def train(self):
"""
Train the model.
Model saved to
"""
num_hid, bidir, net_type, n2p, batch_size, model_url, graph_url, dev = \
None, True, None, None, None, None, None, None
global train_batch_size, MODEL_URL, GRAPH_URL, device, TRAIN_MODEL_URL, TRAIN_GRAPH_URL
if batch_size:
train_batch_size = batch_size
print("Setting up the batch size to {}.........................".
format(batch_size))
if model_url:
TRAIN_MODEL_URL = model_url
print("Setting up the model url to {}.........................".
format(TRAIN_MODEL_URL))
if graph_url:
TRAIN_GRAPH_URL = graph_url
print("Setting up the graph url to {}.........................".
format(TRAIN_GRAPH_URL))
if dev:
device = dev
print(
"Setting up the device to {}.........................".format(
device))
# 1 Load and process the input data including the ontology
# Load the word embeddings
word_vectors = load_word_vectors(self.word_vectors_url)
# Load the ontology and extract the feature vectors
ontology, ontology_vectors, slots = load_ontology(
self.ontology_url, word_vectors)
# Load and process the training data
dialogues, _ = load_woz_data(self.training_url, word_vectors, ontology)
no_dialogues = len(dialogues)
# Load and process the validation data
val_dialogues, _ = load_woz_data(self.validation_url, word_vectors,
ontology)
# Generate the validation batch data
val_data = generate_batch(val_dialogues, 0, len(val_dialogues),
len(ontology))
val_iterations = int(len(val_dialogues) / train_batch_size)
# 2 Initialise and set up the model graph
# Initialise the model
graph = tf.Graph()
with graph.as_default():
model_variables = model_definition(ontology_vectors,
len(ontology),
slots,
num_hidden=num_hid,
bidir=bidir,
net_type=net_type,
dev=device)
(user, sys_res, no_turns, user_uttr_len, sys_uttr_len, labels,
domain_labels, domain_accuracy, slot_accuracy, value_accuracy,
value_f1, train_step, keep_prob, _, _, _) = model_variables
[precision, recall, value_f1] = value_f1
saver = tf.train.Saver()
if device == 'gpu':
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
else:
config = tf.ConfigProto(device_count={'GPU': 0})
sess = tf.Session(config=config)
if os.path.exists(TRAIN_MODEL_URL + ".index"):
saver.restore(sess, TRAIN_MODEL_URL)
print("Loading from an existing model {} ....................".
format(TRAIN_MODEL_URL))
else:
if not os.path.exists(TRAIN_MODEL_URL):
os.makedirs('/'.join(TRAIN_MODEL_URL.split('/')[:-1]))
os.makedirs('/'.join(TRAIN_GRAPH_URL.split('/')[:-1]))
init = tf.global_variables_initializer()
sess.run(init)
print(
"Create new model parameters....................................."
)
merged = tf.summary.merge_all()
val_accuracy = tf.summary.scalar('validation_accuracy',
value_accuracy)
val_f1 = tf.summary.scalar('validation_f1_score', value_f1)
train_writer = tf.summary.FileWriter(TRAIN_GRAPH_URL, graph)
train_writer.flush()
# 3 Perform an epoch of training
last_update = -1
best_f_score = -1
for epoch in range(no_epochs):
batch_size = train_batch_size
sys.stdout.flush()
iterations = math.ceil(no_dialogues / train_batch_size)
start_time = time.time()
val_i = 0
shuffle(dialogues)
for batch_id in range(iterations):
if batch_id == iterations - 1 and no_dialogues % iterations != 0:
batch_size = no_dialogues % train_batch_size
batch_user, batch_sys, batch_labels, batch_domain_labels, batch_user_uttr_len, batch_sys_uttr_len, \
batch_no_turns = generate_batch(dialogues, batch_id, batch_size, len(ontology))
[_, summary, da, sa, va, vf, pr, re] = sess.run(
[
train_step, merged, domain_accuracy, slot_accuracy,
value_accuracy, value_f1, precision, recall
],
feed_dict={
user: batch_user,
sys_res: batch_sys,
labels: batch_labels,
domain_labels: batch_domain_labels,
user_uttr_len: batch_user_uttr_len,
sys_uttr_len: batch_sys_uttr_len,
no_turns: batch_no_turns,
keep_prob: 0.5
})
print(
"The accuracies for domain is {:.2f}, slot {:.2f}, value {:.2f}, f1_score {:.2f} precision {:.2f}"
" recall {:.2f} for batch {}".format(
da, sa, va, vf, pr, re, batch_id + iterations * epoch))
train_writer.add_summary(
summary, start_batch + batch_id + iterations * epoch)
# ================================ VALIDATION ==============================================
if batch_id % batches_per_eval == 0 or batch_id == 0:
if batch_id == 0:
print("Batch", "0", "to", batch_id, "took",
round(time.time() - start_time, 2), "seconds.")
else:
print("Batch",
batch_id + iterations * epoch - batches_per_eval,
"to", batch_id + iterations * epoch, "took",
round(time.time() - start_time, 3), "seconds.")
start_time = time.time()
_, _, v_acc, f1_score, sm1, sm2 = evaluate_model(
sess, model_variables, val_data,
[val_accuracy, val_f1], batch_id, val_i)
val_i += 1
val_i %= val_iterations
train_writer.add_summary(
sm1, start_batch + batch_id + iterations * epoch)
train_writer.add_summary(
sm2, start_batch + batch_id + iterations * epoch)
stime = time.time()
current_metric = f1_score
print(" Validation metric:", round(current_metric,
5), " eval took",
round(time.time() - stime, 2), "last update at:",
last_update, "/", iterations)
# and if we got a new high score for validation f-score, we need to save the parameters:
if current_metric > best_f_score:
last_update = batch_id + iterations * epoch + 1
print(
"\n ====================== New best validation metric:",
round(current_metric,
4), " - saving these parameters. Batch is:",
last_update, "/", iterations,
"---------------- =========== \n")
best_f_score = current_metric
saver.save(sess, TRAIN_MODEL_URL)
print("The best parameters achieved a validation metric of",
round(best_f_score, 4))
def update(self, user_act=None):
"""Update the dialog state."""
if type(user_act) is not str:
raise Exception(
'Expected user_act to be <class \'str\'> type, but get {}.'.
format(type(user_act)))
prev_state = self.state
if not os.path.exists(os.path.join(self.data_dir, "results")):
os.makedirs(os.path.join(self.data_dir, "results"))
global train_batch_size
model_variables = self.model_variables
(user, sys_res, no_turns, user_uttr_len, sys_uttr_len, labels,
domain_labels, domain_accuracy, slot_accuracy, value_accuracy,
value_f1, train_step, keep_prob, predictions, true_predictions,
[y, _]) = model_variables
# generate fake dialogue based on history (this os to reuse the original MDBT code)
# actual_history = prev_state['history'] # [[sys, user], [sys, user], ...]
actual_history = copy.deepcopy(
prev_state['history']) # [[sys, user], [sys, user], ...]
actual_history = [['null']]
actual_history[-1].append(user_act)
actual_history = self.normalize_history(actual_history)
if len(actual_history) == 0:
actual_history = [[
'', user_act if len(user_act) > 0 else 'fake user act'
]]
fake_dialogue = {}
turn_no = 0
for _sys, _user in actual_history:
turn = {}
turn['system'] = _sys
fake_user = {}
fake_user['text'] = _user
fake_user['belief_state'] = default_state()['belief_state']
turn['user'] = fake_user
key = str(turn_no)
fake_dialogue[key] = turn
turn_no += 1
context, actual_context = process_history([fake_dialogue],
self.word_vectors,
self.ontology)
batch_user, batch_sys, batch_labels, batch_domain_labels, batch_user_uttr_len, batch_sys_uttr_len, \
batch_no_turns = generate_batch(context, 0, 1, len(self.ontology)) # old feature
# run model
[pred, y_pred] = self.sess.run(
[predictions, y],
feed_dict={
user: batch_user,
sys_res: batch_sys,
labels: batch_labels,
domain_labels: batch_domain_labels,
user_uttr_len: batch_user_uttr_len,
sys_uttr_len: batch_sys_uttr_len,
no_turns: batch_no_turns,
keep_prob: 1.0
})
# convert to str output
dialgs, _, _ = track_dialogue(actual_context, self.ontology, pred,
y_pred)
assert len(dialgs) >= 1
last_turn = dialgs[0][-1]
predictions = last_turn['prediction']
new_belief_state = copy.deepcopy(prev_state['belief_state'])
# updaet belief state
for item in predictions:
item = item.lower()
domain, slot, value = item.strip().split('-')
value = value[::-1].split(':', 1)[1][::-1]
if slot == 'price range':
slot = 'pricerange'
if slot not in ['name', 'book']:
if domain not in new_belief_state:
raise Exception(
'Error: domain <{}> not in belief state'.format(
domain))
slot = REF_SYS_DA[domain.capitalize()].get(slot, slot)
assert 'semi' in new_belief_state[domain]
assert 'book' in new_belief_state[domain]
if 'book' in slot:
assert slot.startswith('book ')
slot = slot.strip().split()[1]
domain_dic = new_belief_state[domain]
if slot in domain_dic['semi']:
new_belief_state[domain]['semi'][slot] = normalize_value(
self.value_dict, domain, slot, value)
elif slot in domain_dic['book']:
new_belief_state[domain]['book'][slot] = value
elif slot.lower() in domain_dic['book']:
new_belief_state[domain]['book'][slot.lower()] = value
else:
with open('mdbt_unknown_slot.log', 'a+') as f:
f.write(
'unknown slot name <{}> with value <{}> of domain <{}>\nitem: {}\n\n'
.format(slot, value, domain, item))
new_request_state = copy.deepcopy(prev_state['request_state'])
# update request_state
user_request_slot = self.detect_requestable_slots(user_act)
for domain in user_request_slot:
for key in user_request_slot[domain]:
if domain not in new_request_state:
new_request_state[domain] = {}
if key not in new_request_state[domain]:
new_request_state[domain][key] = user_request_slot[domain][
key]
# update state
new_state = copy.deepcopy(dict(prev_state))
new_state['belief_state'] = new_belief_state
new_state['request_state'] = new_request_state
self.state = new_state
return self.state
def test(self, sess):
"""Test the MDBT model on mdbt dataset. Almost the same as original code."""
if not os.path.exists("../../data/mdbt/results"):
os.makedirs("../../data/mdbt/results")
global train_batch_size, MODEL_URL, GRAPH_URL
model_variables = self.model_variables
(user, sys_res, no_turns, user_uttr_len, sys_uttr_len, labels,
domain_labels, domain_accuracy, slot_accuracy, value_accuracy,
value_f1, train_step, keep_prob, predictions, true_predictions,
[y, _]) = model_variables
[precision, recall, value_f1] = value_f1
# print("\tMDBT: Loading from an existing model {} ....................".format(MODEL_URL))
iterations = math.ceil(self.no_dialogues / train_batch_size)
batch_size = train_batch_size
[slot_acc,
tot_accuracy] = [np.zeros(len(self.ontology), dtype="float32"), 0]
slot_accurac = 0
# value_accurac = np.zeros((len(slots),), dtype="float32")
value_accurac = 0
joint_accuracy = 0
f1_score = 0
preci = 0
recal = 0
processed_dialogues = []
# np.set_printoptions(threshold=np.nan)
for batch_id in range(int(iterations)):
if batch_id == iterations - 1:
batch_size = self.no_dialogues - batch_id * train_batch_size
batch_user, batch_sys, batch_labels, batch_domain_labels, batch_user_uttr_len, batch_sys_uttr_len, \
batch_no_turns = generate_batch(self.dialogues, batch_id, batch_size, len(self.ontology))
[da, sa, va, vf, pr, re, pred, true_pred, y_pred] = sess.run(
[
domain_accuracy, slot_accuracy, value_accuracy, value_f1,
precision, recall, predictions, true_predictions, y
],
feed_dict={
user: batch_user,
sys_res: batch_sys,
labels: batch_labels,
domain_labels: batch_domain_labels,
user_uttr_len: batch_user_uttr_len,
sys_uttr_len: batch_sys_uttr_len,
no_turns: batch_no_turns,
keep_prob: 1.0
})
true = sum([
1 if np.array_equal(pred[k, :], true_pred[k, :])
and sum(true_pred[k, :]) > 0 else 0
for k in range(true_pred.shape[0])
])
actual = sum([
1 if sum(true_pred[k, :]) > 0 else 0
for k in range(true_pred.shape[0])
])
ja = true / actual
tot_accuracy += da
# joint_accuracy += ja
slot_accurac += sa
if math.isnan(pr):
pr = 0
preci += pr
recal += re
if math.isnan(vf):
vf = 0
f1_score += vf
# value_accurac += va
slot_acc += np.mean(np.asarray(np.equal(pred, true_pred),
dtype="float32"),
axis=0)
dialgs, va1, ja = track_dialogue(
self.actual_dialogues[batch_id * train_batch_size:batch_id *
train_batch_size + batch_size],
self.ontology, pred, y_pred)
processed_dialogues += dialgs
joint_accuracy += ja
value_accurac += va1
print(
"The accuracies for domain is {:.2f}, slot {:.2f}, value {:.2f}, other value {:.2f}, f1_score {:.2f} precision {:.2f}"
" recall {:.2f} for batch {}".format(da, sa, np.mean(va), va1,
vf, pr, re, batch_id))
print(
"End of evaluating the test set..........................................................................."
)
slot_acc /= iterations
# print("The accuracies for each slot:")
# print(value_accurac/iterations)
print("The overall accuracies for domain is"
" {}, slot {}, value {}, f1_score {}, precision {},"
" recall {}, joint accuracy {}".format(
tot_accuracy / iterations, slot_accurac / iterations,
value_accurac / iterations, f1_score / iterations,
preci / iterations, recal / iterations,
joint_accuracy / iterations))
with open(self.results_url, 'w') as f:
json.dump(processed_dialogues, f, indent=4)
