os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(
__file__))))), 'data/camrest/CamRest676_v2.json'))
self.__turn = 0
self.goal = None
self.agenda = None
random.seed(seed)
self.goal_seeds = [random.randint(1, 1e7) for i in range(max_goal_num)]
Policy.__init__(self)
class UserPolicyVHUS(Policy):
def __init__(
self,
archive_file=DEFAULT_ARCHIVE_FILE,
model_file='https://tatk-data.s3-ap-northeast-1.amazonaws.com/vhus_simulator_camrest.zip'
):
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
config = json.load(f)
manager = UserDataManager()
voc_goal_size, voc_usr_size, voc_sys_size = manager.get_voc_size()
self.user = VHUS(config, voc_goal_size, voc_usr_size,
voc_sys_size).to(device=DEVICE)
self.goal_gen = GoalGenerator()
self.manager = manager
self.user.eval()
if not os.path.isfile(archive_file):
if not model_file:
raise Exception("No model for VHUS Policy is specified!")
archive_file = cached_path(model_file)
model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'save')
if not os.path.exists(model_dir):
os.mkdir(model_dir)
if not os.path.exists(os.path.join(model_dir, 'best_simulator.mdl')):
archive = zipfile.ZipFile(archive_file, 'r')
archive.extractall(model_dir)
self.load(config['load'])
def init_session(self):
self.time_step = -1
self.topic = 'NONE'
self.goal = self.goal_gen.get_user_goal()
self.goal_input = torch.LongTensor(
self.manager.get_goal_id(self.manager.usrgoal2seq(self.goal)))
self.goal_len_input = torch.LongTensor([len(self.goal_input)
]).squeeze()
self.sys_da_id_stack = [] # to save sys da history
def predict(self, state):
"""Predict an user act based on state and preorder system action.
Args:
state (tuple):
Dialog state.
Returns:
usr_action (tuple):
User act.
session_over (boolean):
True to terminate session, otherwise session continues.
"""
sys_action = state
sys_seq_turn = self.manager.sysda2seq(
self.manager.ref_data2stand(sys_action), self.goal)
self.sys_da_id_stack += self.manager.get_sysda_id([sys_seq_turn])
sys_seq_len = torch.LongTensor(
[max(len(sen), 1) for sen in self.sys_da_id_stack])
max_sen_len = sys_seq_len.max().item()
sys_seq = torch.LongTensor(padding(self.sys_da_id_stack, max_sen_len))
usr_a, terminal = self.user.select_action(self.goal_input,
self.goal_len_input, sys_seq,
sys_seq_len)
usr_action = self.manager.usrseq2da(self.manager.id2sentence(usr_a),
self.goal)
return usr_action, terminal
def load(self, filename):
user_mdl = os.path.join(os.path.dirname(os.path.abspath(__file__)),
filename + '_simulator.mdl')
if os.path.exists(user_mdl):
self.user.load_state_dict(torch.load(user_mdl))
print('<<user simulator>> loaded checkpoint from file: {}'.format(
user_mdl))
import json
import logging
import sys
root_dir = os.path.dirname(
os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
sys.path.append(root_dir)
from tatk.util.train_util import init_logging_handler
from tatk.task.camrest.goal_generator import GoalGenerator
from tatk.policy.vhus.camrest.usermanager import UserDataManager
from tatk.policy.vhus.train import VHUS_Trainer
if __name__ == '__main__':
with open('config.json', 'r') as f:
cfg = json.load(f)
init_logging_handler(cfg['log_dir'])
manager = UserDataManager()
goal_gen = GoalGenerator()
env = VHUS_Trainer(cfg, manager, goal_gen)
logging.debug('start training')
best = float('inf')
for e in range(cfg['epoch']):
env.imitating(e)
best = env.imit_test(e, best)
class UserPolicyVHUS(Policy):
def __init__(self):
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
config = json.load(f)
manager = UserDataManager()
voc_goal_size, voc_usr_size, voc_sys_size = manager.get_voc_size()
self.user = VHUS(config, voc_goal_size, voc_usr_size,
voc_sys_size).to(device=DEVICE)
self.goal_gen = GoalGenerator()
self.manager = manager
self.user.eval()
self.load(config['load'])
def init_session(self):
self.time_step = -1
self.topic = 'NONE'
self.goal = self.goal_gen.get_user_goal()
self.goal_input = torch.LongTensor(
self.manager.get_goal_id(self.manager.usrgoal2seq(self.goal)))
self.goal_len_input = torch.LongTensor([len(self.goal_input)
]).squeeze()
self.sys_da_id_stack = [] # to save sys da history
def predict(self, state):
"""Predict an user act based on state and preorder system action.
Args:
state (tuple):
Dialog state.
Returns:
usr_action (tuple):
User act.
session_over (boolean):
True to terminate session, otherwise session continues.
"""
sys_action = state['system_action']
sys_seq_turn = self.manager.sysda2seq(
self.manager.ref_data2stand(sys_action), self.goal)
self.sys_da_id_stack += self.manager.get_sysda_id([sys_seq_turn])
sys_seq_len = torch.LongTensor(
[max(len(sen), 1) for sen in self.sys_da_id_stack])
max_sen_len = sys_seq_len.max().item()
sys_seq = torch.LongTensor(padding(self.sys_da_id_stack, max_sen_len))
usr_a, terminal = self.user.select_action(self.goal_input,
self.goal_len_input, sys_seq,
sys_seq_len)
usr_action = self.manager.usrseq2da(self.manager.id2sentence(usr_a),
self.goal)
return usr_action, terminal
def load(self, filename):
user_mdl = os.path.join(os.path.dirname(os.path.abspath(__file__)),
filename + '_simulator.mdl')
if os.path.exists(user_mdl):
self.user.load_state_dict(torch.load(user_mdl))
print('<<user simulator>> loaded checkpoint from file: {}'.format(
user_mdl))