def test_multilingual(self):
# import nltk
# nltk.download('omw')
# French
text = 'chien'
expected_texts = [
'cliquer', 'clic', 'aboyeur', 'hot dog', 'franc',
'canis familiaris', 'achille', 'toutou', 'cliquet', 'clébard',
'talon', 'chienchien', 'quignon', 'chien de chasse'
]
aug = naw.SynonymAug(aug_src='wordnet', lang='fra')
augmented_text = aug.augment(text)
self.assertTrue(augmented_text in expected_texts)
expected_texts = [
'toutou', 'maître chien', 'clébard', 'dog', 'chienne', 'chiens',
'chiot', 'cynophiles', 'clebs'
]
model_path = os.path.join(os.environ.get("MODEL_DIR"), 'word', 'ppdb',
'ppdb-1.0-s-lexical-french')
aug = naw.SynonymAug(aug_src='ppdb', model_path=model_path)
augmented_text = aug.augment(text)
self.assertTrue(augmented_text in expected_texts)
# Spanish
text = 'Un rápido zorro marrón salta sobre el perro perezoso'
aug = naw.SynonymAug(aug_src='wordnet', lang='spa')
for _ in range(10):
augmented_text = aug.augment(text)
if augmented_text != text:
break
self.assertNotEqual(augmented_text, text)
def test_reload(self):
text = 'The quick brown fox jumps over the lazy dog'
aug = naw.SynonymAug(aug_src='wordnet')
self.assertNotEqual(text, aug.augment(text))
model_path = os.path.join(os.environ.get("MODEL_DIR"), 'word', 'ppdb', 'ppdb-2.0-s-all')
aug2 = naw.SynonymAug(aug_src='ppdb', model_path=model_path)
self.assertNotEqual(text, aug2.augment(text))
def setUpClass(cls):
env_config_path = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', '..', '.env'))
load_dotenv(env_config_path)
cls.augs = [
naw.SynonymAug(aug_src='wordnet'),
naw.SynonymAug(aug_src='ppdb',
model_path=os.environ.get("MODEL_DIR") +
'ppdb-2.0-s-all.txt')
]
class QuestionGenerator:
"""Class contains logic for augmenting text"""
aug = naw.ContextualWordEmbsAug(model_path="bert-base-uncased",
action="substitute")
aug_single = naw.SynonymAug(aug_src="wordnet")
@staticmethod
def augment(text):
"""
checks whether to apply synonym or contextual augmentation
:param text:
:return:
"""
tokens = split_sentence(re.sub("[^a-zA-Z0-9 ]+", "", text))
if len(tokens) > 1:
return QuestionGenerator.aug.augment(text, n=10, num_thread=4)
else:
return QuestionGenerator.aug_single.augment(tokens[0], n=10)
@staticmethod
async def generateQuestions(texts):
"""
generates a list of variations for a given sentence/question
:param texts: list of text
:return: list of variations
"""
if type(texts) == str:
texts = [texts]
result = [QuestionGenerator.augment(text) for text in texts]
return sum(result, [])
def test_empty_input_substitute(self):
texts = ['', ' ']
self.word2vec_model.action = 'substitute'
self.context_word_embs_model.action = 'substitute'
augs = [
naw.SpellingAug(),
naw.AntonymAug(),
naw.RandomWordAug(action='substitute'),
naw.SynonymAug(aug_src='wordnet'),
naw.TfIdfAug(model_path=self.tfidf_model_path,
action="substitute"), self.word2vec_model,
self.context_word_embs_model
]
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() == '')
class QuestionGenerator:
aug = naw.ContextualWordEmbsAug(model_path='bert-base-uncased',
action="substitute")
aug_single = naw.SynonymAug(aug_src='wordnet')
@staticmethod
def augment(text):
tokens = split_sentence(re.sub('[^a-zA-Z0-9 ]+', '', text))
if len(tokens) > 1:
return QuestionGenerator.aug.augment(text, n=10, num_thread=4)
else:
return QuestionGenerator.aug_single.augment(tokens[0], n=10)
@staticmethod
async def generateQuestions(texts):
""" This function generates a list of variations for a given sentence/question.
E.g. await QuestionGenerator.generateQuestions('your question') will return the list
of variations for that particular question """
if type(texts) == str:
texts = [texts]
result = [QuestionGenerator.augment(text) for text in texts]
return sum(result, [])
def augmentation(text, insert=False, substitute=False, swap=True, delete=True):
augs = []
if insert:
aug = naw.WordEmbsAug(
model_type='word2vec',
model_path=
'/media/jionie/my_disk/Kaggle/Tweet/model/word2vec/GoogleNews-vectors-negative300.bin',
action="insert")
augs.append(aug)
if substitute:
aug_sub = naw.SynonymAug(aug_src='wordnet')
augs.append(aug_sub)
if swap:
aug_swap = naw.RandomWordAug(action="swap")
augs.append(aug_swap)
if delete:
aug_del = naw.RandomWordAug()
augs.append(aug_del)
aug = naf.Sometimes(augs, aug_p=0.5, pipeline_p=0.5)
# print("before aug:", text)
text = aug.augment(text, n=1)
# print("after aug:", text)
return text
def augment_dataset(csv, model_dir):
original = pd.read_csv(csv)
"""
Conduct two process of augmentation
1. Synonym augmentation
2. Word Embedding augmemntation
"""
syn_df = original.copy()
syn_aug = naw.SynonymAug(aug_src='wordnet')
# synonym augenter(simple version)
for i, query in enumerate(syn_df.src):
synonym = syn_aug.augment(query)
syn_df.at[i, 'src'] = synonym
#word embedding augmenter
word_df = original.copy()
embed_aug = naw.WordEmbsAug(model_type='fasttext',
model_path=model_dir +
'/wiki-news-300d-1M.vec',
action="insert")
for i, query in enumerate(word_df.src):
insertion = embed_aug.augment(query)
word_df.at[i, 'src'] = insertion
a1 = pd.catcat([original, syn_df])
a2 = pd.concat([a1, word_df])
a2.to_csv(os.path.join(model_dir, 'augmented.csv'), index=False)
return a2
def train_eval_dataset(dataset: pd.DataFrame,lang="ita",expansion=10):
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
nltk.download('omw')
flow = naf.Sometimes([naw.SynonymAug(lang=lang, aug_min=10),naw.RandomWordAug("swap"),naw.RandomWordAug("delete"),nac.KeyboardAug()])
train_afert_exp=[]
dev_after_exp=[]
for idx, row in dataset.iterrows():
logging.info("[{}/{}] {}".format(idx, len(dataset), row["question"]))
new_text = [new for new in flow.augment(row["question"], n=expansion)]
train_afert_exp.append({"label": row["question_id"], "text": row["question"]})
th=int(len(new_text)*0.8)
for text in new_text[:th]:
train_afert_exp.append({"label": row["question_id"], "text": text})
for text in new_text[th:]:
dev_after_exp.append({"label": row["question_id"], "text": text})
train=train_afert_exp
dev=dev_after_exp
train = pd.DataFrame(train).sample(frac=1.0)
dev = pd.DataFrame(dev).sample(frac=1.0)
return train, dev
def augment_by_class(df, max_n):
word_index = {}
for phrase in df['Body']:
words = phrase.split(' ')
for word in words:
word_index[word] = word_index.get(word, 0) + 1
index_df = pd.DataFrame([{
'token': i,
'count': word_index[i]
} for i in word_index])
index_df = index_df[index_df['count'] >= 10]
aug = naw.SynonymAug(stopwords=index_df['token'])
aug2 = naw.RandomWordAug()
factor = (max_n // len(df)) + 2
result = set()
for phrase in df['Body']:
result.add(phrase)
print(f'Augmenting for {phrase}')
for item in aug.augment(phrase, n=factor):
result.add(item)
for item in aug2.augment(phrase, n=2):
result.add(item)
return list(result)
def synonym_wordnet(text):
#Synonym Augmenter
#Substitute word by WordNet's synonym
aug = naw.SynonymAug(aug_src='wordnet')
attacked_text = aug.augment(text)
print("Attacked Text:")
print(attacked_text)
def word_substitution(text, aug_src='wordnet'):
# import nlpaug.flow as naf
import nlpaug.augmenter.word as naw
aug = naw.SynonymAug(aug_src=aug_src)
augmented_text = aug.augment(text)
return augmented_text
def test_multilingual(self):
# French
text = 'chien'
expected_texts = [
'cliquer', 'clic', 'aboyeur', 'hot dog', 'franc',
'canis familiaris', 'achille', 'toutou', 'cliquet', 'clébard',
'talon', 'chienchien', 'quignon', 'chien de chasse'
]
aug = naw.SynonymAug(aug_src='wordnet', lang='fra')
augmented_text = aug.augment(text)
self.assertTrue(augmented_text in expected_texts)
# Spanish
text = 'Un rápido zorro marrón salta sobre el perro perezoso'
aug = naw.SynonymAug(aug_src='wordnet', lang='spa')
augmented_text = aug.augment(text)
self.assertNotEqual(augmented_text, text)
def data_augment(corpus, label):
syn_aug = naw.SynonymAug(aug_src="wordnet")
rand_aug = naw.RandomWordAug(action="swap")
data_struc = {'emotion_label': [], 'emotion_text': []}
aug_dataframe = pd.DataFrame(data_struc)
print('Augmenting data')
for label, sentence in zip(label, corpus):
if sentence.find("\n") > 0:
sentence = sentence.replace("\n", "")
aug_dataframe = aug_dataframe.append(
{
'emotion_label': label,
'emotion_text': sentence
},
ignore_index=True)
augmented_sent = syn_aug.augment(sentence)
aug_dataframe = aug_dataframe.append(
{
'emotion_label': label,
'emotion_text': augmented_sent
},
ignore_index=True)
augmented_sent1 = rand_aug.augment(sentence)
aug_dataframe = aug_dataframe.append(
{
'emotion_label': label,
'emotion_text': augmented_sent1
},
ignore_index=True)
else:
aug_dataframe = aug_dataframe.append(
{
'emotion_label': label,
'emotion_text': sentence
},
ignore_index=True)
augmented_sent = syn_aug.augment(sentence)
aug_dataframe = aug_dataframe.append(
{
'emotion_label': label,
'emotion_text': augmented_sent
},
ignore_index=True)
aug1 = naw.RandomWordAug(action="swap")
augmented_sent1 = rand_aug.augment(sentence)
aug_dataframe = aug_dataframe.append(
{
'emotion_label': label,
'emotion_text': augmented_sent1
},
ignore_index=True)
print('Augmentation Completed')
return aug_dataframe['emotion_text'], aug_dataframe['emotion_label']
def __init__(self):
aug0 = naw.RandomWordAug()
aug1 = naw.ContextualWordEmbsAug(model_path='bert-base-uncased',
action="substitute")
aug2 = naw.SynonymAug(aug_src='wordnet')
aug3 = naw.SplitAug()
aug4 = naw.ContextualWordEmbsAug(model_path='bert-base-uncased',
action="insert")
self.augs = [aug0, aug1, aug2, aug3, aug4]
def prepare_aug():
# Contextual Word Embeddings Augmenter, Substitute word by contextual word embeddings
neu_aug = []
neu_aug.append(
naw.ContextualWordEmbsAug(model_path='bert-base-uncased',
action="insert"))
neu_aug.append(
naw.ContextualWordEmbsAug(model_path='bert-base-uncased',
action="substitute"))
neu_aug.append(
naw.ContextualWordEmbsAug(model_path='distilbert-base-uncased',
action="substitute"))
neu_aug.append(
naw.ContextualWordEmbsAug(model_path='roberta-base',
action="substitute"))
# Synonym Augmenter, Substitute word by WordNet's synonym
syn_aug = []
syn_aug.append(naw.SynonymAug(aug_src='wordnet'))
syn_aug.append(
naw.SynonymAug(
aug_src='ppdb',
model_path=
'/home/ubuntu/sentiment_analysis/bert-sentiment/syn_model/ppdb-2.0-tldr'
))
# Antonym Augmenter
ant_aug = []
ant_aug.append(naw.AntonymAug())
# Random Word Augmenter
random_aug = []
random_aug.append(naw.RandomWordAug(action="swap"))
random_aug.append(naw.RandomWordAug())
print('augmenter initialization finished ...')
aug = []
aug.extend(neu_aug)
aug.extend(syn_aug)
aug.extend(ant_aug)
aug.extend(random_aug)
return aug
def synonym_replacement(text, n=N):
"""
Randomly choose n words from the sentence that are not stop words. Replace each of these words with one of its
synonyms chosen at random.
"""
aug = naw.SynonymAug(aug_src='wordnet',
aug_min=n,
aug_max=n,
stopwords=english_stopwords)
augmented_text = aug.augment(text)
return augmented_text
def augment_data(
num_new_class_0,
num_new_class_1,
clear_old_augmented_data=False,
write_to_path='data/synonym_augmented_reddit_submissions.csv'):
"""
Generates augmented data by producing new samples for class 0 and/or class 1, the two
classes that are underrepresented in our dataset, and writing them to a designated new
file 'data/augmented_reddit_submissions.csv'.
Takes in:
- num_new_class_0: Integer representing how many new samples of class 0 to generate
- num_new_class_1: Integer representing how many new samples of class 1 to generate
- clear_old_augmented_data: Boolean; if set to True, will overwrite the old augmented data rather than
- write_to_path: The path of the file to write or append the new samples to.
This function makes use of the nlpaug library's word augmenter.
"""
# We experimented with a couple other nlpaug models, but we ended up choosing SynonymAug
# because it gave us the most natural-sounding and least noisy samples.
# Other models we tried were:
# naw.WordEmbsAug this one uses word2vec to find similar words for augmentation; it
# ended up giving us very noisy data that made the performance of
# all models decrease.
# naw.ContextualWordEmbsAug this one uses BERT to do the same as the above; it was slightly
# better, but still pretty noisy.
aug = naw.SynonymAug(aug_src='wordnet')
new_rows = []
with open('data/reddit_submissions.csv') as f:
reader = csv.reader(f)
# Skip the first row that just has column names
rows = list(reader)[1:]
print('unfiltered rows: {}'.format(len(rows)))
seed_rows_with_class_0 = list(
filter(lambda r: CLASSES[r[0]] == 0, rows))
seed_rows_with_class_1 = list(
filter(lambda r: CLASSES[r[0]] == 1, rows))
print('filtered rows: {}'.format(
len(seed_rows_with_class_0) + len(seed_rows_with_class_1)))
print('generating new data with class 0')
create_new_rows(seed_rows_with_class_0, num_new_class_0, new_rows, aug)
print('generating new data with class 1')
create_new_rows(seed_rows_with_class_1, num_new_class_1, new_rows, aug)
file_open_mode = 'w' if clear_old_augmented_data else 'a'
with open(write_to_path, file_open_mode) as f:
writer = csv.writer(f, quoting=csv.QUOTE_NONNUMERIC, delimiter=',')
print('writing new rows')
writer.writerows(new_rows)
def test_language(self):
text = 'chien'
expected_texts = [
'cliquer', 'clic', 'aboyeur', 'hot dog', 'franc',
'canis familiaris', 'achille', 'toutou', 'cliquet', 'clébard',
'talon', 'chienchien', 'quignon', 'chien de chasse'
]
aug = naw.SynonymAug(aug_src='wordnet', lang='fra')
augmented_text = aug.augment(text)
self.assertTrue(augmented_text in expected_texts)
def fn_synonym_replacement(self):
product_choices = list(
product(self.database_choice, self.aug_p_choices))
for item in product_choices:
aug = naw.SynonymAug(aug_src=item[0][1],
aug_p=item[1],
stopwords=self.stopwords)
print("\nmodelname-action-words augmented: {}-{}-{}\n".format(
item[0][0], "substitute", item[1]))
augmented_text = aug.augment(text, n=self.n_words)
print(augmented_text, "\n")
self.write_excel(augmented_text, item, item[1])
self.workbook.close()
def __init__(self,
aug_min=1,
aug_max=10,
aug_p=0.3,
tokenizer=None,
always_apply=False,
p=0.5):
super().__init__(always_apply, p)
self.aug = naw.SynonymAug(
aug_min=aug_min,
aug_max=aug_max,
aug_p=aug_p,
tokenizer=tokenizer,
)
def augmentation(self,
text,
insert=False,
substitute=False,
swap=True,
delete=True):
augs = []
if insert:
# aug = naw.ContextualWordEmbsAug(
# model_path=self.model_type, action="insert", device='cuda')
# wget https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz
aug = naw.WordEmbsAug(
model_type='word2vec',
model_path=
'/C:/Users/admin/Documents/Nitin/mycodes/kaggle_google_quest_qna/data/helpers/word2vec/GoogleNews-vectors-negative300.bin',
action="insert")
augs.append(aug)
if substitute:
# aug = naw.ContextualWordEmbsAug(
# model_path=self.model_type, action="substitute", device='cuda')
# aug = naw.WordEmbsAug(
# model_type='word2vec', model_path='/media/jionie/my_disk/Kaggle/Google_Quest_Answer/model/word2vec/GoogleNews-vectors-negative300.bin',
# action="substitute")
aug_sub = naw.SynonymAug(aug_src='wordnet')
augs.append(aug_sub)
# text = aug.augment(text)
if swap:
aug_swap = naw.RandomWordAug(action="swap")
augs.append(aug_swap)
# text = aug.augment(text)
if delete:
aug_del = naw.RandomWordAug()
augs.append(aug_del)
# text = aug.augment(text)
aug = naf.Sometimes(augs, aug_p=0.5, pipeline_p=0.5)
# print("before aug:", text)
text = aug.augment(text, n=1)
# print("after aug:", text)
return text
def __init__(self):
antAug = naw.AntonymAug()
synAug = naw.SynonymAug(aug_src='wordnet')
embAug = naw.ContextualWordEmbsAug(model_path='bert-base-uncased',
action="substitute")
self.model_dict = {
0: antAug,
1: synAug,
2: embAug
}
self.output_data = {
'Sentence1': [],
'Sentence2': [],
'Label': []
}
def augment_dataset(dataset, prob=0.3, augment_type='wordnet'):
'''Augment data using wordnet'''
data_targets = list(zip(dataset.data, dataset.target))
sample_size = int(np.ceil(len(dataset.data) * prob))
LOGGER.info(f'Augmenting {sample_size} datasets')
sample_data = random.choices(data_targets, k=sample_size)
if augment_type == 'wordnet':
aug = naw.SynonymAug(aug_src='wordnet')
aug_data, aug_targets = list(
zip(*[(aug.augment(text), target) for text, target in sample_data]))
dataset.data = [*dataset.data, *aug_data]
dataset.target = [*dataset.target, *aug_targets]
LOGGER.info(
f'Total dataset size after augmentation: {len(dataset.target)}')
return dataset
def main():
print("Loading ppdb dataset...")
aug = naw.SynonymAug(aug_p=0.5,
aug_src="ppdb",
model_path="../data/ppdb-2.0-tldr",
aug_max=100)
print("Augmentor initialized")
dataset = load_dataset('cnn_dailymail', "3.0.0")
train_data = dataset['train']
inputs = train_data['article']
targets = train_data['highlights']
ids = train_data['id']
print(len(train_data))
# articles = [nltk.sent_tokenize(inp) for inp in tqdm(inputs)]
# pickle.dump(articles, open('articles_sentences.json', 'wb'))
articles = pickle.load(open('articles_sentences.json', 'rb'))[:80000]
d = {}
for i, art in enumerate(tqdm(articles)):
augmented = aug.augment(art)
# d[ids[i]] = ' '.join(augmented)
d[ids[i]] = augmented
# num_splits = 10
# split = len(articles)//num_splits
# pool = mp.Pool(processes=num_splits)
# results = []
# for i in range(num_splits):
# data = articles[i*split:(i+1)*split] if i < num_splits-1 else articles[i*split:]
# ids = ids[i*split:(i+1)*split] if i < num_splits - 1 else ids[i*split:]
# results.append(pool.apply_async(paraphrase, args=(aug, data, ids)))
# results = [pool.apply_async(paraphrase, args=(aug, articles[i*split:(i+1)*split] if i < num_splits-1 else articles[i*split:], ids[i*split:(i+1)*split] if i < num_splits-1 else ids[i*split:])) for i in range(num_splits)]
# outputs = [p.get() for p in results]
# for x in outputs:
# d = {**d, **x}
pickle.dump(d, open('ppdb_paraphrase.pkl', 'wb'))
def augment_text(self, data):
op = random.choice(self.all_transform)
# use specified operation magnitude if avalible
if isinstance(op, tuple):
op, scale = op
else:
scale = random.uniform(0, self.max_strength)
if op == "identity":
return data
elif op == "syn_replacement":
op = naw.SynonymAug(aug_src="wordnet", aug_p=scale, aug_max=None)
elif op == "random_swap":
op = naw.RandomWordAug(action="swap", aug_p=scale, aug_max=None)
elif op == "random_delete":
op = naw.RandomWordAug(action="delete", aug_p=scale, aug_max=None)
elif op == "insert_punc":
op = InsertPunctuation() # scale will be randomized inside function
else:
raise NotImplementedError
return op.augment(data)
def augment_n(data, N=1):
pbar = tqdm(desc='Augmenting Data N={}'.format(N),
total=data.shape[0],
leave=False)
# random synonym replacement
# aug = naw.SynonymAug(aug_max=4, stopwords=stop_words())
aug = naf.Sequential([
# naw.ContextualWordEmbsAug(
# 'bert-base-uncased',
# aug_max=5,
# stopwords=stop_words(),
# device='cuda',
# optimize=True
# ),
naw.ContextualWordEmbsAug('bert-base-uncased',
aug_max=3,
stopwords=stop_words(),
device='cuda',
optimize=True,
action='insert'),
naw.SynonymAug(aug_max=4, stopwords=stop_words())
])
results = []
for row in data:
t, s = augment(row[1], row[2], aug, N)
augs = []
for j, t in enumerate(t):
augs.append([row[0] + str(j), t, s[j], row[3]])
if len(augs) > 0:
results.append(np.array(augs))
pbar.update()
results.append(data)
pbar.clear()
pbar.close()
return np.concatenate(results, axis=0)
def get_augmenter(method: str, stopwords: List[str] = None) -> naw.SynonymAug:
"""
Initialize an augmenter depending on the given method.
Parameters
----------
method : str (supported methods: wordnet_synonym and aug_sub_bert)
stopwords : list
list of words to freeze throughout the augmentation
Returns
-------
Initialized nlpaug augmenter
"""
if method == 'wordnet_synonym':
return naw.SynonymAug(aug_src='wordnet', stopwords=stopwords)
if method == 'aug_sub_bert':
return naw.ContextualWordEmbsAug(model_path='bert-base-uncased',
action="substitute",
stopwords=stopwords)
raise UnavailableAugmenter(
'The given augmenter is not supported. You must choose one \
of the following: wordnet_synonym or aug_sub_bert')
def word_substitution(text, aug_src='wordnet'):
aug = naw.SynonymAug(aug_src=aug_src)
augmented_text = aug.augment(text)
return augmented_text
def augment(dataset_path,
factor,
balance_aware=False,
alpha=0.7,
beta=1,
verbose=False):
"""Augments a dataset in place using nlpaug.
Args:
dataset (string): Path to training set.
factor (int): Factor by which to augment training set size.
balance_aware (bool): Whether to use balance-aware data augmentation (not fully tested yet).
alpha (float): Alpha parameter in balance-aware data augmentation.
beta (float): Beta parameter in balance-aware data augmentation.
verbose (bool, optional): Verbose output. Defaults to False.
"""
logging.basicConfig(level=logging.DEBUG,
format="[%(asctime)s:%(name)s] %(message)s")
logger = logging.getLogger("augment")
if not os.path.exists(dataset_path):
if verbose:
logger.info(
f'Skipping training set augmentation, for {dataset_path} not found.'
)
return
if factor < 2:
if verbose:
logger.info(
f'Skipping training set augmentation, for factor is {factor} < 2.'
)
return
if verbose:
logger.info(f"Begin training set augmentation.")
aug = naw.SynonymAug(aug_src='wordnet')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
golden = []
with open(dataset_path) as fin:
for line in fin:
data = json.loads(line)
golden.append(data)
label_count = get_label_count(golden)
if balance_aware:
min_rareness, max_rareness, spread_factor = analyze_for_balance_awareness(
golden, alpha, beta)
if verbose:
logger.info(
f"Analyzing papers in {dataset_path} for balance-aware data augmentation."
)
logger.info(f"Minimum rareness score is {min_rareness}.")
logger.info(f"Maximum rareness score is {max_rareness}.")
logger.info(f"This allows a spread factor of {spread_factor}.")
if verbose:
logger.info(
f"Augmenting dataset at {dataset_path} with {len(golden)} examples."
)
with open(dataset_path, 'w') as fout:
for epoch in (tqdm(range(factor), desc='Full augmentation progress')
if verbose else range(factor)):
for js in (tqdm(golden, desc='Per epoch progress', leave=False)
if verbose else golden):
if epoch == 0:
fout.write(json.dumps(js) + '\n')
else:
if balance_aware:
relative_rareness = floor(
rareness_score(js, label_count, alpha, beta) /
min_rareness)
if relative_rareness <= epoch:
continue
title = ' '.join(js['title'])
aug_title = aug.augment(title)
abstract = ' '.join(js['abstract'])
aug_abstract = aug.augment(abstract)
aug_js = copy.deepcopy(js)
aug_js['title'] = aug_title.split()
aug_js['abstract'] = aug_abstract.split()
fout.write(json.dumps(aug_js) + '\n')
if verbose:
logger.info(f"Finish training set augmentation.")