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import json
import os
import tarfile
import zipfile
import gzip
import requests
from itertools import chain
from glob import glob
import gdown
from datasets import load_dataset
k = 10 # the 3rd level negative-distance ranking
m = 5 # the 3rd level negative-distance ranking
top_n = 10 # threshold of positive pairs in the 1st and 2nd relation
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 = {}
positives_limit = {}
all_relation_type = {}
# 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)
positives[relation_id] = list(list(zip(*positive_pairs))[1])
positives_limit[relation_id] = list(list(zip(*positive_pairs[:min(top_n, len(positive_pairs))]))[1])
negatives[relation_id] = [tuple(p.split(':')) for s, p in filter(lambda _x: _x[0] < 0, scales)]
all_relation_type[relation_id] = relation_type
parent = list(set([i[:-1] for i in all_relation_type.keys()]))
# 1st level relation contrast (among parent relations)
relation_pairs_1st = []
relation_pairs_1st_validation = []
for p in parent:
child_positive = list(filter(lambda x: x.startswith(p), list(all_relation_type.keys())))
child_negative = list(filter(lambda x: not x.startswith(p), list(all_relation_type.keys())))
positive_pairs = []
negative_pairs = []
for c in child_positive:
positive_pairs += positives_limit[c]
for c in child_negative:
negative_pairs += positives_limit[c]
relation_pairs_1st += [{
"positives": positive_pairs, "negatives": negative_pairs, "relation_type": p, "level": "parent"
}]
# 2nd level relation contrast (among child relations) & 3rd level relation contrast (within child relations)
relation_pairs_2nd = []
relation_pairs_2nd_validation = []
for p in all_relation_type.keys():
positive_pairs = positives_limit[p]
negative_pairs = []
for n in all_relation_type.keys():
if p == n:
continue
negative_pairs += positives[n]
relation_pairs_2nd += [{
"positives": positive_pairs, "negatives": negative_pairs, "relation_type": p, "level": "child"
}]
relation_pairs_3rd = []
for p in all_relation_type.keys():
positive_pairs = positives[p]
negative_pairs = positive_pairs + negatives[p]
for n, anchor in enumerate(positive_pairs):
if n > m:
continue
for _n, posi in enumerate(positive_pairs):
if n < _n and len(negative_pairs) > _n + k:
relation_pairs_3rd += [{
"positives": [(anchor, posi)],
"negatives": [(anchor, neg) for neg in negative_pairs[_n+k:]],
"relation_type": p,
"level": "child_prototypical"
}]
train = relation_pairs_1st + relation_pairs_2nd + relation_pairs_3rd
# conceptnet as the validation set
cn = load_dataset('relbert/conceptnet_high_confidence_v2')
valid = list(chain(*cn.values()))
for i in valid:
i['level'] = 'N/A'
return train, valid
if __name__ == '__main__':
data_train, data_validation = get_training_data()
print(f"- training data : {len(data_train)}")
print(f"- validation data : {len(data_validation)}")
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_validation]))
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