def main(in_dataset_folder, in_noisy_dataset_folder, in_custom_vocab_file, in_model_folder, in_config):
with open(in_config, encoding='utf-8') as config_in:
config = json.load(config_in)
train_dialogs, train_indices = read_dialogs(os.path.join(in_dataset_folder, 'dialog-babi-task6-dstc2-trn.txt'),
with_indices=True)
dev_dialogs, dev_indices = read_dialogs(os.path.join(in_dataset_folder, 'dialog-babi-task6-dstc2-dev.txt'),
with_indices=True)
test_dialogs, test_indices = read_dialogs(os.path.join(in_noisy_dataset_folder, 'dialog-babi-task6-dstc2-tst.txt'),
with_indices=True)
kb = make_augmented_knowledge_base(os.path.join(in_dataset_folder, 'dialog-babi-task6-dstc2-kb.txt'),
os.path.join(in_dataset_folder, 'dialog-babi-task6-dstc2-candidates.txt'))
max_noisy_dialog_length = max([item['end'] - item['start'] + 1 for item in test_indices])
config['max_input_length'] = max_noisy_dialog_length
post_ood_turns_clean, post_ood_turns_noisy = mark_post_ood_turns(test_dialogs, BABI_CONFIG['backoff_utterance'].lower())
et = EntityTracker(kb)
at = ActionTracker(os.path.join(in_dataset_folder, 'dialog-babi-task6-dstc2-candidates.txt'), et)
if in_custom_vocab_file is not None:
with open(in_custom_vocab_file) as vocab_in:
rev_vocab = [line.rstrip() for line in vocab_in]
vocab = {word: idx for idx, word in enumerate(rev_vocab)}
else:
utterances_tokenized = []
for dialog in train_dialogs:
utterances_tokenized += list(map(lambda x: x.split(), dialog))
vocab, rev_vocab = make_vocabulary(utterances_tokenized,
config['max_vocabulary_size'],
special_tokens=[PAD, START, UNK, EOS] + list(kb.keys()))
config['vocabulary_size'] = len(vocab)
data_train = make_dataset_for_hierarchical_hcn(train_dialogs, train_indices, vocab, et, at, **config)
data_dev = make_dataset_for_hierarchical_hcn(dev_dialogs, dev_indices, vocab, et, at, **config)
data_test = make_dataset_for_hierarchical_hcn(test_dialogs, test_indices, vocab, et, at, **config)
random_input = generate_dropout_turns_for_hierarchical_hcn(10000,
config['max_sequence_length'],
[utterance[0] for utterance in train_dialogs],
vocab,
config['turn_word_dropout_prob'])
save_model(rev_vocab, config, kb, at.action_templates, in_model_folder)
trainer = Trainer(data_train,
data_dev,
data_test,
at.action_templates,
random_input,
post_ood_turns_noisy,
config,
vocab,
in_model_folder)
trainer.train()
def main(in_clean_dataset_folder, in_noisy_dataset_folder, in_model_folder, in_mode, in_runs_number):
rev_vocab, kb, action_templates, config = load_model(in_model_folder)
clean_dialogs, clean_indices = read_dialogs(os.path.join(in_clean_dataset_folder, 'dialog-babi-task6-dstc2-tst.txt'),
with_indices=True)
noisy_dialogs, noisy_indices = read_dialogs(os.path.join(in_noisy_dataset_folder, 'dialog-babi-task6-dstc2-tst.txt'),
with_indices=True)
max_noisy_dialog_length = max([item['end'] - item['start'] + 1 for item in noisy_indices])
config['max_input_length'] = max_noisy_dialog_length
post_ood_turns_clean, post_ood_turns_noisy = mark_post_ood_turns(noisy_dialogs, BABI_CONFIG['backoff_utterance'].lower())
et = EntityTracker(kb)
at = ActionTracker(None, et)
at.set_action_templates(action_templates)
vocab = {word: idx for idx, word in enumerate(rev_vocab)}
data_clean = make_dataset_for_vhcn_v2(clean_dialogs, clean_indices, vocab, et, at, **config)
data_noisy = make_dataset_for_vhcn_v2(noisy_dialogs, noisy_indices, vocab, et, at, **config)
context_features_clean, action_masks_clean = data_clean[2:4]
net = VariationalHierarchicalLSTMv3(rev_vocab, config, context_features_clean.shape[-1], action_masks_clean.shape[-1])
net.restore(in_model_folder)
if in_mode == 'clean':
eval_stats_clean = evaluate_advanced(net,
data_clean,
at.action_templates,
BABI_CONFIG['backoff_utterance'].lower(),
post_ood_turns=post_ood_turns_clean,
runs_number=in_runs_number)
print('Clean dataset: {} turns overall'.format(eval_stats_clean['total_turns']))
print('Accuracy:')
accuracy = eval_stats_clean['correct_turns'] / eval_stats_clean['total_turns']
accuracy_continuous = eval_stats_clean['correct_continuous_turns'] / eval_stats_clean['total_turns']
accuracy_post_ood = eval_stats_clean['correct_post_ood_turns'] / eval_stats_clean['total_post_ood_turns'] \
if eval_stats_clean['total_post_ood_turns'] != 0 \
else 0
print('overall: {:.3f}; continuous: {:.3f}; directly post-OOD: {:.3f}'.format(accuracy, accuracy_continuous, accuracy_post_ood))
print('Loss : {:.3f}'.format(eval_stats_clean['avg_loss']))
elif in_mode == 'noisy':
eval_stats_noisy = evaluate_advanced(net,
data_noisy,
at.action_templates,
BABI_CONFIG['backoff_utterance'].lower(),
post_ood_turns=post_ood_turns_noisy,
runs_number=in_runs_number)
print('\n\n')
print('Noisy dataset: {} turns overall'.format(eval_stats_noisy['total_turns']))
print('Accuracy:')
accuracy = eval_stats_noisy['correct_turns'] / eval_stats_noisy['total_turns']
accuracy_continuous = eval_stats_noisy['correct_continuous_turns'] / eval_stats_noisy['total_turns']
accuracy_post_ood = eval_stats_noisy['correct_post_ood_turns'] / eval_stats_noisy['total_post_ood_turns'] \
if eval_stats_noisy['total_post_ood_turns'] != 0 \
else 0
accuracy_ood = eval_stats_noisy['correct_ood_turns'] / eval_stats_noisy['total_ood_turns'] \
if eval_stats_noisy['total_ood_turns'] != 0 \
else 0
print('overall: {:.3f}; continuous: {:.3f}; directly post-OOD: {:.3f}; OOD: {:.3f}'.format(accuracy,
accuracy_continuous,
accuracy_post_ood,
accuracy_ood))
print('Loss : {:.3f}'.format(eval_stats_noisy['avg_loss']))
elif in_mode == 'noisy_ignore_ood':
eval_stats_no_ood = evaluate_advanced(net,
data_noisy,
at.action_templates,
BABI_CONFIG['backoff_utterance'].lower(),
post_ood_turns=post_ood_turns_noisy,
ignore_ood_accuracy=True,
runs_number=in_runs_number)
print('Accuracy (OOD turns ignored):')
accuracy = eval_stats_no_ood['correct_turns'] / eval_stats_no_ood['total_turns']
accuracy_after_ood = eval_stats_no_ood['correct_turns_after_ood'] / eval_stats_no_ood['total_turns_after_ood'] \
if eval_stats_no_ood['total_turns_after_ood'] != 0 \
else 0
accuracy_post_ood = eval_stats_no_ood['correct_post_ood_turns'] / eval_stats_no_ood['total_post_ood_turns'] \
if eval_stats_no_ood['total_post_ood_turns'] != 0 \
else 0
print('overall: {:.3f}; after first OOD: {:.3f}, directly post-OOD: {:.3f}'.format(accuracy, accuracy_after_ood, accuracy_post_ood))
def main(in_dataset_folder, in_noisy_dataset_folder, in_custom_vocab_file,
in_model_folder, in_config):
with open(in_config, encoding='utf-8') as config_in:
config = json.load(config_in)
train_json = load_hcn_json(os.path.join(in_dataset_folder, 'train.json'))
dev_json = load_hcn_json(os.path.join(in_dataset_folder, 'dev.json'))
# test_json = load_hcn_json(os.path.join(in_dataset_folder, 'test.json'))
test_ood_json = load_hcn_json(
os.path.join(in_noisy_dataset_folder, 'test_ood.json'))
kb = make_augmented_knowledge_base(
os.path.join(BABI_FOLDER, 'dialog-babi-task6-dstc2-kb.txt'),
os.path.join(BABI_FOLDER, 'dialog-babi-task6-dstc2-candidates.txt'))
action_templates = train_json['actions']
max_noisy_dialog_length = max(
[len(dialog['turns']) for dialog in test_ood_json['dialogs']])
config['max_input_length'] = max_noisy_dialog_length
et = EntityTracker(kb)
post_ood_turns_clean, post_ood_turns_noisy = mark_post_ood_turns(
test_ood_json)
if in_custom_vocab_file is not None:
with open(in_custom_vocab_file) as vocab_in:
rev_vocab = [line.rstrip() for line in vocab_in]
vocab = {word: idx for idx, word in enumerate(rev_vocab)}
else:
utterances_tokenized = []
for dialog in train_json['dialogs']:
for utterance in dialog['turns']:
utterances_tokenized.append(utterance['input'].split())
vocab, rev_vocab = make_vocabulary(
utterances_tokenized,
config['max_vocabulary_size'],
special_tokens=[PAD, START, UNK, EOS] + list(kb.keys()))
ctx_features = []
for dialog in train_json['dialogs']:
for utterance in dialog['turns']:
if 'context_features' in utterance:
ctx_features.append(utterance['context_features'])
ctx_features_vocab, ctx_features_rev_vocab = make_vocabulary(
ctx_features, config['max_vocabulary_size'], special_tokens=[])
config['vocabulary_size'] = len(vocab)
print('Training with config: {}'.format(json.dumps(config)))
data_preparation_function = getattr(utils.preprocessing,
config['data_preparation_function'])
data_train = data_preparation_function(train_json, vocab,
ctx_features_vocab, et, **config)
data_dev = data_preparation_function(dev_json, vocab, ctx_features_vocab,
et, **config)
# data_test = data_preparation_function(test_json, vocab, ctx_features_vocab, et, **config)
data_test_ood = data_preparation_function(test_ood_json, vocab,
ctx_features_vocab, et, **config)
dropout_turn_generation_function = getattr(
utils.preprocessing, config['dropout_turn_generation_function'])
random_input = dropout_turn_generation_function(
10000, 3, config['max_sequence_length'], train_json, vocab,
config['turn_word_dropout_prob'])
save_model(rev_vocab, config, kb, action_templates, in_model_folder)
net = getattr(modules, config['model_name'])(vocab, config,
len(ctx_features_vocab),
len(action_templates))
trainer = Trainer(data_train, data_dev, data_test_ood, action_templates,
random_input, post_ood_turns_noisy, config, net,
in_model_folder)
trainer.train()
def main(in_clean_dataset_folder, in_noisy_dataset_folder, in_model_folder,
in_mode, in_runs_number):
rev_vocab, kb, action_templates, config = load_model(in_model_folder)
train_json = load_hcn_json(
os.path.join(in_clean_dataset_folder, 'train.json'))
test_json = load_hcn_json(
os.path.join(in_clean_dataset_folder, 'test.json'))
test_ood_json = load_hcn_json(
os.path.join(in_noisy_dataset_folder, 'test_ood.json'))
max_noisy_dialog_length = max(
[len(dialog['turns']) for dialog in test_ood_json['dialogs']])
config['max_input_length'] = max_noisy_dialog_length
post_ood_turns_clean, post_ood_turns_noisy = mark_post_ood_turns(
test_ood_json)
et = EntityTracker(kb)
action_weights = defaultdict(lambda: 1.0)
action_weights[0] = 0.0
action_weighting = np.vectorize(action_weights.__getitem__)
vocab = {word: idx for idx, word in enumerate(rev_vocab)}
ctx_features = []
for dialog in train_json['dialogs']:
for utterance in dialog['turns']:
if 'context_features' in utterance:
ctx_features.append(utterance['context_features'])
ctx_features_vocab, ctx_features_rev_vocab = make_vocabulary(
ctx_features, config['max_vocabulary_size'], special_tokens=[])
data_preparation_function = getattr(utils.preprocessing,
config['data_preparation_function'])
data_clean = data_preparation_function(test_json, vocab,
ctx_features_vocab, et, **config)
data_noisy = data_preparation_function(test_ood_json, vocab,
ctx_features_vocab, et, **config)
data_clean_with_weights = *data_clean, action_weighting(data_clean[-1])
data_noisy_with_weights = *data_noisy, action_weighting(data_noisy[-1])
net = getattr(modules, config['model_name'])(vocab, config,
len(ctx_features_vocab),
len(action_templates))
net.restore(in_model_folder)
if in_mode == 'clean':
eval_stats_clean = evaluate_advanced(
net,
data_clean_with_weights,
action_templates,
BABI_CONFIG['backoff_utterance'],
post_ood_turns=post_ood_turns_clean,
runs_number=in_runs_number)
print('Clean dataset: {} turns overall'.format(
eval_stats_clean['total_turns']))
print('Accuracy:')
accuracy = eval_stats_clean['correct_turns'] / eval_stats_clean[
'total_turns']
accuracy_continuous = eval_stats_clean[
'correct_continuous_turns'] / eval_stats_clean['total_turns']
accuracy_post_ood = eval_stats_clean['correct_post_ood_turns'] / eval_stats_clean['total_post_ood_turns'] \
if eval_stats_clean['total_post_ood_turns'] != 0 \
else 0
ood_f1 = eval_stats_clean['ood_f1']
print('overall acc: {:.3f}; continuous acc: {:.3f}; '
'directly post-OOD acc: {:.3f}; OOD F1: {:.3f}'.format(
accuracy, accuracy_continuous, accuracy_post_ood, ood_f1))
print('Loss : {:.3f}'.format(eval_stats_clean['avg_loss']))
elif in_mode == 'noisy':
eval_stats_noisy = evaluate_advanced(
net,
data_noisy_with_weights,
action_templates,
BABI_CONFIG['backoff_utterance'],
post_ood_turns=post_ood_turns_noisy,
runs_number=in_runs_number)
print('\n\n')
print('Noisy dataset: {} turns overall'.format(
eval_stats_noisy['total_turns']))
print('Accuracy:')
accuracy = eval_stats_noisy['correct_turns'] / eval_stats_noisy[
'total_turns']
accuracy_continuous = eval_stats_noisy[
'correct_continuous_turns'] / eval_stats_noisy['total_turns']
accuracy_post_ood = eval_stats_noisy['correct_post_ood_turns'] / eval_stats_noisy['total_post_ood_turns'] \
if eval_stats_noisy['total_post_ood_turns'] != 0 \
else 0
accuracy_ood = eval_stats_noisy['correct_ood_turns'] / eval_stats_noisy['total_ood_turns'] \
if eval_stats_noisy['total_ood_turns'] != 0 \
else 0
ood_f1 = eval_stats_noisy['ood_f1']
print('overall acc: {:.3f}; continuous acc: {:.3f}; '
'directly post-OOD acc: {:.3f}; OOD acc: {:.3f}; OOD F1: {:.3f}'.
format(accuracy, accuracy_continuous, accuracy_post_ood,
accuracy_ood, ood_f1))
print('Loss : {:.3f}'.format(eval_stats_noisy['avg_loss']))
elif in_mode == 'noisy_ignore_ood':
eval_stats_no_ood = evaluate_advanced(
net,
data_noisy_with_weights,
action_templates,
BABI_CONFIG['backoff_utterance'],
post_ood_turns=post_ood_turns_noisy,
ignore_ood_accuracy=True,
runs_number=in_runs_number)
print('Accuracy (OOD turns ignored):')
accuracy = eval_stats_no_ood['correct_turns'] / eval_stats_no_ood[
'total_turns']
accuracy_after_ood = eval_stats_no_ood['correct_turns_after_ood'] / eval_stats_no_ood['total_turns_after_ood'] \
if eval_stats_no_ood['total_turns_after_ood'] != 0 \
else 0
accuracy_post_ood = eval_stats_no_ood['correct_post_ood_turns'] / eval_stats_no_ood['total_post_ood_turns'] \
if eval_stats_no_ood['total_post_ood_turns'] != 0 \
else 0
print('overall: {:.3f}; '
'after first OOD: {:.3f}; '
'directly post-OOD: {:.3f}'.format(accuracy, accuracy_after_ood,
accuracy_post_ood))