def test_multiple_actions(self):
texts = [
'The quick brown fox jumps over the lazy dog',
'Zology raku123456 fasdasd asd4123414 1234584'
]
flows = [
naf.Sequential(
[nac.RandomCharAug(action=Action.INSERT),
naw.RandomWordAug()]),
naf.Sequential([
nac.OcrAug(),
nac.KeyboardAug(aug_min=1),
nac.RandomCharAug(action=Action.SUBSTITUTE,
aug_min=1,
aug_char_p=0.6,
aug_word_p=0.6)
])
]
for flow in flows:
for text in texts:
augmented_text = flow.augment(text)
self.assertNotEqual(text, augmented_text)
self.assertLess(0, len(text))
self.assertLess(0, len(texts))
self.assertLess(0, len(flows))
def test_n_output_without_augmentation(self):
texts = [
'AAAAAAAAAAA AAAAAAAAAAAAAA'
]
flows = [
naf.Sequential([
nac.OcrAug(),
nac.OcrAug()
]),
naf.Sometimes([
nac.RandomCharAug(),
nac.RandomCharAug()
], pipeline_p=0.00001)
]
for flow in flows:
for text in texts:
for _ in range(5):
augmented_texts = flow.augment(text, n=3)
all_not_equal = False
for augmented_text in augmented_texts:
if augmented_text != text:
all_not_equal = True
break
if all_not_equal:
break
self.assertFalse(all_not_equal)
self.assertLess(0, len(flows))
self.assertLess(0, len(texts))
def nlpaug(word):
aug = naf.Sometimes([
nac.OcrAug(),
nac.KeyboardAug(),
nac.RandomCharAug(action="insert"),
nac.RandomCharAug(action="substitute"),
nac.RandomCharAug(action="swap"),
nac.RandomCharAug(action="delete"),
naw.SpellingAug(),
])
word = aug.augment(word)
return word
def test_multi_thread(self):
text = 'The quick brown fox jumps over the lazy dog'
n = 3
w2v_model_path = os.path.join(os.environ["MODEL_DIR"], 'word',
'word_embs',
'GoogleNews-vectors-negative300.bin')
flows = [
naf.Sequential([
naf.Sequential([
nac.OcrAug(),
naw.WordEmbsAug(model_type='word2vec',
model_path=w2v_model_path)
]),
naf.Sequential([
nac.RandomCharAug(),
]),
naw.ContextualWordEmbsAug(model_path='xlnet-base-cased',
action="substitute",
temperature=0.7,
device='cpu')
]),
naf.Sometimes([
naf.Sequential([
nac.OcrAug(),
nac.RandomCharAug(),
]),
naf.Sometimes([
naw.WordEmbsAug(model_type='word2vec',
model_path=w2v_model_path)
],
pipeline_p=0.999),
naw.ContextualWordEmbsAug(model_path='xlnet-base-cased',
action="substitute",
temperature=0.7,
device='cpu')
],
pipeline_p=0.9999)
]
for num_thread in [1, 3]:
for flow in flows:
augmented_data = flow.augment(text, n=n, num_thread=num_thread)
self.assertEqual(len(augmented_data), n)
def setUpClass(cls):
env_config_path = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', '.env'))
load_dotenv(env_config_path)
cls.augs = [
nac.RandomCharAug(),
naw.ContextualWordEmbsAug(),
nas.ContextualWordEmbsForSentenceAug()
]
def test_n_output_without_augmentation(self):
texts = ['AAAAAAAAAAA AAAAAAAAAAAAAA']
flows = [
naf.Sequential([nac.OcrAug(), nac.OcrAug()]),
naf.Sometimes(
[nac.RandomCharAug(), nac.RandomCharAug()], pipeline_p=0.00001)
]
for flow in flows:
for text in texts:
at_least_one_equal = False
for _ in range(5):
augmented_texts = flow.augment(text, n=3)
if len(augmented_texts
) == 1 and augmented_texts[0] == text:
at_least_one_equal = True
break
self.assertTrue(at_least_one_equal)
self.assertLess(0, len(flows))
self.assertLess(0, len(texts))
def test_multi_thread(self):
text = 'The quick brown fox jumps over the lazy dog.'
n = 3
augs = [
nac.KeyboardAug(tokenizer=text_tokenizer.split_sentence),
nac.RandomCharAug(tokenizer=text_tokenizer.split_sentence),
]
for num_thread in [1, 3]:
for aug in augs:
augmented_data = aug.augment(text, n=n, num_thread=num_thread)
self.assertEqual(len(augmented_data), n)
def test_multiple_actions(self):
texts = [
'The quick brown fox jumps over the lazy dog',
'Zology raku123456 fasdasd asd4123414 1234584'
]
flows = [
naf.Sequential([
naf.Sometimes([
nac.RandomCharAug(action=Action.INSERT),
nac.RandomCharAug(action=Action.DELETE)
],
pipeline_p=0.5),
naf.Sequential([
nac.OcrAug(),
nac.QwertyAug(aug_min=1),
nac.RandomCharAug(action=Action.SUBSTITUTE,
aug_min=1,
aug_char_p=0.6,
aug_word_p=0.6)
],
name='Sub_Seq')
]),
naf.Sometimes([
naf.Sometimes([
nac.RandomCharAug(action=Action.INSERT),
nac.RandomCharAug(action=Action.DELETE)
]),
naf.Sequential([
nac.OcrAug(),
nac.QwertyAug(aug_min=1),
nac.RandomCharAug(action=Action.SUBSTITUTE,
aug_min=1,
aug_char_p=0.6,
aug_word_p=0.6)
])
],
pipeline_p=0.5)
]
# Since prob may be low and causing do not perform data augmentation. Retry 5 times
for flow in flows:
at_least_one_not_equal = False
for _ in range(0, 5):
for text in texts:
self.assertLess(0, len(text))
augmented_text = flow.augment(text)
if text != augmented_text:
at_least_one_not_equal = True
self.assertLess(0, len(text))
if at_least_one_not_equal:
break
self.assertTrue(at_least_one_not_equal)
self.assertLess(0, len(flows))
self.assertLess(0, len(texts))
def test_empty_input_for_insert(self):
texts = ['', ' ']
augs = [
nac.RandomCharAug(action='insert')
]
for aug in augs:
for text in texts:
augmented_text = aug.augment(text)
self.assertTrue(augmented_text is None or augmented_text.strip() == '')
augmented_texts = aug.augment(texts)
for augmented_text in augmented_texts:
self.assertTrue(augmented_text is None or augmented_text.strip() == '')
def test_stopwords(self):
text = 'The quick brown fox jumps over the lazy dog.'
stopwords = ['The', 'brown', 'fox', 'jumps', 'the', 'dog']
augs = [
nac.RandomCharAug(stopwords=stopwords),
nac.KeyboardAug(stopwords=stopwords),
nac.OcrAug(stopwords=stopwords)
]
for aug in augs:
for i in range(10):
augmented_text = aug.augment(text)
self.assertTrue(
'quick' not in augmented_text or 'over' not in augmented_text or 'lazy' not in augmented_text)
def test_stopwords_regex(self):
text = 'The quick brown fox jumps over the lazy dog.'
stopwords_regex = "( [a-zA-Z]{1}ox | [a-z]{1}og|(brown)|[a-zA-z]{1}he)|[a-z]{2}mps "
augs = [
nac.RandomCharAug(action="delete", stopwords_regex=stopwords_regex),
nac.KeyboardAug(stopwords_regex=stopwords_regex),
nac.OcrAug(stopwords_regex=stopwords_regex)
]
for aug in augs:
for i in range(10):
augmented_text = aug.augment(text)
self.assertTrue(
'quick' not in augmented_text or 'over' not in augmented_text or 'lazy' not in augmented_text)
def test_single_action(self):
texts = [
'The quick brown fox jumps over the lazy dog',
'Zology raku123456 fasdasd asd4123414 1234584 s@#'
]
flow = naf.Sequential([nac.RandomCharAug(action=Action.INSERT, min_char=1)])
for text in texts:
augmented_text = flow.augment(text)
self.assertNotEqual(text, augmented_text)
self.assertLess(0, len(text))
self.assertLess(0, len(texts))
def test_empty_input_for_substitute(self):
texts = ['', ' ']
augs = [
nac.RandomCharAug(action='substitute'),
nac.KeyboardAug(),
nac.OcrAug()
]
for aug in augs:
for text in texts:
augmented_text = aug.augment(text)
self.assertTrue(augmented_text is None or augmented_text.strip() == '')
augmented_texts = aug.augment(texts)
for augmented_text in augmented_texts:
self.assertTrue(augmented_text is None or augmented_text.strip() == '')
def test_multiple_actions(self):
texts = [
'The quick brown fox jumps over the lazy dog',
'Zology raku123456 fasdasd asd4123414 1234584'
]
flows = [
naf.Sequential([
naf.Sometimes([
nac.RandomCharAug(action="insert"),
nac.RandomCharAug(action="delete")
],
pipeline_p=0.9),
naf.Sequential(
[
# nac.OcrAug(), nac.QwertyAug(aug_min=1),
nac.RandomCharAug(action="substitute",
aug_char_min=1,
aug_char_p=0.6,
aug_word_p=0.6)
],
name='Sub_Seq')
]),
naf.Sometimes([
naf.Sometimes([
nac.RandomCharAug(action="insert"),
nac.RandomCharAug(action="delete")
]),
naf.Sequential([
nac.OcrAug(),
nac.KeyboardAug(aug_char_min=1),
nac.RandomCharAug(action="substitute",
aug_char_min=1,
aug_char_p=0.6,
aug_word_p=0.6)
])
],
pipeline_p=0.9)
]
# Since prob may be low and causing do not perform data augmentation. Retry 5 times
for flow in flows:
for text in texts:
at_least_one_not_equal = False
for _ in range(5):
augmented_text = flow.augment(text, n=1)
if text != augmented_text:
at_least_one_not_equal = True
break
self.assertTrue(at_least_one_not_equal)
self.assertLess(0, len(flows))
self.assertLess(0, len(texts))
def test_min_char(self):
text = 'He eats apple'
augs = [
nac.RandomCharAug(min_char=5),
nac.KeyboardAug(min_char=5),
nac.OcrAug(min_char=5)
]
for aug in augs:
augmented = False
for i in range(10):
augmented_text = aug.augment(text)
if 'apple' not in augmented_text:
augmented = True
break
self.assertTrue(augmented)
def apply_noise(datestr: str, format_dict: Dict[str, str],
noise_dict: Dict[str, Any]) -> str:
sep = format_dict["separator"]
sep = sep[0] if len(sep) > 1 else sep
date_parts = datestr.split(sep)
if noise_dict["append_day_suffix"]:
date_parts[0] = date_parts[0] + noise_dict["day_suffix"]
# Add spelling mistake to month name
if len(format_dict["month"]) > 2 and np.random.random() <= 0.3:
aug = nac.RandomCharAug(
action=noise_dict["aug_char_action"],
aug_char_min=1,
aug_char_max=1,
)
date_parts[1] = aug.augment(date_parts[1])
out = ""
for idx, date_part in enumerate(date_parts):
part_sep = sep
rand_val = np.random.random()
if noise_dict["noisy_separator"] and rand_val <= 0.15:
part_sep += " "
if noise_dict["noisy_separator"] and rand_val <= 0.15:
part_sep = " " + part_sep
elif noise_dict["noisy_separator"] and rand_val <= 0.5:
part_sep += "".join(
np.random.choice(ADDITIONAL_PUNCTUATION, size=2))
if idx == 0:
out += date_part
else:
out += f"{part_sep}{date_part}"
# out = f"{sep}".join(date_parts)
if noise_dict["casing"] == "uppercase":
out = out.upper()
elif noise_dict["casing"] == "lowercase":
out = out.lower()
if noise_dict["place_in_sentence"]:
out = put_datestr_in_sentence(out, noise_dict["sentence"])
return out
def test_tokenizer(self):
augs = [
nac.OcrAug(tokenizer=text_tokenizer.split_sentence),
nac.KeyboardAug(tokenizer=text_tokenizer.split_sentence),
nac.RandomCharAug(tokenizer=text_tokenizer.split_sentence),
]
text = 'The quick brown fox, jumps over lazy dog.'
expected_tokens = ['The', ' quick', ' brown', ' fox', ', ', 'jumps', ' over', ' lazy', ' dog', '.']
for aug in augs:
tokens = aug.tokenizer(text)
self.assertEqual(tokens, expected_tokens)
text = 'The quick !brown fox, jumps # over lazy dog .'
expected_tokens = ['The', ' quick', ' !', 'brown', ' fox', ', ', 'jumps', ' # ', 'over', ' lazy', ' dog', ' .']
for aug in augs:
tokens = aug.tokenizer(text)
self.assertEqual(tokens, expected_tokens)
def setUpClass(cls):
env_config_path = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', '.env'))
load_dotenv(env_config_path)
# https://freewavesamples.com/yamaha-v50-rock-beat-120-bpm
cls.sample_wav_file = os.environ.get(
"DATA_DIR") + 'Yamaha-V50-Rock-Beat-120bpm.wav'
cls.audio, cls.sampling_rate = librosa.load(cls.sample_wav_file)
cls.textual_augs = [
nac.RandomCharAug(),
naw.ContextualWordEmbsAug(),
nas.ContextualWordEmbsForSentenceAug()
]
cls.audio_augs = [
naa.CropAug(sampling_rate=cls.sampling_rate),
naa.SpeedAug(),
]
def test_multi_inputs(self):
texts = [
'The quick brown fox jumps over the lazy dog.',
'The quick brown fox jumps over the lazy dog.',
'nac KeyboardAug ( tokenizer = text_tokenizer . split_sentence )',
'nac KeyboardAug ( tokenizer = text_tokenizer . split_sentence )'
]
augs = [
nac.KeyboardAug(tokenizer=text_tokenizer.split_sentence),
nac.RandomCharAug(tokenizer=text_tokenizer.split_sentence),
]
num_thread = 2
for aug in augs:
augmented_data = aug.augment(texts, num_thread=num_thread)
self.assertEqual(len(augmented_data), len(texts))
num_thread = 1
for aug in augs:
augmented_data = aug.augment(texts, num_thread=num_thread)
self.assertEqual(len(augmented_data), len(texts))
def test_augment_detail(self):
text = 'The quick brown fox jumps over the lazy dog'
augs = [
nac.KeyboardAug(min_char=1, include_detail=True),
nac.OcrAug(min_char=1, include_detail=True),
nac.RandomCharAug(min_char=2, include_detail=True)
]
for aug in augs:
augmented_text, augment_details = aug.augment(text)
self.assertNotEqual(text, augmented_text)
self.assertGreater(len(augment_details), 0)
for augment_detail in augment_details:
self.assertTrue(augment_detail['orig_token'] in text)
self.assertGreater(augment_detail['orig_start_pos'], -1)
self.assertGreater(augment_detail['new_start_pos'], -1)
self.assertGreater(augment_detail['change_seq'], 0)
self.assertIn(augment_detail['action'], Action.getall())
# Get back original input by re-engineering
reengineering_text = augmented_text
for change_obj in sorted(augment_details, key=lambda item: item['orig_start_pos'], reverse=True):
if change_obj['action'] == Action.DELETE:
text_prefix = reengineering_text[:change_obj['new_start_pos']]
text_core = change_obj['orig_token'] + ' '
text_suffix = reengineering_text[change_obj['new_start_pos']:]
elif change_obj['action'] in [Action.INSERT, Action.SUBSTITUTE]:
text_prefix = reengineering_text[:change_obj['new_start_pos']]
text_core = reengineering_text[change_obj['new_start_pos']:].replace(
change_obj['new_token'], change_obj['orig_token'], 1)
text_suffix = ''
# TODO
# elif change_obj['action'] in Action.SWAP:
reengineering_text = text_prefix + text_core + text_suffix
reengineering_text = reengineering_text.strip()
self.assertEqual(text, reengineering_text)
def test_augment_detail(self):
text = 'The quick brown fox jumps over the lazy dog'
flows = [
naf.Sequential([
naf.Sometimes([
nac.RandomCharAug(action="insert"),
nac.RandomCharAug(action="delete")
],
pipeline_p=0.5),
naf.Sequential([
nac.RandomCharAug(action="substitute",
aug_char_min=1,
aug_char_p=0.6,
aug_word_p=0.6)
],
name='Sub_Seq')
],
include_detail=True),
naf.Sometimes([
naf.Sometimes([
nac.RandomCharAug(action="insert"),
nac.RandomCharAug(action="delete")
]),
naf.Sequential([
nac.OcrAug(),
nac.KeyboardAug(aug_char_min=1),
nac.RandomCharAug(action="substitute",
aug_char_min=1,
aug_char_p=0.6,
aug_word_p=0.6)
])
],
pipeline_p=1,
include_detail=True)
]
for flow in flows:
augmented_text, augment_details = flow.augment(text)
self.assertNotEqual(text, augmented_text)
self.assertGreater(len(augment_details), 0)
for augment_detail in augment_details:
self.assertGreater(augment_detail['orig_start_pos'], -1)
self.assertGreater(augment_detail['new_start_pos'], -1)
self.assertGreater(augment_detail['change_seq'], 0)
self.assertIn(augment_detail['action'], Action.getall())
def test_single_action(self):
texts = [
'The quick brown fox jumps over the lazy dog',
'Zology raku123456 fasdasd asd4123414 1234584 s@#'
]
# Since prob may be low and causing do not perform data augmentation. Retry 5 times
at_least_one_not_equal = False
for _ in range(0, 5):
flow = naf.Sometimes([nac.RandomCharAug(action=Action.INSERT)], pipeline_p=0.6)
for text in texts:
augmented_text = flow.augment(text)
if text != augmented_text:
at_least_one_not_equal = True
self.assertLess(0, len(text))
if at_least_one_not_equal:
break
self.assertTrue(at_least_one_not_equal)
self.assertLess(0, len(texts))
def random_delete_aug(corpus):
aug = nac.RandomCharAug(tokenizer=whitespace_tokenizer, action="delete")
# go through all train and dev sentences
augmented_sentences = []
for sentence in corpus.train:
augmented_texts = aug.augment(sentence.to_tokenized_string(), n=3)
for augmented_text in augmented_texts:
augmented_sentence: Sentence = Sentence()
augmented_token_texts = augmented_text.split(" ")
for augmented_token_text, original_token in zip(augmented_token_texts, sentence):
# make a new token
augmented_token = Token(augmented_token_text)
# transfer annotations over to augmented token
augmented_token.annotation_layers = original_token.annotation_layers
# add augmented token to augmented sentence
augmented_sentence.add_token(augmented_token)
# add augmented sentence to list of all augmented sentences
augmented_sentences.append(augmented_sentence)
corpus = Corpus(train=SentenceDataset(augmented_sentences),
dev=corpus.dev,
test=corpus.test)
return corpus
def test_n_output(self):
texts = [
'The quick brown fox jumps over the lazy dog',
'Zology raku123456 fasdasd asd4123414 1234584',
'AAAAAAAAAAA AAAAAAAAAAAAAA'
]
flows = [
naf.Sequential(
[nac.RandomCharAug(action=Action.INSERT),
naw.RandomWordAug()]),
naf.Sometimes([
nac.RandomCharAug(action=Action.INSERT),
nac.RandomCharAug(action=Action.DELETE)
],
pipeline_p=0.9),
naf.Sequential([
naf.Sequential([
nac.RandomCharAug(action=Action.INSERT),
naw.RandomWordAug()
]),
naf.Sometimes([
nac.RandomCharAug(action=Action.INSERT),
nac.RandomCharAug(action=Action.DELETE)
],
pipeline_p=0.9)
])
]
for flow in flows:
for text in texts:
augmented_texts = flow.augment(text, n=3)
self.assertGreater(len(augmented_texts), 1)
for augmented_text in augmented_texts:
self.assertNotEqual(augmented_text, text)
self.assertLess(0, len(flows))
self.assertLess(0, len(texts))
def random_char_swap(text):
#Swap character randomly
aug = nac.RandomCharAug(action="swap")
attacked_text = aug.augment(text)
print("Attacked Text:")
print(attacked_text)
def random_char_del(text):
#Delete character randomly
aug = nac.RandomCharAug(action="delete")
attacked_text = aug.augment(text)
print("Attacked Text:")
print(attacked_text)
rows = []
for row in csv_reader:
rows.append(row)
for row in list(rows): # Substitute character by keyboard distance
if row[1] != "flag" and row[1] !='R' and row[1]!="":
counter += 1
if counter != 2:
row[2] = str(keyboard_dis.augment(row[2]))
if counter == 3:
counter = 0
csv_writer.writerow(row)
with open("input_classification_test_data.csv","r") as input:
with open("/Users/wenyaxie/Downloads/negative_data_character_insertion.csv","w") as output:
random_insert = nac.RandomCharAug(action="insert")
csv_reader = csv.reader(input)
csv_writer = csv.writer(output)
counter = 0
rows = []
for row in csv_reader:
rows.append(row)
for row in list(rows): # Insert character randomly
if row[1] != "flag" and row[1] !='R' and row[1]!="":
counter += 1
if counter != 2:
row[2] = str(random_insert.augment(row[2]))
if counter == 3:
def random_char_subsi(text):
# Substitute character randomly
aug = nac.RandomCharAug(action="substitute")
attacked_text = aug.augment(text)
print("Attacked Text:")
print(attacked_text)
import warnings
import pandas as pd
import numpy as np
from numpy.random import choice
import nlpaug.augmenter.char as nac
from pandas.api.types import is_string_dtype
from pandas.api.types import is_numeric_dtype
warnings.filterwarnings('ignore')
swapRandom = nac.RandomCharAug(action="swap")
replaceTwoCharsBasedOnKeyboard = nac.KeyboardAug()
deleteRandomChar = nac.RandomCharAug(action="delete")
np.random.seed(0) # Setting seed globally
# r = np.random.RandomState(0) TODO: Setting the seed for the class locally without impacting global numpy seed
class DataCorruptor:
def __init__(self, data, feature_cols, feature_stats=None, log=False):
# np.random.seed(0)
if feature_stats is None:
# TODO: Take the cardinlal statistics (like most common value), into account while corupting data
self.feature_stats = data.describe().T[['mean', 'std', 'max', 'min']]
else:
self.feature_stats = feature_stats
def random_char_insert(text):
# Insert character randomly
aug = nac.RandomCharAug(action="insert")
attacked_text = aug.augment(text)
print("Attacked Text:")
print(attacked_text)
