def get_air_score(self, encoder_states, expected_action, kb):
intent_out = encoder_states[3]
intent_pred = intent_out.max(-1)[1].item()
name_out = encoder_states[4]
name_pred_vec = name_out.max(-1)[1]
name_pred_vec_stop = 0
for i in name_pred_vec:
if i == 0:
break
else:
name_pred_vec_stop += 1
name_pred_vec = name_pred_vec[:name_pred_vec_stop]
name_pred = self._v2t(name_pred_vec)
name_pred = ' '.join([i.capitalize() for i in name_pred.split(' ')])
ticket_attn = encoder_states[2]
ticket_pred = ticket_attn.max(-1)[1].item()
status_pred, flight_pred = intent_to_status(ticket_pred,
kb['reservation'] > 0,
intent_pred)
if flight_pred == 0:
flight_pred = []
else:
flight_pred = [flight_pred - 1 + 1000]
pred = {
'status': STATUS_DICT[status_pred],
'flight': flight_pred,
'name': name_pred,
}
reward, name_score, flight_score, status_score = compute_reward(
action_obj_to_str(pred),
action_obj_to_str(expected_action),
tokenize_kb(kb),
)
pred['reward'] = reward
pred['name_score'] = name_score
pred['flight_score'] = flight_score
pred['status_score'] = status_score
pred['intent'] = INTENT_DICT[intent_pred]
return pred
def score_human_data(flags):
assert flags.true_data and flags.true_kb
scores = []
expanded_kb = expanduser(flags.true_kb)
expanded_data = expanduser(flags.true_data)
f2 = gfile.Open(expanded_kb)
with gfile.Open(expanded_data) as f:
for line in tqdm(f):
a = json.loads(line)
kb_line = f2.readline()
if a['correct_sample'] == False:
pred_action = action_obj_to_str(a['action'])
true_action = action_obj_to_str(a['expected_action'])
kb = tokenize_kb(json.loads(kb_line))
ss = compute_reward(pred_action, true_action, kb)
scores.append(ss)
else:
scores.append([1, 1, 1, 1])
sn = np.array(scores)
# np.mean(sn[:,0]), np.mean(sn[:,1]),np.mean(sn[:,2]),np.mean(sn[:,3])
score = np.mean(sn[:, 0])
print('final score', score)
return {'score': score}
def score_selfplay(flags):
assert flags.true_data and flags.true_kb and flags.pred_data
# check output
all_score = []
bleu_scores = []
with tf.gfile.GFile(flags.pred_data) as f:
with tf.gfile.GFile(flags.true_data) as t:
with tf.gfile.GFile(flags.true_kb) as kb:
for pred_line, true_line, kb_line in tqdm(list(zip(f, t, kb))):
pred_json_obj = json.loads(pred_line)
true_json_obj = json.loads(true_line)
kb = tokenize_kb(json.loads(kb_line))
pred_action = ''
if 'action' not in pred_json_obj:
pred_action = '<unk> <unk> <unk> <unk>'.split(' ')
else:
pred_action = action_obj_to_str(
pred_json_obj['action'])
true_action = action_obj_to_str(
true_json_obj['expected_action'])
score = compute_reward(pred_action, true_action, kb)
all_score.append(score)
pred_raw_text = json_obj_to_tokens(pred_json_obj)
true_raw_text = json_obj_to_tokens(true_json_obj)
b = compute_bleu([[true_raw_text]], [pred_raw_text])
bleu_scores.append(b[0] * 100)
avg_score = np.mean(all_score)
avg_bleu = np.mean(bleu_scores)
print('score=', avg_score)
print('bleu=', avg_bleu)
return {'score': avg_score, 'bleu': avg_bleu}
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--ref_file',
required=True,
type=str,
help='path to reference file')
parser.add_argument('--ref_kb',
required=True,
type=str,
help='path to reference kb file')
parser.add_argument('--output_dir',
required=True,
type=str,
help='path to output dir')
args = parser.parse_args()
print(args)
ref_data = []
ref_score = 0
with open(args.ref_file, 'r') as ref_file:
with open(args.ref_kb, 'r') as kb_file:
for l, kb_line in zip(ref_file, kb_file):
_json = json.loads(l)
kb = tokenize_kb(json.loads(kb_line))
action = action_obj_to_str(_json['action'])
expected_action = action_obj_to_str(_json['expected_action'])
if 'reward' not in _json:
score = compute_reward(action, expected_action, kb)
_json['reward'] = score[0]
ref_score += _json['reward']
ref_data.append(_json)
print('# of reference dialogue: ', len(ref_data))
print('avg ref reward: ', ref_score / len(ref_data))
result_data = []
for s in ref_data:
dia = s['dialogue']
new_s = copy.deepcopy(s)
customer_lines = len([l for l in dia if l.startswith('customer: ')])
agent_lines = len([l for l in dia if l.startswith('agent: ')])
gen_lines = len([l for l in dia if l.startswith('agent_tgt: ')])
assert agent_lines + customer_lines + gen_lines == len(dia)
if not dia[0].startswith('customer: '):
new_s['dialogue'] = ['customer: '] + new_s['dialogue']
if dia[-1].startswith('customer: '):
new_s['dialogue'] = new_s['dialogue'][:-1]
assert len(new_s['dialogue']) % 3 == 0
new_s['ref_customer_response'] = [
l.replace('customer:', '').lstrip().rstrip()
for l in new_s['dialogue'][::3]
]
new_s['ref_agent_response'] = [
l.replace('agent:', '').lstrip().rstrip()
for l in new_s['dialogue'][1::3]
]
new_s['gen_agent_response'] = [
l.replace('agent_tgt:', '').lstrip().rstrip()
for l in new_s['dialogue'][2::3]
]
# print(len(new_s['ref_agent_response']))
result_data.append(new_s)
os.makedirs(args.output_dir, exist_ok=True)
print('saving to :', args.output_dir)
with open(os.path.join(args.output_dir, 'data.json'), 'w') as save_file:
for i, r in enumerate(result_data):
save_file.write(json.dumps(r))
save_file.write('\n')