File size: 5,653 Bytes
57c22a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | import json
import os
import tarfile
import zipfile
import gzip
import requests
from glob import glob
from itertools import chain
import gdown
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(return_validation_set: bool = False):
""" Get RelBERT training data
Returns
-------
pairs: dictionary of list (positive pairs, negative pairs)
{'1b': [[0.6, ('office', 'desk'), ..], [[-0.1, ('aaa', 'bbb'), ...]]
"""
top_n = 10
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 = {}
# 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)
if return_validation_set:
positive_pairs = positive_pairs[min(top_n, len(positive_pairs)):]
if len(positive_pairs) == 0:
continue
else:
positive_pairs = positive_pairs[:min(top_n, len(positive_pairs))]
positives_score[relation_id] = positive_pairs
positives[relation_id] = list(list(zip(*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
# 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]))
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]
return [{'relation_type': k, 'positives': pos, 'negatives': neg} for k, (pos, neg) in pairs.items()]
if __name__ == '__main__':
data_train = get_training_data(return_validation_set=False)
with open('dataset/train.jsonl', 'w') as f_writer:
f_writer.write('\n'.join([json.dumps(i) for i in data_train]))
data_valid = get_training_data(return_validation_set=True)
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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