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prepro.py
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from tqdm import tqdm
import ujson as json
import numpy as np
import unidecode
cdr_rel2id = {'1:NR:2': 0, '1:CID:2': 1}
gda_rel2id = {'1:NR:2': 0, '1:GDA:2': 1}
biored_rel2id = {'1:NR:2': 0, '1:Association:2': 1, '1:Bind:2': 2, '1:Comparison:2': 3, '1:Conversion:2': 4,
'1:Cotreatment:2': 5, '1:Drug_Interaction:2': 6, '1:Negative_Correlation:2': 7, '1:Positive_Correlation:2': 8}
def chunks(l, n):
res = []
for i in range(0, len(l), n):
assert len(l[i:i + n]) == n
res += [l[i:i + n]]
return res
def read_cdr(file_in, tokenizer, max_seq_length=1024):
pmids = set()
features = []
# ent_nums = 0
# rel_nums = 0
maxlen = 0
with open(file_in, 'r') as infile:
lines = infile.readlines()
# lines = lines[:10]
for i_l, line in enumerate(tqdm(lines)):
line = line.rstrip().split('\t')
pmid = line[0]
if pmid not in pmids:
pmids.add(pmid)
text = line[1]
prs = chunks(line[2:], 17)
ent2idx = {}
train_triples = {}
entity_pos = set()
for p in prs:
es = list(map(int, p[8].split(':')))
ed = list(map(int, p[9].split(':')))
tpy = p[7]
for start, end in zip(es, ed):
entity_pos.add((start, end, tpy))
es = list(map(int, p[14].split(':')))
ed = list(map(int, p[15].split(':')))
tpy = p[13]
for start, end in zip(es, ed):
entity_pos.add((start, end, tpy))
sents = [t.split(' ') for t in text.split('|')]
new_sents = []
sent_map = {}
i_t = 0
for sent in sents:
for token in sent:
tokens_wordpiece = tokenizer.tokenize(token)
for start, end, tpy in list(entity_pos):
if i_t == start:
tokens_wordpiece = ["*"] + tokens_wordpiece
if i_t + 1 == end:
tokens_wordpiece = tokens_wordpiece + ["*"]
sent_map[i_t] = len(new_sents)
new_sents.extend(tokens_wordpiece)
i_t += 1
sent_map[i_t] = len(new_sents)
sents = new_sents
entity_pos = []
for p in prs:
if p[0] == "not_include":
continue
if p[1] == "L2R":
h_id, t_id = p[5], p[11]
h_start, t_start = p[8], p[14]
h_end, t_end = p[9], p[15]
else:
t_id, h_id = p[5], p[11]
t_start, h_start = p[8], p[14]
t_end, h_end = p[9], p[15]
h_start = map(int, h_start.split(':'))
h_end = map(int, h_end.split(':'))
t_start = map(int, t_start.split(':'))
t_end = map(int, t_end.split(':'))
h_start = [sent_map[idx] for idx in h_start]
h_end = [sent_map[idx] for idx in h_end]
t_start = [sent_map[idx] for idx in t_start]
t_end = [sent_map[idx] for idx in t_end]
if h_id not in ent2idx:
ent2idx[h_id] = len(ent2idx)
entity_pos.append(list(zip(h_start, h_end)))
if t_id not in ent2idx:
ent2idx[t_id] = len(ent2idx)
entity_pos.append(list(zip(t_start, t_end)))
h_id, t_id = ent2idx[h_id], ent2idx[t_id]
r = cdr_rel2id[p[0]]
if (h_id, t_id) not in train_triples:
train_triples[(h_id, t_id)] = [{'relation': r}]
# train_triples[(t_id, h_id)] = [{'relation': r}]
else:
train_triples[(h_id, t_id)].append({'relation': r})
# train_triples[(t_id, h_id)].append({'relation': r})
relations, hts = [], []
# for h, t in train_triples.keys():
# relation = [0] * len(cdr_rel2id)
# for mention in train_triples[h, t]:
# relation[mention["relation"]] = 1
# relations.append(relation)
# hts.append([h, t])
for h in range(len(entity_pos)):
for t in range(len(entity_pos)):
if (h, t) in train_triples.keys():
relation = [0] * len(cdr_rel2id)
for mention in train_triples[h, t]:
relation[mention["relation"]] = 1
# evidence = mention["evidence"]
relations.append(relation)
hts.append([h, t])
# pos_samples += 1
elif (h, t) not in train_triples.keys():
relation = [1] + [0] * (len(cdr_rel2id) - 1)
relations.append(relation)
hts.append([h, t])
# neg_samples += 1
assert len(relations) == len(entity_pos) * len(entity_pos)
maxlen = max(maxlen, len(sents))
sents = sents[:max_seq_length - 2]
input_ids = tokenizer.convert_tokens_to_ids(sents)
input_ids = tokenizer.build_inputs_with_special_tokens(input_ids)
if len(hts) > 0:
feature = {'input_ids': input_ids,
'entity_pos': entity_pos,
'labels': relations,
'hts': hts,
'title': pmid,
}
features.append(feature)
print("Number of documents: {}.".format(len(features)))
print("Max document length: {}.".format(maxlen))
# print("Avg. Number of entities per doc: {}.".format(maxlen))
# print("Avg. Number of relations per doc: {}.".format(maxlen))
return features
def read_gda(file_in, tokenizer, max_seq_length=1024):
pmids = set()
features = []
maxlen = 0
with open(file_in, 'r') as infile:
lines = infile.readlines()
for i_l, line in enumerate(tqdm(lines)):
line = line.rstrip().split('\t')
pmid = line[0]
if pmid not in pmids:
pmids.add(pmid)
text = line[1]
prs = chunks(line[2:], 17)
ent2idx = {}
train_triples = {}
entity_pos = set()
for p in prs:
es = list(map(int, p[8].split(':')))
ed = list(map(int, p[9].split(':')))
tpy = p[7]
for start, end in zip(es, ed):
entity_pos.add((start, end, tpy))
es = list(map(int, p[14].split(':')))
ed = list(map(int, p[15].split(':')))
tpy = p[13]
for start, end in zip(es, ed):
entity_pos.add((start, end, tpy))
sents = [t.split(' ') for t in text.split('|')]
new_sents = []
sent_map = {}
i_t = 0
for sent in sents:
for token in sent:
tokens_wordpiece = tokenizer.tokenize(token)
for start, end, tpy in list(entity_pos):
if i_t == start:
tokens_wordpiece = ["*"] + tokens_wordpiece
if i_t + 1 == end:
tokens_wordpiece = tokens_wordpiece + ["*"]
sent_map[i_t] = len(new_sents)
new_sents.extend(tokens_wordpiece)
i_t += 1
sent_map[i_t] = len(new_sents)
sents = new_sents
entity_pos = []
for p in prs:
if p[0] == "not_include":
continue
if p[1] == "L2R":
h_id, t_id = p[5], p[11]
h_start, t_start = p[8], p[14]
h_end, t_end = p[9], p[15]
else:
t_id, h_id = p[5], p[11]
t_start, h_start = p[8], p[14]
t_end, h_end = p[9], p[15]
h_start = map(int, h_start.split(':'))
h_end = map(int, h_end.split(':'))
t_start = map(int, t_start.split(':'))
t_end = map(int, t_end.split(':'))
h_start = [sent_map[idx] for idx in h_start]
h_end = [sent_map[idx] for idx in h_end]
t_start = [sent_map[idx] for idx in t_start]
t_end = [sent_map[idx] for idx in t_end]
if h_id not in ent2idx:
ent2idx[h_id] = len(ent2idx)
entity_pos.append(list(zip(h_start, h_end)))
if t_id not in ent2idx:
ent2idx[t_id] = len(ent2idx)
entity_pos.append(list(zip(t_start, t_end)))
h_id, t_id = ent2idx[h_id], ent2idx[t_id]
r = gda_rel2id[p[0]]
if (h_id, t_id) not in train_triples:
train_triples[(h_id, t_id)] = [{'relation': r}]
else:
train_triples[(h_id, t_id)].append({'relation': r})
relations, hts = [], []
# for h, t in train_triples.keys():
# relation = [0] * len(gda_rel2id)
# for mention in train_triples[h, t]:
# relation[mention["relation"]] = 1
# relations.append(relation)
# hts.append([h, t])
for h in range(len(entity_pos)):
for t in range(len(entity_pos)):
if (h, t) in train_triples.keys():
relation = [0] * len(cdr_rel2id)
for mention in train_triples[h, t]:
relation[mention["relation"]] = 1
# evidence = mention["evidence"]
relations.append(relation)
hts.append([h, t])
# pos_samples += 1
elif (h, t) not in train_triples.keys():
relation = [1] + [0] * (len(cdr_rel2id) - 1)
relations.append(relation)
hts.append([h, t])
# neg_samples += 1
assert len(relations) == len(entity_pos) * len(entity_pos)
maxlen = max(maxlen, len(sents))
sents = sents[:max_seq_length - 2]
input_ids = tokenizer.convert_tokens_to_ids(sents)
input_ids = tokenizer.build_inputs_with_special_tokens(input_ids)
if len(hts) > 0:
feature = {'input_ids': input_ids,
'entity_pos': entity_pos,
'labels': relations,
'hts': hts,
'title': pmid,
}
features.append(feature)
print("Number of documents: {}.".format(len(features)))
print("Max document length: {}.".format(maxlen))
# print("# ents per doc", 1. * ent_nums / len(features))
# print("# rels per doc", 1. * rel_nums / len(features))
return features
def read_biored(file_in, tokenizer, max_seq_length=1024):
pmids = set()
features = []
maxlen = 0
with open(file_in, 'r') as infile:
lines = infile.readlines()
# lines = lines[:10]
for i_l, line in enumerate(tqdm(lines)):
line = line.rstrip().split('\t')
pmid = line[0]
if pmid not in pmids:
pmids.add(pmid)
text = line[1]
prs = chunks(line[2:], 17)
ent2idx = {}
train_triples = {}
entity_pos = set()
for p in prs:
# if p[0] == "not_include" or p[0] == "1:AMB:2":
# continue
es = list(map(int, p[8].split(':')))
ed = list(map(int, p[9].split(':')))
tpy = p[7]
for start, end in zip(es, ed):
entity_pos.add((start, end, tpy))
es = list(map(int, p[14].split(':')))
ed = list(map(int, p[15].split(':')))
tpy = p[13]
for start, end in zip(es, ed):
entity_pos.add((start, end, tpy))
sents = [t.split(' ') for t in text.split('|')]
new_sents = []
sent_map = {}
i_t = 0
for sent in sents:
for token in sent:
tokens_wordpiece = tokenizer.tokenize(token)
for start, end, tpy in list(entity_pos):
if i_t == start:
tokens_wordpiece = ["*"] + tokens_wordpiece
if i_t + 1 == end:
tokens_wordpiece = tokens_wordpiece + ["*"]
sent_map[i_t] = len(new_sents)
new_sents.extend(tokens_wordpiece)
i_t += 1
sent_map[i_t] = len(new_sents)
sents = new_sents
entity_pos = []
for p in prs:
if p[0] == "not_include":
# if p[0] == "not_include" or p[0] == "1:AMB:2":
continue
if p[1] == "L2R":
h_id, t_id = p[5], p[11]
h_start, t_start = p[8], p[14]
h_end, t_end = p[9], p[15]
else:
t_id, h_id = p[5], p[11]
t_start, h_start = p[8], p[14]
t_end, h_end = p[9], p[15]
h_start = map(int, h_start.split(':'))
h_end = map(int, h_end.split(':'))
t_start = map(int, t_start.split(':'))
t_end = map(int, t_end.split(':'))
h_start = [sent_map[idx] for idx in h_start]
h_end = [sent_map[idx] for idx in h_end]
t_start = [sent_map[idx] for idx in t_start]
t_end = [sent_map[idx] for idx in t_end]
if h_id not in ent2idx:
ent2idx[h_id] = len(ent2idx)
entity_pos.append(list(zip(h_start, h_end)))
if t_id not in ent2idx:
ent2idx[t_id] = len(ent2idx)
entity_pos.append(list(zip(t_start, t_end)))
h_id, t_id = ent2idx[h_id], ent2idx[t_id]
# r = renet2_rel2id[p[0]]
r = biored_rel2id[p[0]]
if (h_id, t_id) not in train_triples:
train_triples[(h_id, t_id)] = [{'relation': r}]
else:
train_triples[(h_id, t_id)].append({'relation': r})
relations, hts = [], []
# for h, t in train_triples.keys():
# relation = [0] * len(biored_rel2id)
# for mention in train_triples[h, t]:
# relation[mention["relation"]] = 1
# relations.append(relation)
# hts.append([h, t])
for h in range(len(entity_pos)):
for t in range(len(entity_pos)):
if (h, t) in train_triples.keys():
relation = [0] * len(biored_rel2id)
for mention in train_triples[h, t]:
relation[mention["relation"]] = 1
# evidence = mention["evidence"]
relations.append(relation)
hts.append([h, t])
# pos_samples += 1
elif (h, t) not in train_triples.keys():
relation = [1] + [0] * (len(biored_rel2id) - 1)
relations.append(relation)
hts.append([h, t])
# neg_samples += 1
assert len(relations) == len(entity_pos) * len(entity_pos)
maxlen = max(maxlen, len(sents))
sents = sents[:max_seq_length - 2]
input_ids = tokenizer.convert_tokens_to_ids(sents)
input_ids = tokenizer.build_inputs_with_special_tokens(input_ids)
if len(hts) > 0:
feature = {'input_ids': input_ids,
'entity_pos': entity_pos,
'labels': relations,
'hts': hts,
'title': pmid,
}
features.append(feature)
print("Number of documents: {}.".format(len(features)))
print("Max document length: {}.".format(maxlen))
return features