def add_more_examples(train,
prob=0.1,
drop=False,
swap=False,
key=False,
add=False,
all=False):
extra_examples = []
for line, tag in train:
if random.random() > prob: continue # this is correct...
if swap:
gen_attacks = attacks.swap_one_attack(line)
elif drop:
gen_attacks = attacks.drop_one_attack(line)
elif add:
gen_attacks = attacks.add_one_attack(line)
elif key:
gen_attacks = attacks.key_one_attack(line)
elif all:
gen_attacks = attacks.all_one_attack(line)
for _, adversary in gen_attacks:
w_i, c_i = get_word_and_char_indices(adversary)
adv_pred = predict(w_i, c_i)
if adv_pred != t2i[tag]:
# found incorrect
extra_examples.append((adversary, tag))
return extra_examples
def check_against_spell_mistakes(filename):
lines, tags = read_valid_lines(filename)
c = list(zip(lines, tags))
random.shuffle(c)
lines, tags = zip(*c)
lines = lines
tags = tags
# if in small (or COMPUTATION HEAVY) modes
if params['small']:
lines = lines[:200]
tags = tags[:200]
if params['small'] and params['sc_atd']:
lines = lines[:99]
tags = tags[:99]
inc_count = 0.0
inc_count_per_attack = [0.0 for _ in range(NUM_ATTACKS + 1)]
error_analyser = {}
for line, tag in tqdm(zip(lines, tags)):
w_i, c_i = get_word_and_char_indices(line)
if params['is_spell_check']:
w_i, c_i = get_word_and_char_indices(checker.correct_string(line))
# check if model prediction is incorrect, if yes, continue
model_prediction = predict(w_i, c_i)
if t2i[tag] != model_prediction:
# already incorrect, no attack needed
inc_count += 1
inc_count_per_attack[0] += 1.0
continue
found_incorrect = False
worst_example = line
worst_confidence = 1.0
worst_idx = -1
ignore_incides = set()
for attack_count in range(1, 1 + NUM_ATTACKS):
ignore_incides.add(worst_idx)
if 'drop' in type_of_attack:
gen_attacks = attacks.drop_one_attack(
worst_example,
ignore_incides,
include_ends=params['include_ends'])
elif 'swap' in type_of_attack:
gen_attacks = attacks.swap_one_attack(
worst_example, include_ends=params['include_ends'])
elif 'key' in type_of_attack:
gen_attacks = attacks.key_one_attack(
worst_example,
ignore_incides,
include_ends=params['include_ends'])
elif 'add' in type_of_attack:
gen_attacks = attacks.add_one_attack(
worst_example,
ignore_incides,
include_ends=params['include_ends'])
elif 'all' in type_of_attack:
gen_attacks = attacks.all_one_attack(
worst_example,
ignore_incides,
include_ends=params['include_ends'])
for idx, adversary in gen_attacks:
original_adv = adversary
if found_incorrect: break
if params['is_spell_check']:
adversary = checker.correct_string(adversary)
w_i, c_i = get_word_and_char_indices(adversary)
adv_pred = predict(w_i, c_i)
confidence = get_confidence(w_i, c_i)
if confidence < worst_confidence:
worst_confidence = confidence
worst_idx = idx
worst_example = adversary
if adv_pred != t2i[tag]:
# found incorrect prediction
found_incorrect = True
break
if found_incorrect:
inc_count += 1.0
inc_count_per_attack[attack_count] += 1.0
if params['analyse']:
error_analyser[line] = {}
error_analyser[line]['adversary'] = original_adv.split(
)[idx]
error_analyser[line]['correction'] = adversary.split()[idx]
error_analyser[line]['idx'] = idx
break
for num in range(NUM_ATTACKS + 1):
log.pr_red(
'adversarial accuracy of the model after %d attacks = %.2f' %
(num, 100. *
(1 - sum(inc_count_per_attack[:num + 1]) / len(lines))))
if params['analyse']:
curr_time = datetime.datetime.now().strftime("%B_%d_%I:%M%p")
pickle.dump(error_analyser,
open("error_analyser_" + str(curr_time) + ".p", 'wb'))
return None
def check_against_spell_mistakes(filename):
if(singles):
lines, tags = read_valid_lines_single(filename)
spell_check_model = BertForMaskedLM.from_pretrained("bert-base-uncased", cache_dir="/data/kashyap_data/")
spell_check_model.eval()
spell_check_model.to('cuda')
c = list(zip(lines, tags))
random.shuffle(c)
lines, tags = zip(*c)
lines = lines
tags = tags
# if in small (or COMPUTATION HEAVY) modes
if params['small']:
lines = lines[:200]
tags = tags[:200]
if params['small'] and params['sc_atd']:
lines = lines[:99]
tags = tags[:99]
inc_count = 0.0
inc_count_per_attack = [0.0 for _ in range(NUM_ATTACKS+1)]
error_analyser = {}
for line, tag in tqdm(zip(lines, tags)):
w_i, c_i = get_word_and_char_indices(line)
if params['is_spell_check']:
"""
pickle_off = open("tokenizer.pkl", "rb")
bert_tokenizer = pickle.load(pickle_off)
tokenizer = nlp.Defaults.create_tokenizer(nlp)
tokens = [t.text for t in tokenizer(line)]
misspelled = spell.unknown(tokens)
indices = []
for i in misspelled:
indices.append(tokens.index(i))
tokens[tokens.index(i)] = "[MASK]"
tokens.insert(0, "[CLS]")
tokens.insert(-1, "[SEP]")
new_string = " ".join(tokens)
bert_tokenized_text = bert_tokenizer.tokenize(new_string)
indexed_tokens = bert_tokenizer.convert_tokens_to_ids(
bert_tokenized_text)
segments_ids = [0]*len(bert_tokenized_text)
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
tokens_tensor = tokens_tensor.to('cuda')
segments_tensors = segments_tensors.to('cuda')
with torch.no_grad():
outputs = spell_check_model(tokens_tensor, token_type_ids=segments_tensors)
predictions = outputs[0]
predicted_tokens = []
indices = [i+1 for i in indices]
for i in indices:
predicted_tokens.append(bert_tokenizer.convert_ids_to_tokens(
[torch.argmax(predictions[0, i]).item()])[0])
for j in range(len(predicted_tokens)):
tokens[indices[j]] = predicted_tokens[j]
tokens.remove("[SEP]")
tokens.remove("[CLS]")
final_string = " ".join(tokens)
"""
temp = line.split(" ")
new_string = []
for i in temp:
new_string.append(spell.correction(i))
temp = " ".join(new_string)
line = temp
w_i, c_i = get_word_and_char_indices(line)
# check if model prediction is incorrect, if yes, continue
model_prediction = predict(w_i, c_i)
if tag != model_prediction:
# already incorrect, no attack needed
inc_count += 1
inc_count_per_attack[0] += 1.0
continue
found_incorrect = False
worst_example = line
worst_confidence = 1.0
worst_idx = -1
ignore_incides=set()
for attack_count in range(1, 1 + NUM_ATTACKS):
ignore_incides.add(worst_idx)
if 'drop' in type_of_attack:
gen_attacks = attacks.drop_one_attack(worst_example, ignore_incides, include_ends=params['include_ends'])
elif 'swap' in type_of_attack:
gen_attacks = attacks.swap_one_attack(worst_example, include_ends=params['include_ends'])
elif 'key' in type_of_attack:
gen_attacks = attacks.key_one_attack(worst_example, ignore_incides, include_ends=params['include_ends'])
elif 'add' in type_of_attack:
gen_attacks = attacks.add_one_attack(worst_example, ignore_incides, include_ends=params['include_ends'])
elif 'all' in type_of_attack:
gen_attacks = attacks.all_one_attack(worst_example, ignore_incides, include_ends=params['include_ends'])
for idx, adversary in gen_attacks:
original_adv = adversary
if found_incorrect: break
if params['is_spell_check']:
"""
pickle_off = open("tokenizer.pkl", "rb")
bert_tokenizer = pickle.load(pickle_off)
tokenizer = nlp.Defaults.create_tokenizer(nlp)
tokens = [t.text for t in tokenizer(line)]
misspelled = spell.unknown(tokens)
indices = []
for i in misspelled:
indices.append(tokens.index(i))
tokens[tokens.index(i)] = "[MASK]"
tokens.insert(0, "[CLS]")
tokens.insert(-1, "[SEP]")
new_string = " ".join(tokens)
bert_tokenized_text = bert_tokenizer.tokenize(new_string)
indexed_tokens = bert_tokenizer.convert_tokens_to_ids(
bert_tokenized_text)
segments_ids = [0]*len(bert_tokenized_text)
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
tokens_tensor = tokens_tensor.to('cuda')
segments_tensors = segments_tensors.to('cuda')
with torch.no_grad():
outputs = spell_check_model(
tokens_tensor, token_type_ids=segments_tensors)
predictions = outputs[0]
predicted_tokens = []
indices = [i+1 for i in indices]
for i in indices:
predicted_tokens.append(bert_tokenizer.convert_ids_to_tokens(
[torch.argmax(predictions[0, i]).item()])[0])
for j in range(len(predicted_tokens)):
tokens[indices[j]] = predicted_tokens[j]
tokens.remove("[SEP]")
tokens.remove("[CLS]")
final_string = " ".join(tokens)
"""
temp = line[0].split(" ")
new_string = []
for i in temp:
new_string.append(spell.correction(i))
temp = " ".join(new_string)
adversary = temp
w_i, c_i = get_word_and_char_indices(adversary)
adv_pred = predict(w_i, c_i)
confidence = get_confidence(w_i, c_i)
if confidence < worst_confidence:
worst_confidence = confidence
worst_idx = idx
worst_example = adversary
if adv_pred != tag:
# found incorrect prediction
found_incorrect = True
break
if found_incorrect:
inc_count += 1.0
inc_count_per_attack[attack_count] += 1.0
if params['analyse']:
error_analyser[line] = {}
error_analyser[line]['adversary'] = original_adv.split()[idx]
error_analyser[line]['correction'] = adversary.split()[idx]
error_analyser[line]['idx'] = idx
break
else:
lines, tags = read_valid_lines_single(filename)
spell_check_model = BertForMaskedLM.from_pretrained("bert-base-uncased", cache_dir="/data/kashyap_data/")
spell_check_model.eval()
spell_check_model.to('cuda')
# c = list(zip(lines, tags))
# random.shuffle(c)
# lines, tags = zip(*c)
lines = lines
tags = tags
# if in small (or COMPUTATION HEAVY) modes
if params['small']:
lines = lines[:200]
tags = tags[:200]
if params['small'] and params['sc_atd']:
lines = lines[:99]
tags = tags[:99]
inc_count = 0.0
inc_count_per_attack = [0.0 for _ in range(NUM_ATTACKS+1)]
error_analyser = {}
for line in tqdm(lines):
tag = line[2]
w_i, c_i = get_word_and_char_indices(line)
if params['is_spell_check']:
temp = line[0].split(" ")
new_string = []
for i in temp:
new_string.append(spell.correction(i))
temp = " ".join(new_string)
line[0] = temp
w_i, c_i = get_word_and_char_indices(line)
# check if model prediction is incorrect, if yes, continue
model_prediction = predict(w_i, c_i)
if tag != model_prediction:
# already incorrect, no attack needed
inc_count += 1
inc_count_per_attack[0] += 1.0
continue
found_incorrect = False
worst_example = line[0] #could make random choice
worst_confidence = 1.0
worst_idx = -1
ignore_incides=set()
for attack_count in range(1, 1 + NUM_ATTACKS):
ignore_incides.add(worst_idx)
if 'drop' in type_of_attack:
gen_attacks = attacks.drop_one_attack(worst_example, ignore_incides, include_ends=params['include_ends'])
elif 'swap' in type_of_attack:
gen_attacks = attacks.swap_one_attack(worst_example, include_ends=params['include_ends'])
elif 'key' in type_of_attack:
gen_attacks = attacks.key_one_attack(worst_example, ignore_incides, include_ends=params['include_ends'])
elif 'add' in type_of_attack:
gen_attacks = attacks.add_one_attack(worst_example, ignore_incides, include_ends=params['include_ends'])
elif 'all' in type_of_attack:
gen_attacks = attacks.all_one_attack(worst_example, ignore_incides, include_ends=params['include_ends'])
for idx, adversary in gen_attacks:
original_adv = adversary
if found_incorrect: break
if params['is_spell_check']:
temp = adversary.split(" ")
new_string = []
for i in temp:
new_string.append(spell.correction(i))
temp = " ".join(new_string)
adversary = adversary
w_i, c_i = get_word_and_char_indices_adv(adversary)
adv_pred = predict(w_i, c_i)
confidence = get_confidence(w_i, c_i)
if confidence < worst_confidence:
worst_confidence = confidence
worst_idx = idx
worst_example = adversary
if adv_pred != tag:
# found incorrect prediction
found_incorrect = True
break
if found_incorrect:
inc_count += 1.0
inc_count_per_attack[attack_count] += 1.0
if params['analyse']:
error_analyser[line] = {}
error_analyser[line]['adversary'] = original_adv.split()[idx]
error_analyser[line]['correction'] = adversary.split()[idx]
error_analyser[line]['idx'] = idx
break
for num in range(NUM_ATTACKS + 1):
log.pr_red('adversarial accuracy of the model after %d attacks = %.2f'
%(num, 100. * (1 - sum(inc_count_per_attack[:num+1])/len(lines))))
if params['analyse']:
curr_time = datetime.datetime.now().strftime("%B_%d_%I:%M%p")
pickle.dump(error_analyser, open("error_analyser_" + str(curr_time) + ".p", 'wb'))
return None