def main(in_dataset_folder, in_model_folder, in_no_ood_evaluation):
rev_vocab, kb, action_templates, config = load_model(in_model_folder)
test_dialogs, test_indices = read_dialogs(os.path.join(
in_dataset_folder, 'dialog-babi-task6-dstc2-tst.txt'),
with_indices=True)
et = EntityTracker(kb)
at = ActionTracker(None, et)
at.set_action_templates(action_templates)
vocab = {word: idx for idx, word in enumerate(rev_vocab)}
X, context_features, action_masks, y = make_dataset_for_hierarchical_lstm(
test_dialogs, test_indices, vocab, et, at, **config)
net = HierarchicalLSTM(config, context_features.shape[-1],
action_masks.shape[-1])
net.restore(in_model_folder)
eval_stats_full_dataset = evaluate_advanced(
net, (X, context_features, action_masks, y), test_dialogs,
at.action_templates)
print(
'Full dataset: {} turns overall, {} turns after the first OOD'.format(
eval_stats_full_dataset['total_turns'],
eval_stats_full_dataset['total_turns_after_ood']))
print('Accuracy:')
accuracy = eval_stats_full_dataset[
'correct_turns'] / eval_stats_full_dataset['total_turns']
accuracy_after_ood = eval_stats_full_dataset['correct_turns_after_ood'] / eval_stats_full_dataset['total_turns_after_ood'] \
if eval_stats_full_dataset['total_turns_after_ood'] != 0 \
else 0
accuracy_post_ood = eval_stats_full_dataset['correct_post_ood_turns'] / eval_stats_full_dataset['total_post_ood_turns'] \
if eval_stats_full_dataset['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))
print('Loss : {:.3f}'.format(eval_stats_full_dataset['avg_loss']))
if in_no_ood_evaluation:
eval_stats_no_ood = evaluate_advanced(
net, (X, context_features, action_masks, y),
test_indices,
at.action_templates,
ignore_ood_accuracy=True)
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))
print('Loss : {:.3f}'.format(eval_stats_no_ood['avg_loss']))
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_clean_dataset_folder, in_noisy_dataset_folder, in_model_folder,
in_no_ood_evaluation):
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)
post_ood_turns_clean, post_ood_turns_noisy = mark_post_ood_turns(
noisy_dialogs)
assert len(post_ood_turns_clean) == len(post_ood_turns_noisy)
for post_ood_turn_clean, post_ood_turn_noisy in zip(
sorted(post_ood_turns_clean), sorted(post_ood_turns_noisy)):
noisy_dialogs[post_ood_turn_noisy][0] = clean_dialogs[
post_ood_turn_clean][0]
et = EntityTracker(kb)
at = ActionTracker(None, et)
at.set_action_templates(action_templates)
vocab = {word: idx for idx, word in enumerate(rev_vocab)}
X_clean, context_features_clean, action_masks_clean, y_clean = make_dataset_for_hierarchical_lstm(
clean_dialogs, clean_indices, vocab, et, at, **config)
X_noisy, context_features_noisy, action_masks_noisy, y_noisy = make_dataset_for_hierarchical_lstm(
noisy_dialogs, noisy_indices, vocab, et, at, **config)
net = HierarchicalLSTM(config, context_features_clean.shape[-1],
action_masks_clean.shape[-1])
net.restore(in_model_folder)
eval_stats_clean = evaluate_advanced(
net, (X_clean, context_features_clean, action_masks_clean, y_clean),
at.action_templates,
post_ood_turns=post_ood_turns_clean)
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_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}; directly post-OOD: {:.3f}'.format(
accuracy, accuracy_post_ood))
print('Loss : {:.3f}'.format(eval_stats_clean['avg_loss']))
eval_stats_noisy = evaluate_advanced(
net, (X_noisy, context_features_noisy, action_masks_noisy, y_noisy),
at.action_templates,
post_ood_turns=post_ood_turns_noisy)
print('\n\n')
print(
'Noisy dataset: {} turns overall, {} turns after the first OOD'.format(
eval_stats_noisy['total_turns'],
eval_stats_noisy['total_turns_after_ood']))
print('Accuracy:')
accuracy = eval_stats_noisy['correct_turns'] / eval_stats_noisy[
'total_turns']
accuracy_after_ood = eval_stats_noisy['correct_turns_after_ood'] / eval_stats_noisy['total_turns_after_ood'] \
if eval_stats_noisy['total_turns_after_ood'] != 0 \
else 0
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
print(
'overall: {:.3f}; after first OOD: {:.3f}, directly post-OOD: {:.3f}'.
format(accuracy, accuracy_after_ood, accuracy_post_ood))
print('Loss : {:.3f}'.format(eval_stats_noisy['avg_loss']))
if in_no_ood_evaluation:
eval_stats_no_ood = evaluate_advanced(
net,
(X_noisy, context_features_noisy, action_masks_noisy, y_noisy),
at.action_templates,
post_ood_turns=post_ood_turns_noisy,
ignore_ood_accuracy=True)
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_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))