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sim.py
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sim.py
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import argparse, json, shutil, io, time
import numpy as np
from collections import Counter
agent_map = {'rule-no' : 'nl-rule-no',
'rl-no' : 'simple-rl-no',
'rule-hard' : 'nl-rule-hard',
'rl-hard' : 'simple-rl-hard',
'rule-soft' : 'nl-rule-soft',
'rl-soft' : 'simple-rl-soft',
'e2e-soft' : 'e2e-rl-soft',
}
parser = argparse.ArgumentParser()
parser.add_argument('--agent', dest='agent_type', type=str, default='rule-soft',
help='agent to use (rule-no / rl-no / rule-hard / rl-hard / rule-soft / rl-soft / e2e-soft)')
parser.add_argument('--N', dest='N', type=int, default=5000, help='Number of simulations')
parser.add_argument('--db', dest='db', type=str, default='imdb-M',
help='imdb-(S/M/L/XL) -- This is the KB split to use, e.g. imdb-M')
parser.add_argument('--max_turn', dest='max_turn', default=20, type=int,
help='maximum length of each dialog (default=20, 0=no maximum length)')
parser.add_argument('--err_prob', dest='err_prob', default=0.5, type=float,
help='the probability of the user simulator corrupting a slot value')
parser.add_argument('--dontknow_prob', dest='dontknow_prob', default=0.5, type=float,
help='the probability that user simulator does not know a slot value')
parser.add_argument('--sub_prob', dest='sub_prob', default=0.05, type=float,
help='the probability that user simulator substitutes a slot value')
parser.add_argument('--nlg_temp', dest='nlg_temp', type=float, default=1.,
help='Natural Language Generator softmax temperature (to control noise)')
parser.add_argument('--max_first_turn', dest='max_first_turn', type=int, default=5,
help='Maximum number of slots informed by user in first turn')
parser.add_argument('--model_name', dest='model_name', type=str, default='pretrained',
help='model name to evaluate (This should be the same as what you gave for training). Pass "pretrained" to use pretrained models.')
args = parser.parse_args()
params = vars(args)
params['act_set'] = './data/dia_acts.txt'
params['template_path'] = './data/templates.p'
params['nlg_slots_path'] = './data/nlg_slot_set.txt'
params['nlg_model_path'] = './data/pretrained/lstm_tanh_[1470015675.73]_115_120_0.657.p'
config = importlib.import_module('settings.config_'+params['db'])
agent_params = config.agent_params
dataset_params = config.dataset_params
for k,v in dataset_params[params['db']].iteritems():
params[k] = v
for k,v in agent_params[agent_map[params['agent_type']]].iteritems():
params[k] = v
print 'Dialog Parameters: '
print json.dumps(params, indent=2)
max_turn = params['max_turn']
err_prob = params['err_prob']
dk_prob = params['dontknow_prob']
template_path = params['template_path']
agent_type = agent_map[params['agent_type']]
N = params['N']
save_path = None
datadir = './data/' + params['dataset']
db_full_path = datadir + '/db.txt'
db_inc_path = datadir + '/incomplete_db_%.2f.txt' %params['unk']
dict_path = datadir + '/dicts.json'
slot_path = datadir + '/slot_set.txt'
corpus_path = './data/corpora/' + params['dataset'] + '_corpus.txt'
from deep_dialog.dialog_system import DialogManager, MovieDict, DictReader, Database
from deep_dialog.agents import AgentNLRuleSoft, AgentNLRuleHard, AgentNLRuleNoDB
from deep_dialog.agents import AgentSimpleRLAllAct, AgentSimpleRLAllActHardDB
from deep_dialog.agents import AgentSimpleRLAllActNoDB, AgentE2ERLAllAct
from deep_dialog.usersims import RuleSimulator, TemplateNLG, S2SNLG
from deep_dialog.objects import SlotReader
act_set = DictReader()
act_set.load_dict_from_file(params['act_set'])
slot_set = SlotReader(slot_path)
movie_kb = MovieDict(dict_path)
db_full = Database(db_full_path, movie_kb, name=params['dataset'])
db_inc = Database(db_inc_path, movie_kb, name='incomplete%.2f_'%params['unk']+params['dataset'])
nlg = S2SNLG(template_path, params['nlg_slots_path'], params['nlg_model_path'], params['nlg_temp'])
user_sim = RuleSimulator(movie_kb, act_set, slot_set, None, max_turn, nlg, err_prob, db_full, \
1.-dk_prob, sub_prob=params['sub_prob'], max_first_turn=params['max_first_turn'])
if params['model_name']=='pretrained':
params['model_name'] = 'best_'+agent_type+'_imdb.m'
if agent_type == 'act-rule':
agent = AgentActRule(movie_kb, act_set, slot_set, db_inc,
upd=params['upd'], tr=params['tr'], ts=params['ts'],
frac=params['frac'], max_req=params['max_req'])
elif agent_type == 'simple-rl-soft':
agent = AgentSimpleRLAllAct(movie_kb, act_set, slot_set, db_inc, train=False, _reload=True,
n_hid=params['nhid'], batch=params['batch'], ment=params['ment'],
inputtype=params['input'],
pol_start=params['pol_start'], lr=params['lr'], upd=params['upd'], tr=params['tr'],
ts=params['ts'], frac=params['frac'], max_req=params['max_req'],
name=params['model_name'])
elif agent_type == 'simple-rl-hard':
agent = AgentSimpleRLAllActHardDB(movie_kb, act_set, slot_set, db_inc, train=False,
_reload=True,
n_hid=params['nhid'], batch=params['batch'], ment=params['ment'],
inputtype=params['input'],
pol_start=params['pol_start'], lr=params['lr'], upd=params['upd'],
ts=params['ts'], frac=params['frac'], max_req=params['max_req'],
name=params['model_name'])
elif agent_type == 'simple-rl-no':
agent = AgentSimpleRLAllActNoDB(movie_kb, act_set, slot_set, db_inc, train=False,
_reload=True,
n_hid=params['nhid'], batch=params['batch'], ment=params['ment'],
inputtype=params['input'],
pol_start=params['pol_start'], lr=params['lr'], upd=params['upd'],
ts=params['ts'], frac=params['frac'], max_req=params['max_req'],
name=params['model_name'])
elif agent_type == 'e2e-rl-soft':
agent = AgentE2ERLAllAct(movie_kb, act_set, slot_set, db_inc, corpus_path, train=False,
_reload=True, pol_start=params['pol_start'], sl=params['sl'], rl=params['rl'],
n_hid=params['nhid'], batch=params['batch'], ment=params['ment'], lr=params['lr'],
N=params['featN'],
inputtype=params['input'], tr=params['tr'], ts=params['ts'], frac=params['frac'],
max_req=params['max_req'], upd=params['upd'], name=params['model_name'])
elif agent_type=='nl-rule-hard':
agent = AgentNLRuleHard(movie_kb, act_set, slot_set, db_inc, corpus_path,
ts=params['ts'], frac=params['frac'],
max_req=params['max_req'], upd=params['upd'])
elif agent_type=='nl-rule-soft':
agent = AgentNLRuleSoft(movie_kb, act_set, slot_set, db_inc, corpus_path,
tr=params['tr'], ts=params['ts'], frac=params['frac'],
max_req=params['max_req'], upd=params['upd'])
else:
agent = AgentNLRuleNoDB(movie_kb, act_set, slot_set, db_inc, corpus_path,
ts=params['ts'], frac=params['frac'],
max_req=params['max_req'], upd=params['upd'])
dialog_manager = DialogManager(agent, user_sim, db_full, db_inc, movie_kb, verbose=False)
all_rewards = np.zeros((N,))
all_success = np.zeros((N,))
all_turns = np.zeros((N,))
if save_path is not None: fs = io.open(save_path, 'w')
tst = time.time()
for i in range(N):
current_reward = 0
current_success = False
ua = dialog_manager.initialize_episode()
utt = ua['nl_sentence']
if save_path is not None: fs.write(utt+'\n')
t = 0
while(True):
t += 1
episode_over, reward, ua, sa = dialog_manager.next_turn()
utt = ua['nl_sentence']
if save_path is not None: fs.write(utt+'\n')
current_reward += reward
if episode_over:
if reward > 0:
print ("Successful Dialog! Total reward = {}".format(current_reward))
current_success = True
else:
print ("Failed Dialog! Total reward = {}".format(current_reward))
break
all_rewards[i] = current_reward
all_success[i] = 1 if current_success else 0
all_turns[i] = t
if save_path is not None: fs.close()
time_elapsed = time.time()-tst
nn = np.sqrt(N)
print("Overall: {} times, (mean/std) {} / {} reward, {} / {} success rate, {} / {} turns, {} time elapsed".format(N,
np.mean(all_rewards), np.std(all_rewards)/nn, np.mean(all_success),
np.std(all_success)/nn,
np.mean(all_turns), np.std(all_turns)/nn, time_elapsed))