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util.py
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import sys
import os
import random
import torch
import numpy as np
import re
import json
import torch.nn.functional as F
from collections import defaultdict
from dataprocess import get_data_loader, get_data_loader_unlable
import hashlib
def set_seed(config, seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if config['n_gpu'] > 0 and torch.cuda.is_available() and config['use_gpu']:
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def readtrain(filename):
f = open(filename,'r')
res = {}
while True:
line = f.readline().strip()
if not line:
break
content = line.split("\t")
#if len(content) != 7:
if len(content) != 9:
print("error!!!")
exit -1
else:
rel = int(content[0])
if rel not in res:
res[rel] = []
res[rel].append(line)
else:
res[rel].append(line)
f.close()
return res
def transtonpy(data,tokenizer):
for sample in data:
tokens = tokenizer.tokenize(sample[2])
max_length = 128
length = min(len(tokens), max_length)
tokens = tokenizer.convert_tokens_to_ids(tokens, unk_id=tokenizer.vocab['[UNK]'])
if (len(tokens) > max_length):
tokens = tokens[:max_length]
sample[2] = tokens
sample.append(length)
return np.asarray(data)
def cotinualfewshotpreprocess(config,tokenizer):
rel_index = np.load("data/fewrel/rel_index.npy")
#rel_cluster_label = np.load("rel_cluster_label.npy")
alltraindata = readtrain("data/fewrel/train_all.txt")
print(len(alltraindata))
allrel = []
for i in alltraindata.keys():
allrel.append(i)
print(i, "\t", len(alltraindata[i]))
print(len(allrel))
alltestdata = readtrain("data/fewrel/test.txt")
print(len(alltestdata))
for i in alltestdata.keys():
print(i, "\t", len(alltestdata[i]))
samplenum = 1
basenum = 10
datanumforbaserel = 100
allnum = len(allrel)
waysnum = 10
howmanyways = (allnum - basenum) // waysnum
shotnum = 100
filenum = 9999
numforeverytestrel = 100
for i in range(samplenum):
####sample basenum base relations
sample_list = random.sample(allrel, basenum)
#sample_list = [6, 12, 14, 17, 21, 25, 49, 64, 65, 78]
print(sample_list)
####
tousetraindata = []
for k in alltraindata.keys():
if k in sample_list:
trainsamplelist = random.sample(alltraindata[k], datanumforbaserel)
#trainsamplelist = random.sample(alltraindata[k], len(alltraindata[k]))
tousetraindata.extend(trainsamplelist)
else:
trainsamplelist = random.sample(alltraindata[k], shotnum)
#trainsamplelist = random.sample(alltraindata[k], len(alltraindata[k]))
tousetraindata.extend(trainsamplelist)
random.shuffle(tousetraindata) ###train
print(len(tousetraindata))
tousetestdata = []
for k in alltestdata.keys():
testsamplelist = random.sample(alltestdata[k], numforeverytestrel)
#testsamplelist = random.sample(alltestdata[k], len(alltestdata[k]))
tousetestdata.extend(testsamplelist)
random.shuffle(tousetestdata)
print(len(tousetestdata))
print(rel_index)
print(sample_list)
newlabeltasknum = []
for k in range(allnum):
if rel_index[k] in sample_list:
newlabeltasknum.append(howmanyways)
else:
newlabeltasknum.append(-1)
print(newlabeltasknum)
###other howmanyways tasks
temptaskindex = []
for k in range(howmanyways):
for j in range(waysnum):
temptaskindex.append(k)
random.shuffle(temptaskindex)
realindex = 0
for k in range(allnum):
if newlabeltasknum[k] != -1:
continue
else:
newlabeltasknum[k] = temptaskindex[realindex]
realindex += 1
print(realindex)
print(newlabeltasknum)
newname = "data/fewrel/CFRLdatatest_10_100_10_"+str(filenum)+"/rel_cluster_label_" + str(i) + ".npy"
np.save(newname, np.asarray(newlabeltasknum))
traintxtname = "data/fewrel/CFRLdatatest_10_100_10_"+str(filenum)+"/train_" + str(i) + ".txt"
fw = open(traintxtname, "w")
for line in tousetraindata:
fw.write(line + "\n")
fw.close()
testtxtname = "data/fewrel/CFRLdatatest_10_100_10_"+str(filenum)+"/test_" + str(i) + ".txt"
fw = open(testtxtname, "w")
for line in tousetestdata:
fw.write(line + "\n")
fw.close()
trainnpyname = "data/fewrel/CFRLdatatest_10_100_10_"+str(filenum)+"/train_" + str(i) + ".npy"
saveasnpytrain = []
for l in range(0, len(tousetraindata)):
items = tousetraindata[l].split("\t")
relation_ix = int(items[0])
candidate_ixs = [int(ix) for ix in items[1].split()]
question = items[2].split('\n')[0]
saveasnpytrain.append([relation_ix, candidate_ixs, question])
# print(saveasnpytrain[0])
tosavetrain = transtonpy(saveasnpytrain, tokenizer)
np.save(trainnpyname, tosavetrain)
testnpyname = "data/fewrel/CFRLdatatest_10_100_10_"+str(filenum)+"/test_" + str(i) + ".npy"
saveasnpytest = []
for l in range(0, len(tousetestdata)):
items = tousetestdata[l].split("\t")
relation_ix = int(items[0])
candidate_ixs = [int(ix) for ix in items[1].split()]
question = items[2].split('\n')[0]
saveasnpytest.append([relation_ix, candidate_ixs, question])
tosavetest = transtonpy(saveasnpytest, tokenizer)
np.save(testnpyname, tosavetest)
newtrain1 = np.load("data/fewrel/CFRLdatatest_10_100_10_"+str(filenum)+"/train_0.npy",allow_pickle=True)
print(newtrain1.shape)
print(newtrain1[0])
def getnegfrombatch(oneindex,firstent,firstentindex,secondent,secondentindex,sentences,lengths,getnegfromnum,allnum,labels,neg_labels):
# thislabel = labels[oneindex]
###get information
thissentence = sentences[oneindex].numpy().tolist()
#print(thissentence)
thislength = lengths[oneindex]
#print(thislength)
thisfirstent = firstent[oneindex]
#print(thisfirstent)
thisfirstentindex = firstentindex[oneindex].numpy().tolist()
#print(thisfirstentindex)
headstart = thisfirstentindex[0]
#print(headstart)
headend = thisfirstentindex[-1]
#print(headend)
posheadlength = len(thisfirstentindex)
#print(posheadlength)
thissecondent = secondent[oneindex]
#print(thissecondent)
thissecondentindex = secondentindex[oneindex].numpy().tolist()
#print(thissecondentindex)
tailstart = thissecondentindex[0]
#print(tailstart)
tailend = thissecondentindex[-1]
#print(tailend)
postaillength = len(thissecondentindex)
#print(postaillength)
negres = []
lenres = []
for j in range(getnegfromnum):
touseindex = (oneindex + j + 1) % allnum
negusehead = firstent[touseindex].numpy().tolist()
negheadlength = len(negusehead)
negusetail = secondent[touseindex].numpy().tolist()
negtaillength = len(negusetail)
negsamplechangehead = thissentence[0:headstart] + negusehead + thissentence[headend + 1:]
changeheadlength = thislength - posheadlength + negheadlength
negsamplechangetail = thissentence[0:tailstart] + negusetail + thissentence[tailend + 1:]
changetaillength = thislength - postaillength + negtaillength
#######get 2
negres.append(negsamplechangehead)
lenres.append(changeheadlength)
negres.append(negsamplechangetail)
lenres.append(changetaillength)
######get 1
return np.asarray(negres),np.asarray(lenres)
def getnegfrombatchnew(oneindex,firstent,firstentindex,secondent,secondentindex,sentences,lengths,getnegfromnum,allnum,labels,neg_labels):
# thislabel = labels[oneindex]
###get information
thissentence = sentences[oneindex].numpy().tolist()
#print(thissentence)
thislength = lengths[oneindex]
#print(thislength)
thisfirstent = firstent[oneindex]
#print(thisfirstent)
thisfirstentindex = firstentindex[oneindex].numpy().tolist()
#print(thisfirstentindex)
headstart = thisfirstentindex[0]
#print(headstart)
headend = thisfirstentindex[-1]
#print(headend)
posheadlength = len(thisfirstentindex)
#print(posheadlength)
thissecondent = secondent[oneindex]
#print(thissecondent)
thissecondentindex = secondentindex[oneindex].numpy().tolist()
#print(thissecondentindex)
tailstart = thissecondentindex[0]
#print(tailstart)
tailend = thissecondentindex[-1]
#print(tailend)
postaillength = len(thissecondentindex)
#print(postaillength)
negres = []
lenres = []
for j in range(getnegfromnum):
touseindex = (oneindex + j + 1) % allnum
negusehead = firstent[touseindex].numpy().tolist()
negheadlength = len(negusehead)
negusetail = secondent[touseindex].numpy().tolist()
negtaillength = len(negusetail)
negsamplechangehead = thissentence[0:headstart] + negusehead + thissentence[headend + 1:]
changeheadlength = thislength - posheadlength + negheadlength
negsamplechangetail = thissentence[0:tailstart] + negusetail + thissentence[tailend + 1:]
changetaillength = thislength - postaillength + negtaillength
#######get 1
aa = random.randint(0,1)
if aa == 1:
negres.append(negsamplechangehead)
lenres.append(changeheadlength)
else:
negres.append(negsamplechangetail)
lenres.append(changetaillength)
return np.asarray(negres),np.asarray(lenres)
def getnegfrombatch_bert(oneindex,firstent,firstentindex,secondent,secondentindex,sentences,lengths,getnegfromnum,allnum,labels,neg_labels,config):
thissentence = sentences[oneindex].cpu().numpy().tolist()
thislength = lengths[oneindex]
thisfirstent = firstent[oneindex]
thisfirstentindex = firstentindex[oneindex].numpy().tolist()
headstart = thisfirstentindex[0]
headend = thisfirstentindex[-1]
posheadlength = len(thisfirstentindex)
thissecondent = secondent[oneindex]
thissecondentindex = secondentindex[oneindex].numpy().tolist()
tailstart = thissecondentindex[0]
tailend = thissecondentindex[-1]
postaillength = len(thissecondentindex)
negres = []
maskres = []
for j in range(getnegfromnum):
touseindex = (oneindex + j + 1) % allnum
negusehead = firstent[touseindex].numpy().tolist()
negheadlength = len(negusehead)
negusetail = secondent[touseindex].numpy().tolist()
negtaillength = len(negusetail)
negsamplechangehead = thissentence[0:headstart] + negusehead + thissentence[headend + 1:]
changeheadlength = thislength - posheadlength + negheadlength
if len(negsamplechangehead) > config["max_length"]:
negsamplechangehead = negsamplechangehead[0:config["max_length"]]
for i in range(len(negsamplechangehead), config["max_length"]):
negsamplechangehead.append(0)
mask1 = []
for i in range(0, changeheadlength):
mask1.append(1)
for i in range(changeheadlength, config["max_length"]):
mask1.append(0)
if len(mask1) > config["max_length"]:
mask1 = mask1[0:config["max_length"]]
negsamplechangetail = thissentence[0:tailstart] + negusetail + thissentence[tailend + 1:]
changetaillength = thislength - postaillength + negtaillength
if len(negsamplechangetail) > config["max_length"]:
negsamplechangetail = negsamplechangetail[0:config["max_length"]]
for i in range(len(negsamplechangetail), config["max_length"]):
negsamplechangetail.append(0)
mask2 = []
for i in range(0, changetaillength):
mask2.append(1)
for i in range(changetaillength, config["max_length"]):
mask2.append(0)
if len(mask2) > config["max_length"]:
mask2 = mask2[0:config["max_length"]]
if len(mask1) != len(mask2):
print(len(mask1))
print(len(mask2))
print(mask1)
print(mask2)
negres.append(negsamplechangehead)
maskres.append(mask1)
negres.append(negsamplechangetail)
maskres.append(mask2)
return np.asarray(negres),np.asarray(maskres)
def getnegforonerel(mem_set,key,neg_mem_data):
negusehead = mem_set[key]['1']['h'][0]
negheadlength = len(negusehead)
negusetail = mem_set[key]['1']['t'][0]
negtaillength = len(negusetail)
possen = mem_set[key]['0'][0][2] ####positive sentence tokens
poslen = mem_set[key]['0'][0][7]
poshead = mem_set[key]['0'][0][3]
posheadindex = mem_set[key]['0'][0][4]
headstart = posheadindex[0]
headend = posheadindex[-1]
posheadlength = len(posheadindex)
postail = mem_set[key]['0'][0][5]
postailindex = mem_set[key]['0'][0][6]
tailstart = postailindex[0]
tailend = postailindex[-1]
postaillength = len(postailindex)
negsamplechangehead = possen[0:headstart] + negusehead + possen[headend + 1:]
changeheadlength = poslen - posheadlength + negheadlength
negsamplechangetail = possen[0:tailstart] + negusetail + possen[tailend + 1:]
changetaillength = poslen - postaillength + negtaillength
newnegsample1 = []
newnegsample1.append(mem_set[key]['0'][0][0])
newnegsample1.append(mem_set[key]['0'][0][1])
newnegsample1.append(negsamplechangehead)
newnegsample1.append(negusehead)
newnegsample1.append(posheadindex) ####wrong index
newnegsample1.append(postail)
newnegsample1.append(postailindex)
newnegsample1.append("neghead")
newnegsample1.append("postail")
newnegsample1.append("fakesen")
newnegsample1.append(changeheadlength)
newnegsample1.append(2)
newnegsample2 = []
newnegsample2.append(mem_set[key]['0'][0][0])
newnegsample2.append(mem_set[key]['0'][0][1])
newnegsample2.append(negsamplechangetail)
newnegsample2.append(poshead)
newnegsample2.append(posheadindex)
newnegsample2.append(negusetail)
newnegsample2.append(postailindex)
newnegsample1.append("poshead")
newnegsample1.append("negtail")
newnegsample1.append("fakesen")
newnegsample2.append(changetaillength)
newnegsample2.append(2)
# print(newnegsample2)
neg_mem_data.append(np.asarray(newnegsample1))
neg_mem_data.append(np.asarray(newnegsample2))
def getposandneg(logits,logits_proto,labels,typelabels):
numofpos = 0
numofneg = 0
for index, logit in enumerate(logits):
type = typelabels[index]
if type == 1:
numofpos += 1
else:
numofneg += 1
embedlen = logits.shape[1]
tensorpos = torch.zeros((numofpos, embedlen))
protopos = torch.zeros((numofpos, embedlen))
poslabels = torch.zeros([numofpos],dtype=torch.long)
tensorneg = torch.zeros((numofneg, embedlen))
protoneg = torch.zeros((numofneg, embedlen))
neglabels = torch.zeros([numofneg],dtype=torch.long)
posindex = 0
negindex = 0
for index, logit in enumerate(logits):
type = typelabels[index]
if type == 1:
tensorpos[posindex] = logits[index]
protopos[posindex] = logits_proto[index]
poslabels[posindex] = labels[index]
posindex += 1
else:
tensorneg[negindex] = logits[index]
protoneg[negindex] = logits_proto[index]
neglabels[negindex] = labels[index]
negindex += 1
#numofpos
#numofneg
#print("numofpos:\t",numofpos,"numofneg:\t",numofneg)
return tensorpos,protopos,poslabels,tensorneg,protoneg,neglabels,numofneg
def handletoken(raw_text,h_pos_li,t_pos_li,tokenizer):
h_pattern = re.compile("\* h \*")
t_pattern = re.compile("\^ t \^")
err = 0
tokens = []
h_mention = []
t_mention = []
raw_text_list = raw_text.split(" ")
for i, token in enumerate(raw_text_list):
token = token.lower()
if i >= h_pos_li[0] and i <= h_pos_li[-1]:
if i == h_pos_li[0]:
tokens += ['*', 'h', '*']
h_mention.append(token)
continue
if i >= t_pos_li[0] and i <= t_pos_li[-1]:
if i == t_pos_li[0]:
tokens += ['^', 't', '^']
t_mention.append(token)
continue
tokens.append(token)
text = " ".join(tokens)
h_mention = " ".join(h_mention)
t_mention = " ".join(t_mention)
#print(text)
#print(h_mention)
#print(t_mention)
tokenized_text = tokenizer.tokenize(text)
tokenized_head = tokenizer.tokenize(h_mention)
tokenized_tail = tokenizer.tokenize(t_mention)
p_text = " ".join(tokenized_text)
p_head = " ".join(tokenized_head)
p_tail = " ".join(tokenized_tail)
p_text = h_pattern.sub("[unused0] " + p_head + " [unused1]", p_text)
p_text = t_pattern.sub("[unused2] " + p_tail + " [unused3]", p_text)
#print(p_text)
f_text = ("[CLS] " + p_text + " [SEP]").split()
#print(f_text)
# If h_pos_li and t_pos_li overlap, we can't find head entity or tail entity.
try:
h_pos = f_text.index("[unused0]")
except:
err += 1
h_pos = 0
try:
t_pos = f_text.index("[unused2]")
except:
err += 1
t_pos = 0
tokenized_input = tokenizer.convert_tokens_to_ids(f_text)
return tokenized_input, h_pos, t_pos
def filter_sentence(sentence):
head_pos = sentence["h"]["pos"][0]
tail_pos = sentence["t"]["pos"][0]
if sentence["h"]["name"] == sentence["t"]["name"]: # head mention equals tail mention
return True
if head_pos[0] >= tail_pos[0] and head_pos[0] <= tail_pos[-1]: # head mentioin and tail mention overlap
return True
if tail_pos[0] >= head_pos[0] and tail_pos[0] <= head_pos[-1]: # head mentioin and tail mention overlap
return True
return False
def process_data(file1,file2):
data1 = json.load(open(file1))
#data2 = json.load(open(file2))
data2 = {}
max_num = 16 ###max number for every entity pair
ent_data = defaultdict(list)
for key in data1.keys():
for sentence in data1[key]:
if filter_sentence(sentence):
continue
head = sentence["h"]["id"]
tail = sentence["t"]["id"]
newsen = sentence
#print(newsen["tokens"])
newtokens = " ".join(newsen["tokens"]).lower().split(" ")
#print(newtokens)
newsen["tokens"] = newtokens
#print(newsen)
ent_data[head + "#" + tail].append(newsen)
for key in data2.keys():
for sentence in data2[key]:
if filter_sentence(sentence):
continue
head = sentence["h"]["id"]
tail = sentence["t"]["id"]
newsen = sentence
newtokens = " ".join(newsen["tokens"]).lower().split(" ")
newsen["tokens"] = newtokens
ent_data[head + "#" + tail].append(newsen)
ll = 0
list_data = []
entpair2scope = {}
for key in ent_data.keys():
#if len(ent_data[key]) < 2:
# continue
list_data.extend(ent_data[key][0:max_num])
entpair2scope[key] = [ll, len(list_data)]
ll = len(list_data)
return list_data,entpair2scope
def select_similar_data_new(training_data,tokenizer,entpair2scope,topk,max_sen_length_for_select,list_data,config,SimModel,select_thredsold,max_sen_lstm_tokenize,enctokenizer,faissindex,ifnorm,select_num=2):
#use both methods
selectdata = []
alladdnum = 0
#md5 = hashlib.md5()
has = 0
nothas = 0
for onedata in training_data:
label = onedata[0]
text = onedata[9]
headid = onedata[7]
tailid = onedata[8]
headindex = onedata[4]
tailindex = onedata[6]
onedatatoken, onedatahead, onedatatail = handletoken(text, headindex, tailindex, tokenizer)
onedicid = headid + "#" + tailid
tmpselectnum = 0
if onedicid in entpair2scope:
#print("bbbbbbbbbbbbbbb")
has += 1
thispairnum = entpair2scope[onedicid][1] - entpair2scope[onedicid][0]
#if thispairnum > topk:
if True:
###choose topk
alldisforthispair = []
input_ids = np.zeros((thispairnum + 1, max_sen_length_for_select), dtype=int)
mask = np.zeros((thispairnum + 1, max_sen_length_for_select), dtype=int)
h_pos = np.zeros((thispairnum + 1), dtype=int)
t_pos = np.zeros((thispairnum + 1), dtype=int)
for index in range(entpair2scope[onedicid][0], entpair2scope[onedicid][1]):
oneres = list_data[index]
tokens = " ".join(oneres["tokens"])
###sentence["tokens"], [h_p[0], h_p[-1]+1], [t_p[0], t_p[-1]+1]
'''
sentence example:
{
'tokens': ['Microsoft', 'was', 'founded', 'by', 'Bill', 'Gates', '.']
'h': {'pos':[[0]], 'name': 'Microsoft', 'id': Q123456},
't': {'pos':[[4,5]], 'name': 'Bill Gates', 'id': Q2333},
'r': 'P1'
}
'''
hposstart = oneres["h"]["pos"][0][0]
hposend = oneres["h"]["pos"][0][-1]
tposstart = oneres["t"]["pos"][0][0]
tposend = oneres["t"]["pos"][0][-1]
tokenres, headpos, tailpos = handletoken(tokens, [hposstart, hposend], [tposstart, tposend],
tokenizer)
length = min(len(tokenres), max_sen_length_for_select)
input_ids[index - entpair2scope[onedicid][0]][0:length] = tokenres[0:length]
mask[index - entpair2scope[onedicid][0]][0:length] = 1
h_pos[index - entpair2scope[onedicid][0]] = min(headpos, max_sen_length_for_select - 1)
t_pos[index - entpair2scope[onedicid][0]] = min(tailpos, max_sen_length_for_select - 1)
# onedatatoken, onedatahead, onedatatail
length = min(len(onedatatoken), max_sen_length_for_select)
input_ids[thispairnum][0:length] = onedatatoken[0:length]
mask[thispairnum][0:length] = 1
h_pos[thispairnum] = min(onedatahead, max_sen_length_for_select - 1)
t_pos[thispairnum] = min(onedatatail, max_sen_length_for_select - 1)
###cal score
# print(input_ids)
# print(mask)
input_ids = torch.from_numpy(input_ids).to(config["device"])
mask = torch.from_numpy(mask).to(config["device"])
h_pos = torch.from_numpy(h_pos).to(config["device"])
t_pos = torch.from_numpy(t_pos).to(config["device"])
outputs = SimModel(input_ids, mask)
indice = torch.arange(input_ids.size()[0])
h_state = outputs[0][indice, h_pos]
t_state = outputs[0][indice, t_pos]
state = torch.cat((h_state, t_state), 1)
# print(state.shape)
query = state[thispairnum, :].view(1, state.shape[-1])
toselect = state[0:thispairnum, :].view(thispairnum, state.shape[-1])
if ifnorm:
#print("norm")
querynorm = query / query.norm(dim=1)[:, None]
toselectnorm = toselect / toselect.norm(dim=1)[:, None]
res = (querynorm * toselectnorm).sum(-1)
#print(res)
else:
res = (query * toselect).sum(-1)
# print(res)
pred = []
for i in range(res.size(0)):
pred.append((res[i], i))
pred.sort(key=lambda x: x[0], reverse=True)
# print(pred)
# print(res.shape)
# print(res)
####select from pred
selectedindex = []
tmpselectnum = 0
prescore= -100.0
for k in range(len(pred)):
thistext = " ".join(list_data[entpair2scope[onedicid][0] + pred[k][1]]["tokens"])
if thistext == text:
continue
#if tmpselectnum < topk and pred[k][0] > select_thredsold and pred[k][0] != prescore:
if tmpselectnum < topk and pred[k][0] > select_thredsold:
selectedindex.append(pred[k][1])
prescore = pred[k][0]
tmpselectnum += 1
#print("tmpselectnum: ",tmpselectnum)
for onenum in selectedindex:
onelabel = label
oneneg = [label]
onesen = " ".join(list_data[entpair2scope[onedicid][0] + onenum]["tokens"])
tokens = enctokenizer.tokenize(onesen)
length = min(len(tokens), max_sen_lstm_tokenize)
tokens = enctokenizer.convert_tokens_to_ids(tokens, unk_id=enctokenizer.vocab['[UNK]'])
if (len(tokens) > max_sen_lstm_tokenize):
tokens = tokens[:max_sen_lstm_tokenize]
fakefirstent = [554, 555]
fakefirstindex = [0, 1]
fakesecondent = [665, 666]
fakesecondindex = [3, 4]
fakeheadid = "fheadid"
faketailid = "ftailid"
fakerawtext = "fakefake"
typelabel = 1 ###positive sample
oneseldata = [onelabel, oneneg, tokens, fakefirstent, fakefirstindex, fakesecondent, fakesecondindex, fakeheadid, faketailid, fakerawtext, length, typelabel]
selectdata.append(np.asarray(oneseldata))
#selectres.append(list_data[entpair2scope[onedicid][0] + onenum])
alladdnum += tmpselectnum
#else:
#print("nothing!continue")
#continue
#print("hghagdhasdgjahsgdjahgdjahgdjasgdj")
if onedicid not in entpair2scope or tmpselectnum == 0:
#print("aaaaaaaaaa")
nothas += 1
# print("not in! use fasis")
topuse = select_num
# faissindex
input_ids = np.zeros((1, max_sen_length_for_select), dtype=int)
mask = np.zeros((1, max_sen_length_for_select), dtype=int)
h_pos = np.zeros((1), dtype=int)
t_pos = np.zeros((1), dtype=int)
length = min(len(onedatatoken), max_sen_length_for_select)
input_ids[0][0:length] = onedatatoken[0:length]
mask[0][0:length] = 1
h_pos[0] = min(onedatahead, max_sen_length_for_select - 1)
t_pos[0] = min(onedatatail, max_sen_length_for_select - 1)
input_ids = torch.from_numpy(input_ids).to(config["device"])
mask = torch.from_numpy(mask).to(config["device"])
h_pos = torch.from_numpy(h_pos).to(config["device"])
t_pos = torch.from_numpy(t_pos).to(config["device"])
outputs = SimModel(input_ids, mask)
indice = torch.arange(input_ids.size()[0])
h_state = outputs[0][indice, h_pos]
t_state = outputs[0][indice, t_pos]
state = torch.cat((h_state, t_state), 1)
# print(state.shape)
#####some problems, need normalize!!!!!!!!!!!!
if ifnorm:
state = state / state.norm(dim=1)[:, None]
########################################
query = state.view(1, state.shape[-1]).cpu().detach().numpy()
#print("<query>")
#print(query)
#print("<topuse>")
#print(topuse)
D, I = faissindex.search(query, topuse)
#print("<I>")
#print(I)
newtouse = topuse
newadd = 0
for i in range(newtouse):
thisdis = D[0][i]
#print("&&&&&&&&&&&&&&&&&&")
#print("<thisdis>")
#print(thisdis)
###whether to use this?
#if thisdis < 0.95:
# continue
newadd += 1
onenum = I[0][i]
#print("<onenum>")
#print(onenum)
onelabel = label
oneneg = [label]
onesen = " ".join(list_data[onenum]["tokens"])
###handle onesen
onesen.replace("\n\n\n", " ")
onesen.replace("\n\n", " ")
onesen.replace("\n", " ")
#print(text)
#print("********************************")
#print("<onesen>")
#print(onesen)
#print("^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^")
tokens = enctokenizer.tokenize(onesen)
#print("<tokens>")
#print(tokens)
length = min(len(tokens), max_sen_lstm_tokenize)
tokens = enctokenizer.convert_tokens_to_ids(tokens, unk_id=enctokenizer.vocab['[UNK]'])
if (len(tokens) > max_sen_lstm_tokenize):
tokens = tokens[:max_sen_lstm_tokenize]
fakefirstent = [554, 555]
fakefirstindex = [0, 1]
fakesecondent = [665, 666]
fakesecondindex = [3, 4]
fakeheadid = "fheadid"
faketailid = "ftailid"
fakerawtext = "fakefake"
typelabel = 1 ###positive sample
oneseldata = [onelabel, oneneg, tokens, fakefirstent, fakefirstindex, fakesecondent, fakesecondindex,
fakeheadid, faketailid, fakerawtext, length, typelabel]
#print("<oneseldata>")
#print(oneseldata)
selectdata.append(np.asarray(oneseldata))
alladdnum += newadd
return selectdata
#转化数据（npy2txt2ids)
def npy2txt(index, tokenizer, list_data):
npydata=[]
#for i in range(index.ntotal):
for i in range(50000):
#把二值化数据转化为字符串格式
onenum = i
onesen = " ".join(list_data[onenum]["tokens"])
###handle onesen
onesen.replace("\n\n\n", " ")
onesen.replace("\n\n", " ")
onesen.replace("\n", " ")
#print(onesen)
#转化为ids
tokens = tokenizer.tokenize(onesen)
oldtokenlength = len(tokens)
#tokens = tokenizer.convert_tokens_to_ids(tokens, unk_id=tokenizer.vocab['[UNK]'])
tokens = tokenizer.convert_tokens_to_ids(tokens)
newtokenlength = len(tokens)
assert oldtokenlength == newtokenlength
length = len(tokens)
#print(length)
npydata.append([length, tokens, onesen])
return np.asarray(npydata)
#在未标记的数据中利用旧模型标记数据
def select_before_data_lstm(config, lable_list, refer_model, npydata):
npy_dataloader = get_data_loader_unlable(config, npydata, shuffle=False, batch_size=30)
filename = '/root/lstm_select_data.txt'
labeled_data=[]
n = 0
neg_labels = [-100]
firstent = [-100]
firstentindex = [-100]
secondent =[-100]
secondentindex = [-100]
headid = "-100"
tailid = "-100"
typelabels = -100
num_list = list(0 for i in range(81))
with open(filename, 'w', encoding='utf-8') as f:
for step, (lengths, sentences, rawtext) in enumerate(npy_dataloader):
#预测标签label
logits, rep = refer_model(sentences, lengths)
distances = refer_model.get_mem_feature(rep)
short_logits = distances
for index, logit in enumerate(logits):
score = short_logits[index] # logits[index] + short_logits[index] + long_logits[index]
#print(score.shape) (81,)
golden_score = -2147483647.0
for i in range(81):
if golden_score < score[i] :
golden_score = score[i]
labels = i
#将标签属于before_relations的数据存储
if labels in lable_list and golden_score>=0.8 and num_list[labels] < 1:
num_list[labels] = num_list[labels]+1
lines = [str(labels), '\t', str(sentences[index]), '\t',str(short_logits[index]), '\t', rawtext[index], '\t', str(lengths[index]), '\n']
f.writelines(lines)
#tensor转化list存储到labeld_data
lengths[index] = lengths[index].numpy().tolist()
sentences[index] = sentences[index].numpy().tolist()
labeled_data.append([labels, neg_labels, sentences[index], firstent, firstentindex, secondent, secondentindex, headid, tailid, rawtext[index], lengths[index], typelabels])
print("<num_list>")
print(num_list)
return np.asarray(labeled_data)
#在未标记的数据中利用旧模型标记数据+mem
def select_before_data_lstm_mem(config, lable_list, refer_model, npydata, mem_set):
npy_dataloader = get_data_loader_unlable(config, npydata, shuffle=False, batch_size=30)
filename = '/root/lstm_select_data.txt'
labeled_data=[]
n = 0
neg_labels = [-100]
firstent = [-100]
firstentindex = [-100]
secondent =[-100]
secondentindex = [-100]
headid = "-100"
tailid = "-100"
typelabels = -100
num_list = list(0 for i in range(81))
with open(filename, 'w', encoding='utf-8') as f:
"""
for step, (lengths, sentences, rawtext) in enumerate(npy_dataloader):
#预测标签label
logits, rep = refer_model(sentences, lengths)
distances = refer_model.get_mem_feature(rep)
short_logits = distances
for index, logit in enumerate(logits):
score = short_logits[index] # logits[index] + short_logits[index] + long_logits[index]
#print(score.shape) (81,)
golden_score = -2147483647.0
for i in range(81):
if golden_score < score[i] :
golden_score = score[i]
labels = i
#将标签属于before_relations的数据存储
if labels in lable_list and golden_score>=0.8 and num_list[labels] < 1:
num_list[labels] = num_list[labels]+1
lines = [str(labels), '\t', str(sentences[index]), '\t',str(short_logits[index]), '\t', rawtext[index], '\t', str(lengths[index]), '\n']
f.writelines(lines)
#tensor转化list存储到labeld_data
lengths[index] = lengths[index].numpy().tolist()
sentences[index] = sentences[index].numpy().tolist()
labeled_data.append([labels, neg_labels, sentences[index], firstent, firstentindex, secondent, secondentindex, headid, tailid, rawtext[index], lengths[index], typelabels])
"""
#添加memory数据
mem_data = []
if len(mem_set) != 0:
for key in mem_set.keys():
mem_data.extend(mem_set[key]['0'])
#print(len(mem_data))
#print(mem_data)
for a in range(len(mem_data)):
#print(mem_data[a][0], mem_data[a][1], mem_data[a][2], mem_data[a][3], mem_data[a][4], mem_data[a][5], mem_data[a][6], mem_data[a][7], mem_data[a][8], mem_data[a][9], mem_data[a][10], typelabels)
labeled_data.append([mem_data[a][0], mem_data[a][1], mem_data[a][2], mem_data[a][3], mem_data[a][4], mem_data[a][5], mem_data[a][6], mem_data[a][7], mem_data[a][8], mem_data[a][9], mem_data[a][10], typelabels])
print("<num_list>")
print(num_list)
#print("labeled_data")
#print(labeled_data)
return np.asarray(labeled_data)
def select_before_data_bert(config, lable_list, refer_model, npydata):
npy_dataloader = get_data_loader_unlable(config, npydata, shuffle=False, batch_size=30)
filename = '/root/lstm_select_data.txt'
labeled_data=[]
n = 0
neg_labels = [-100]
firstent = [-100]
firstentindex = [-100]
secondent =[-100]
secondentindex = [-100]
headid = "-100"
tailid = "-100"
typelabels = -100
num_list = list(0 for i in range(81))
with open(filename, 'w', encoding='utf-8') as f:
for step, (lengths, sentences, rawtext) in enumerate(npy_dataloader):
masks = []
sentences_add0 = []
for k,length in enumerate(lengths):
if length <= 128:
mask = []
sentence = []
for j in range(0, length):
mask.append(1)
for j in range(length, 128):
mask.append(0)
masks.append(mask)
sentence = sentences[k].tolist()
for j in range(length, 128):
sentence.append(0)
sentences_add0.append(sentence)
#print(masks)
masks = torch.tensor(masks)
#print(sentences)
#print(sentences_add0)
sentences_add0 = torch.tensor(sentences_add0)
sentences_add0 = sentences_add0.to(config['device'])
masks = masks.to(config['device'])
#预测标签label
logits, rep = refer_model(sentences_add0, masks)
distances = refer_model.get_mem_feature(rep)
short_logits = distances
for index, logit in enumerate(logits):
score = short_logits[index] # logits[index] + short_logits[index] + long_logits[index]
#print(score.shape) (81,)
golden_score = -2147483647.0
for i in range(81):
if golden_score < score[i] :
golden_score = score[i]
labels = i
#将标签属于before_relations的数据存储
if labels in lable_list and golden_score>=0.8 and num_list[labels] < 1:
num_list[labels] = num_list[labels]+1
#lines = [str(labels), '\t', str(sentences_add0[index]), '\t',str(short_logits[index]), '\t', rawtext[index], '\t', str(lengths[index]), '\n']
#f.writelines(lines)
#tensor转化list存储到labeld_data
lengths[index] = lengths[index].numpy().tolist()
a = sentences_add0[index].cpu()
c = a.numpy().tolist()
b = masks[index].cpu()