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prepare_fold_data.py
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prepare_fold_data.py
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import argparse
from collections import Counter
from pydatagrand.configs.base import config
from pydatagrand.common.tools import logger
from pydatagrand.common.tools import init_logger
from pydatagrand.common.tools import save_pickle
from sklearn.model_selection import StratifiedKFold
def data_aug1(data):
new_data = []
i = 0
for line in data:
tags = [x.split("-")[1] for x in line['tag'].split(" ") if "-" in x]
tags = list(set(tags))
if ('b' in tags or 'a' in tags) and 'c' in tags:
c_ = []
t_ = []
context = line['context'].split(" ")
raw_tags = line['tag'].split(" ")
for c, t in zip(context, raw_tags):
if 'c' in t:
continue
c_.append(c)
t_.append(t)
if i <= 5:
logger.info("--------- data aug1 -----------")
logger.info(f"raw: {line['context']}")
logger.info(f'new: {" ".join(c_)}')
logger.info(f"raw_tag: {line['tag']}")
logger.info(f'tag: {" ".join(t_)}')
i += 1
new_data.append({"context": " ".join(c_),
"tag": " ".join(t_),
'id': line['id'],
'raw_context': line['raw_context']})
else:
continue
logger.info(f"data aug size: {len(new_data)}")
return new_data
def data_aug2(data):
new_data = []
i = 0
for line in data:
tags = [x.split("-")[1] for x in line['tag'].split(" ") if "-" in x]
tags = list(set(tags))
if 'b' in tags and 'a' in tags:
c_ = []
t_ = []
context = line['context'].split(" ")
raw_tags = line['tag'].split(" ")
for c, t in zip(context, raw_tags):
if 'c' in t or 'b' in t:
continue
c_.append(c)
t_.append(t)
if i <= 2:
logger.info("--------- data aug2 -----------")
logger.info(f"raw: {line['context']}")
logger.info(f'new: {" ".join(c_)}')
logger.info(f"raw_tag: {line['tag']}")
logger.info(f'tag: {" ".join(t_)}')
i += 1
new_data.append({"context": " ".join(c_),
"tag": " ".join(t_),
'id': line['id'],
'raw_context': line['raw_context']})
else:
continue
logger.info(f"data2 aug size: {len(new_data)}")
return new_data
def data_aug3(data):
new_data = []
i = 0
for line in data:
tags = [x.split("-")[1] for x in line['tag'].split(" ") if "-" in x]
tags = list(set(tags))
if 'b' in tags and 'a' in tags and 'c' in tags:
c_1 = []
t_1 = []
c_2 = []
t_2 = []
context = line['context'].split(" ")
raw_tags = line['tag'].split(" ")
for c, t in zip(context, raw_tags):
if 'a' in t :
continue
c_1.append(c)
t_1.append(t)
for c, t in zip(context, raw_tags):
if 'b' in t:
continue
c_2.append(c)
t_2.append(t)
if i <= 2:
logger.info("--------- data aug3 -----------")
logger.info(f"raw: {line['context']}")
logger.info(f'new: {" ".join(c_1)}')
logger.info(f"raw_tag: {line['tag']}")
logger.info(f'tag: {" ".join(t_1)}')
i += 1
new_data.append({"context": " ".join(c_1),
"tag": " ".join(t_1),
'id': line['id'],
'raw_context': line['raw_context']})
new_data.append({"context": " ".join(c_2),
"tag": " ".join(t_2),
'id': line['id'],
'raw_context': line['raw_context']})
else:
continue
logger.info(f"data3 aug size: {len(new_data)}")
return new_data
def make_folds(args):
train = []
train_path = config['data_dir'] / 'train.txt'
with open(str(train_path), 'r') as fr:
idx = 0
for line in fr:
json_d = {}
line = line.strip("\n")
context = []
tags = []
lines = line.split(" ")
for seg in lines:
segs = seg.split("/")
seg_text = segs[0].split("_")
seg_label = segs[1]
context.extend(seg_text)
if seg_label == 'o':
tags.extend(["O"] * len(seg_text))
elif len(seg_text) == 1:
tags.extend([f"S-{seg_label}"])
else:
head_label = f"B-{seg_label}"
tags.extend([head_label])
tags.extend([f"I-{seg_label}"] * (len(seg_text) - 1))
json_d['id'] = idx
json_d['context'] = " ".join(context)
json_d['tag'] = " ".join(tags)
json_d['raw_context'] = line
la = [x.split("-")[1] for x in tags if '-' in x]
la = list(set(la))
if len(la) == 0:
y = 0
elif len(la) == 3:
y = 4
elif len(la) == 2:
if 'a' in la and 'b' in la:
y = 1
if 'a' in la and 'c' in la:
y = 2
if 'b' in la and 'c' in la:
y = 3
elif len(la) == 1:
if la[0] == 'a':
y = 5
if la[0] == 'b':
y = 6
if la[0] == 'c':
y = 7
else:
raise ValueError("tag is error")
json_d['y'] = y
idx += 1
train.append(json_d)
y_counter = Counter()
y_counter.update([x['y'] for x in train])
print(y_counter)
X = train
y = [d['y'] for d in train]
sss = StratifiedKFold(n_splits=args.folds, random_state=args.seed, shuffle=True)
for fold, (train_index, test_index) in enumerate(sss.split(X, y)):
logger.info(f'fold-{fold} info:')
logger.info(f'raw train data size: {len(train_index)}')
logger.info(f'raw valid data size: {len(test_index)}')
X_train = [X[i] for i in train_index]
if args.do_aug:
new_data1 = data_aug1(X_train)
new_data2 = data_aug2(X_train)
new_data3 = data_aug3(X_train)
X_train.extend(new_data1)
X_train.extend(new_data2)
X_train.extend(new_data3)
logger.info(f"After data augmentation, train data size: {len(X_train)}")
X_test = [X[i] for i in test_index]
train_file_name = f'{args.data_name}_train_fold_{fold}.pkl'
dev_file_name = f'{args.data_name}_valid_fold_{fold}.pkl'
save_pickle(X_train, file_path=config['data_dir'] / train_file_name)
save_pickle(X_test, file_path=config['data_dir'] / dev_file_name)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--folds', type=int, default=5)
parser.add_argument('--seed', default=42, type=int)
parser.add_argument('--do_aug', action='store_true')
parser.add_argument('--data_name',default='datagrand',type=str)
args = parser.parse_args()
init_logger(log_file=config['log_dir'] / 'prepare_fold_data.log')
make_folds(args)
if __name__ == "__main__":
main()