def _build_data(self, root_dir, processed_dir):
self.data = {}
data_loader = ActPolicyDataloader(
dataset_dataloader=MultiWOZDataloader())
for part in ['train', 'val', 'test']:
self.data[part] = []
raw_data = data_loader.load_data(data_key=part, role='sys')[part]
for belief_state, context_dialog_act, terminated, dialog_act in \
zip(raw_data['belief_state'], raw_data['context_dialog_act'], raw_data['terminated'], raw_data['dialog_act']):
state = default_state()
state['belief_state'] = belief_state
state['user_action'] = context_dialog_act[-1]
state['system_action'] = context_dialog_act[-2] if len(
context_dialog_act) > 1 else {}
state['terminated'] = terminated
action = dialog_act
self.data[part].append([
self.vector.state_vectorize(state),
self.vector.action_vectorize(action)
])
os.makedirs(processed_dir)
for part in ['train', 'val', 'test']:
with open(os.path.join(processed_dir, '{}.pkl'.format(part)),
'wb') as f:
pickle.dump(self.data[part], f)
def __init__(self, data_dir='configs', data=None):
"""Constructor of MultiWOzMDBT class.
Args:
data_dir (str): The path of data dir, where the root path is convlab2/dst/mdbt/multiwoz.
"""
if data is None:
loader = AgentDSTDataloader(MultiWOZDataloader())
data = loader.load_data()
self.file_url = 'https://convlab.blob.core.windows.net/convlab-2/mdbt_multiwoz_sys.zip'
local_path = os.path.dirname(os.path.abspath(__file__))
self.data_dir = os.path.join(local_path,
data_dir) # abstract data path
self.validation_url = os.path.join(self.data_dir, 'data/validate.json')
self.training_url = os.path.join(self.data_dir, 'data/train.json')
self.testing_url = os.path.join(self.data_dir, 'data/test.json')
self.word_vectors_url = os.path.join(
self.data_dir, 'word-vectors/paragram_300_sl999.txt')
self.ontology_url = os.path.join(self.data_dir, 'data/ontology.json')
self.model_url = os.path.join(self.data_dir, 'models/model-1')
self.graph_url = os.path.join(self.data_dir, 'graphs/graph-1')
self.results_url = os.path.join(self.data_dir, 'results/log-1.txt')
self.kb_url = os.path.join(self.data_dir, 'data/') # not used
self.train_model_url = os.path.join(self.data_dir,
'train_models/model-1')
self.train_graph_url = os.path.join(self.data_dir,
'train_graph/graph-1')
self.auto_download()
print('Configuring MDBT model...')
self.word_vectors = load_word_vectors(self.word_vectors_url)
# Load the ontology and extract the feature vectors
self.ontology, self.ontology_vectors, self.slots = load_ontology(
self.ontology_url, self.word_vectors)
# Load and process the training data
self.test_dialogues, self.actual_dialogues = load_woz_data_new(
data['test'],
self.word_vectors,
self.ontology,
url=self.testing_url)
self.no_dialogues = len(self.test_dialogues)
super(MultiWozMDBT,
self).__init__(self.ontology_vectors, self.ontology, self.slots,
self.data_dir)
from convlab2.dst.sumbt.multiwoz.sumbt import SUMBTTracker
model = SUMBTTracker()
elif model_name == 'TRADE':
from convlab2.dst.trade.multiwoz.trade import MultiWOZTRADE
model = MultiWOZTRADE()
elif model_name == 'mdbt':
from convlab2.dst.mdbt.multiwoz.dst import MultiWozMDBT
model = MultiWozMDBT()
else:
raise Exception("Available models: TRADE/mdbt/sumbt")
## load data
from convlab2.util.dataloader.module_dataloader import AgentDSTDataloader
from convlab2.util.dataloader.dataset_dataloader import MultiWOZDataloader
dataloader = AgentDSTDataloader(
dataset_dataloader=MultiWOZDataloader(dataset_name.endswith('zh')))
data = dataloader.load_data(data_key=data_key)[data_key]
context, golden_truth = data['context'], data['belief_state']
all_predictions = {}
test_set = []
curr_sess = {}
session_count = 0
turn_count = 0
is_start = True
for i in tqdm(range(len(context))):
if len(context[i]) == 0:
turn_count = 0
if is_start:
is_start = False
else: # save session
all_predictions[session_count] = copy.deepcopy(curr_sess)
def load_data(self, *args, **kwargs):
kwargs.setdefault('belief_state', True)
kwargs.setdefault('utterance', True)
kwargs.setdefault('context', True)
kwargs.setdefault('context_window_size', 3)
return self.dataset_dataloader.load_data(*args, **kwargs)
class SingleTurnNLGDataloader(ModuleDataloader):
def load_data(self, *args, **kwargs):
kwargs.setdefault('utterance', True)
kwargs.setdefault('dialog_act', True)
return self.dataset_dataloader.load_data(*args, **kwargs)
class MultiTurnNLGDataloader(ModuleDataloader):
def load_data(self, *args, **kwargs):
kwargs.setdefault('utterance', True)
kwargs.setdefault('dialog_act', True)
kwargs.setdefault('context', True)
kwargs.setdefault('context_window_size', 3)
return self.dataset_dataloader.load_data(*args, **kwargs)
if __name__ == '__main__':
d = MultiTurnNLUDataloader(dataset_dataloader=MultiWOZDataloader())
data = d.load_data(data_key='val', role='usr')
pprint(data['val']['utterance'][:5])
pprint(data['val']['context'][:5])
pprint(data['val']['dialog_act'][:5])
if model_name == 'TRADE':
from convlab2.dst.trade.multiwoz.trade import MultiWOZTRADE
model = MultiWOZTRADE()
elif model_name == 'mdbt':
from convlab2.dst.mdbt.multiwoz.dst import MultiWozMDBT
model = MultiWozMDBT()
elif model_name == 'sumbt':
from convlab2.dst.sumbt.multiwoz.sumbt import SUMBTTracker
model = SUMBTTracker()
else:
raise Exception("Available models: TRADE/mdbt/sumbt")
## load data
from convlab2.util.dataloader.module_dataloader import AgentDSTDataloader
from convlab2.util.dataloader.dataset_dataloader import MultiWOZDataloader
dataloader = AgentDSTDataloader(dataset_dataloader=MultiWOZDataloader())
data = dataloader.load_data(data_key='test')['test']
context, golden_truth = data['context'], data['belief_state']
all_predictions = {}
test_set = []
curr_sess = {}
session_count = 0
turn_count = 0
is_start = True
for i in tqdm(range(len(context))):
# for i in tqdm(range(200)): # for test
if len(context[i]) == 0:
turn_count = 0
if is_start:
is_start = False
else: # save session
joint_acc_score = joint_acc / float(total) if total != 0 else 0
turn_acc_score = turn_acc / float(total) if total != 0 else 0
F1_score = F1_pred / float(F1_count) if F1_count != 0 else 0
return joint_acc_score, F1_score, turn_acc_score
if __name__ == '__main__':
## init phase
config_data = sys.argv[1]
model = SUMBTTracker(arg_path = config_data)
if dataset_name.startswith('MultiWOZ'):
dataset_name = dataset_name + "zh"
## load data
from convlab2.util.dataloader.module_dataloader import AgentDSTDataloader
from convlab2.util.dataloader.dataset_dataloader import MultiWOZDataloader
dataloader = AgentDSTDataloader(dataset_dataloader=MultiWOZDataloader(config['lang']=='zh')))
data = dataloader.load_data(data_key=data_key)[data_key]
context, golden_truth = data['context'], data['belief_state']
all_predictions = {}
test_set = []
curr_sess = {}
session_count = 0
turn_count = 0
is_start = True
for i in tqdm(range(len(context))):
if len(context[i]) == 0:
turn_count = 0
if is_start:
is_start = False
else: # save session
all_predictions[session_count] = copy.deepcopy(curr_sess)
if model_name == 'sumbt':
from convlab2.dst.sumbt.multiwoz.sum1bt import SUMBTTracker
model = SUMBTTracker()
elif model_name == 'TRADE':
from convlab2.dst.trade.multiwoz.trade import MultiWOZTRADE
model = MultiWOZTRADE()
elif model_name == 'mdbt':
from convlab2.dst.mdbt.multiwoz.dst import MultiWozMDBT
model = MultiWozMDBT()
else:
raise Exception("Available models: TRADE/mdbt/sumbt")
dataset_name = dataset_name + "zh"
## load data
from convlab2.util.dataloader.module_dataloader import AgentDSTDataloader
from convlab2.util.dataloader.dataset_dataloader import MultiWOZDataloader
dataloader = AgentDSTDataloader(dataset_dataloader=MultiWOZDataloader(
not (dataset_name.endswith('zh'))))
data = dataloader.load_data(
data_dir=
'/fs/startiger0/nmoghe/code/ConvLab-2/data/zh_data_en_states',
data_key=data_key)[data_key]
context, golden_truth = data['context'], data['belief_state']
all_predictions = {}
test_set = []
curr_sess = {}
session_count = 0
turn_count = 0
is_start = True
for i in tqdm(range(len(context))):
if len(context[i]) == 0:
turn_count = 0
if is_start:
class WordPolicyDataloader(ModuleDataloader):
def load_data(self, *args, **kwargs):
kwargs.setdefault('belief_state', True)
kwargs.setdefault('utterance', True)
kwargs.setdefault('context', True)
kwargs.setdefault('context_window_size', 3)
return self.dataset_dataloader.load_data(*args, **kwargs)
class SingleTurnNLGDataloader(ModuleDataloader):
def load_data(self, *args, **kwargs):
kwargs.setdefault('utterance', True)
kwargs.setdefault('dialog_act', True)
return self.dataset_dataloader.load_data(*args, **kwargs)
class MultiTurnNLGDataloader(ModuleDataloader):
def load_data(self, *args, **kwargs):
kwargs.setdefault('utterance', True)
kwargs.setdefault('dialog_act', True)
kwargs.setdefault('context', True)
kwargs.setdefault('context_window_size', 3)
return self.dataset_dataloader.load_data(*args, **kwargs)
if __name__ == '__main__':
d = SingleTurnNLUDataloader(dataset_dataloader=MultiWOZDataloader())
data = d.load_data(data_key='val', role='user')
pprint(data['val']['utterance'][:5])
pprint(data['val']['dialog_act'][:5])
model = SCLSTM(is_user=True, use_cuda=True)
elif role == 'sys':
model = SCLSTM(is_user=False, use_cuda=True)
elif model_name == 'TemplateNLG':
from convlab2.nlg.template.multiwoz import TemplateNLG
if role == 'usr':
model = TemplateNLG(is_user=True)
elif role == 'sys':
model = TemplateNLG(is_user=False)
else:
raise Exception("Available models: SCLSTM, TEMPLATE")
from convlab2.util.dataloader.module_dataloader import SingleTurnNLGDataloader
from convlab2.util.dataloader.dataset_dataloader import MultiWOZDataloader
dataloader = SingleTurnNLGDataloader(
dataset_dataloader=MultiWOZDataloader())
data = dataloader.load_data(data_key='test', role=role)['test']
dialog_acts = []
golden_utts = []
gen_utts = []
gen_slots = []
sen_num = 0
for i in tqdm(range(len(data['utterance']))):
dialog_acts.append(data['dialog_act'][i])
golden_utts.append(data['utterance'][i])
gen_utts.append(model.generate(data['dialog_act'][i]))
bleu4 = get_bleu4(dialog_acts, golden_utts, gen_utts)
def test_update():
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
_config = tf.ConfigProto()
_config.gpu_options.allow_growth = True
_config.allow_soft_placement = True
start_time = time.time()
mdbt = MultiWozMDBT()
print('\tMDBT: model build time: {:.2f} seconds'.format(time.time() -
start_time))
mdbt.restore()
# demo state history
mdbt.state['history'] = [
[
'null',
'I\'m trying to find an expensive restaurant in the centre part of town.'
],
[
'The Cambridge Chop House is an good expensive restaurant in the centre of town. Would you like me to book it for you?',
'Yes, a table for 1 at 16:15 on sunday. I need the reference number.'
]
]
new_state = mdbt.update('hi, this is not good')
print(json.dumps(new_state, indent=4))
print('all time: {:.2f} seconds'.format(time.time() - start_time))
if __name__ == '__main__':
loader = AgentDSTDataloader(MultiWOZDataloader())
data = loader.load_data()
model = MultiWozMDBT(data=data)