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import json
import os
import tarfile
import zipfile
import gzip
import requests
from random import shuffle, seed
from glob import glob
from itertools import chain
import gdown
validation_ratio = 0.2
top_n = 10
def wget(url, cache_dir: str = './cache', gdrive_filename: str = None):
""" wget and uncompress data_iterator """
os.makedirs(cache_dir, exist_ok=True)
if url.startswith('https://drive.google.com'):
assert gdrive_filename is not None, 'please provide fileaname for gdrive download'
gdown.download(url, f'{cache_dir}/{gdrive_filename}', quiet=False)
filename = gdrive_filename
else:
filename = os.path.basename(url)
with open(f'{cache_dir}/{filename}', "wb") as f:
r = requests.get(url)
f.write(r.content)
path = f'{cache_dir}/{filename}'
if path.endswith('.tar.gz') or path.endswith('.tgz') or path.endswith('.tar'):
if path.endswith('.tar'):
tar = tarfile.open(path)
else:
tar = tarfile.open(path, "r:gz")
tar.extractall(cache_dir)
tar.close()
os.remove(path)
elif path.endswith('.zip'):
with zipfile.ZipFile(path, 'r') as zip_ref:
zip_ref.extractall(cache_dir)
os.remove(path)
elif path.endswith('.gz'):
with gzip.open(path, 'rb') as f:
with open(path.replace('.gz', ''), 'wb') as f_write:
f_write.write(f.read())
os.remove(path)
def get_training_data():
""" Get RelBERT training data
Returns
-------
pairs: dictionary of list (positive pairs, negative pairs)
{'1b': [[0.6, ('office', 'desk'), ..], [[-0.1, ('aaa', 'bbb'), ...]]
"""
cache_dir = 'cache'
os.makedirs(cache_dir, exist_ok=True)
remove_relation = None
path_answer = f'{cache_dir}/Phase2Answers'
path_scale = f'{cache_dir}/Phase2AnswersScaled'
url = 'https://drive.google.com/u/0/uc?id=0BzcZKTSeYL8VYWtHVmxUR3FyUmc&export=download'
filename = 'SemEval-2012-Platinum-Ratings.tar.gz'
if not (os.path.exists(path_scale) and os.path.exists(path_answer)):
wget(url, gdrive_filename=filename, cache_dir=cache_dir)
files_answer = [os.path.basename(i) for i in glob(f'{path_answer}/*.txt')]
files_scale = [os.path.basename(i) for i in glob(f'{path_scale}/*.txt')]
assert files_answer == files_scale, f'files are not matched: {files_scale} vs {files_answer}'
positives = {}
negatives = {}
all_relation_type = {}
positives_score = {}
seed(42)
# score_range = [90.0, 88.7] # the absolute value of max/min prototypicality rating
for i in files_scale:
relation_id = i.split('-')[-1].replace('.txt', '')
if remove_relation and int(relation_id[:-1]) in remove_relation:
continue
with open(f'{path_answer}/{i}', 'r') as f:
lines_answer = [_l.replace('"', '').split('\t') for _l in f.read().split('\n')
if not _l.startswith('#') and len(_l)]
relation_type = list(set(list(zip(*lines_answer))[-1]))
assert len(relation_type) == 1, relation_type
relation_type = relation_type[0]
with open(f'{path_scale}/{i}', 'r') as f:
# list of tuple [score, ("a", "b")]
scales = [[float(_l[:5]), _l[6:].replace('"', '')] for _l in f.read().split('\n')
if not _l.startswith('#') and len(_l)]
scales = sorted(scales, key=lambda _x: _x[0])
# positive pairs are in the reverse order of prototypicality score
positive_pairs = [[s, tuple(p.split(':'))] for s, p in filter(lambda _x: _x[0] > 0, scales)]
positive_pairs = sorted(positive_pairs, key=lambda x: x[0], reverse=True)
positive_pairs = positive_pairs[:min(top_n, len(positive_pairs))]
shuffle(positive_pairs)
positives_score[relation_id] = positive_pairs
positives[relation_id] = list(list(zip(*positive_pairs))[1])
negative_pairs = [tuple(p.split(':')) for s, p in filter(lambda _x: _x[0] < 0, scales)]
shuffle(negative_pairs)
negatives[relation_id] = negative_pairs
all_relation_type[relation_id] = relation_type
# consider positive from other relation as negative
for k in positives.keys():
negatives[k] += list(chain(*[_v for _k, _v in positives.items() if _k != k]))
# split train & validation
positives_valid = {k: v[:int(len(v) * validation_ratio)] for k, v in positives.items()}
positives_train = {k: v[int(len(v) * validation_ratio):] for k, v in positives.items()}
negatives_valid = {k: v[:int(len(v) * validation_ratio)] for k, v in negatives.items()}
negatives_train = {k: v[int(len(v) * validation_ratio):] for k, v in negatives.items()}
positives_score_valid = {k: v[:int(len(v) * validation_ratio)] for k, v in positives_score.items()}
positives_score_train = {k: v[int(len(v) * validation_ratio):] for k, v in positives_score.items()}
outputs = []
for positives, negatives, positives_score in zip(
[positives_train, positives_valid],
[negatives_train, negatives_valid],
[positives_score_train, positives_score_valid]):
pairs = {k: [positives[k], negatives[k]] for k in positives.keys()}
parent = list(set([i[:-1] for i in all_relation_type.keys()]))
relation_structure = {p: [i for i in all_relation_type.keys() if p == i[:-1]] for p in parent}
for k, v in relation_structure.items():
positive = list(chain(*[positives_score[_v] for _v in v]))
positive = list(list(zip(*sorted(positive, key=lambda x: x[0], reverse=True)))[1])
negative = []
for _k, _v in relation_structure.items():
if _k != k:
negative += list(chain(*[positives[__v] for __v in _v]))
pairs[k] = [positive, negative]
outputs.append([{'relation_type': k, 'positives': pos, 'negatives': neg} for k, (pos, neg) in pairs.items()])
return outputs
if __name__ == '__main__':
data_train, data_valid = get_training_data()
with open('dataset/train.jsonl', 'w') as f_writer:
f_writer.write('\n'.join([json.dumps(i) for i in data_train]))
with open('dataset/valid.jsonl', 'w') as f_writer:
f_writer.write('\n'.join([json.dumps(i) for i in data_valid]))
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