def dump_current_scores_of_devtest(args, m, xp):
for mode in ['dev', 'test']:
if mode == 'dev': current_data = dev_data
if mode == 'test': current_data = test_data
scores, accuracy = list(), list()
for batch in chunked(current_data, args.test_batch_size):
with chainer.using_config('train',
False), chainer.no_backprop_mode():
current_score = m.get_scores(batch, glinks, grelations, gedges,
xp, mode)
for v, (h, r, t, l) in zip(current_score.data, batch):
values = (h, r, t, l, v)
values = map(str, values)
values = ','.join(values)
scores.append(values)
if v < args.threshold: # 模型判断为正三元组
if l == 1: accuracy.append(1.0)
else: accuracy.append(0.0)
else: # 模型判断为负三元组
if l == 1: accuracy.append(0.0)
else: accuracy.append(1.0)
del current_score
tool.trace('\t ', mode, sum(accuracy) / len(accuracy))
if args.margin_file != '':
with open(args.margin_file, 'a') as wf:
wf.write(mode + ':' + ' '.join(scores) + '\n')
def rules_addition():
tool.trace('load rules')
global premise
premise = defaultdict(set)
for line in tool.read(args.rules_file):
r, r_, flag = list(map(int, line.strip().split('\t')))
premise[r].add((r_, flag))
print(len(premise[0]))
print(len(premise))
def main(args):
loadflag = False
initilize_dataset()
args.rel_size, args.entity_size = get_sizes(args)
print('relation size:', args.rel_size, 'entity size:', args.entity_size)
RelCovVal = [1.0] * args.rel_size
RelCovValTmp = [0.0] * args.rel_size
EntCovVal = [1.0] * args.entity_size
EntCovValTmp = [0.0] * args.entity_size
xp = Backend(args)
m = get_model(args)
opt = get_opt(args)
if (loadflag == True):
serializers.load_npz(
'./savedWN/ModelA3CNN_OOKBtest_' + args.pooling_method + '.model',
m)
opt.setup(m)
for epoch in range(args.epoch_size):
'''
fileTmp = open('Ent100Eval_v4.txt','a')
for i in range(100):
fileTmp.write(str(EntCovVal[i])+'\t')
fileTmp.write('\n')
fileTmp.close()
'''
opt.alpha = args.beta0 / (1.0 + args.beta1 * epoch)
trLoss, Ntr = train(args, m, xp, opt, RelCovVal, RelCovValTmp,
EntCovVal, EntCovValTmp)
'''
for i in range(args.rel_size):
RelCovVal[i] = RelCovValTmp[i]
RelCovValTmp[i] = 0.0
for i in range(args.entity_size):
EntCovVal[i] = EntCovValTmp[i]
EntCovValTmp[i] = 0.0'''
tool.trace('epoch:', epoch, 'tr Loss:', tool.dress(trLoss), Ntr)
if (epoch % 5 == 0):
serializers.save_npz(
'./savedWN/ModelA3CNN_OOKBtest_' + args.pooling_method +
'.model', m)
dump_current_scores_of_devtest(args, m, xp, RelCovVal, EntCovVal)
def main(args):
loadflag = False
initilize_dataset()
args.rel_size,args.entity_size = get_sizes(args)
print('relation size:',args.rel_size,'entity size:',args.entity_size)
W1lists = []
file1 = open(args.pretrain_file + '/relation2vec.vec','r')
for line in file1.readlines():
line = line.strip().split('\t')
W1lists.append(line)
W1 = np.array(W1lists).astype(np.float32)
W2lists = []
file2 = open(args.pretrain_file + '/entity2vec.vec','r')
for line in file2.readlines():
line = line.strip().split('\t')
W2lists.append(line)
W2 = np.array(W2lists).astype(np.float32)
#print W1.shape, W2.shape
RelCovVal = [1.0] * args.rel_size
RelCovValTmp = [0.0] * args.rel_size
EntCovVal = [1.0] * args.entity_size
EntCovValTmp = [0.0] * args.entity_size
xp = Backend(args)
m = get_model(args,W1,W2)
opt = get_opt(args)
if(loadflag == True):
serializers.load_npz('./savedFB/ModelA3CNN_ATT_0817_head.model',m)
opt.setup(m)
normalizeR = m.Normal(xp)
#random.shuffle(train_data)
for epoch in range(args.epoch_size):
opt.alpha = args.beta0/(1.0+args.beta1*epoch)
trLoss,Ntr = train(args,m,xp,opt,RelCovVal,RelCovValTmp,EntCovVal,EntCovValTmp)
normalizeR = m.Normal(xp)
tool.trace('epoch:',epoch,'tr Loss:',tool.dress(trLoss),Ntr)
if(epoch % 5 == 0):
serializers.save_npz('./savedFB/ModelA3CNN_ATT_0817_head.model',m)
dump_current_scores_of_devtest(args,m,xp,RelCovVal,EntCovVal)
def main(args):
# 读入规则
rules_addition()
# 初始化所有数据集
initilize_dataset()
# 根据文件内容修改关系和实体的默认值
args.rel_size, args.entity_size = get_sizes(args)
print('relation size:', args.rel_size, 'entity size:', args.entity_size)
xp = Backend(args) #返回一个可调用的对象
m = get_model(args) # return A0(args)
# Setup an optimizer
# 设置训练时用的优化方法，默认为Adam
opt = get_opt(args) # return optimizers.Adam()
# setup()方法只是为优化器提供一个link
opt.setup(m)
for epoch in range(args.epoch_size):
opt.alpha = args.beta0 / (1.0 + args.beta1 * epoch)
trLoss, Ntr = train(args, m, xp, opt) #对应到main.py的train
tool.trace('epoch:', epoch, 'tr Loss:', tool.dress(trLoss), Ntr)
dump_current_scores_of_devtest(args, m, xp)
def main(args):
loadflag = False
initilize_dataset()
args.rel_size, args.entity_size = get_sizes(args)
print('relation size:', args.rel_size, 'entity size:', args.entity_size)
xp = Backend(args)
m = get_model(args)
opt = get_opt(args)
if (loadflag == True):
serializers.load_npz(
'./savedWN/ModelA0_OOKBtest_' + args.pooling_method, m)
opt.setup(m)
for epoch in range(args.epoch_size):
#dump_current_scores_of_devtest(args,m,xp)
opt.alpha = args.beta0 / (1.0 + args.beta1 * epoch)
trLoss, Ntr = train(args, m, xp, opt)
tool.trace('epoch:', epoch, 'tr Loss:', tool.dress(trLoss), Ntr)
if (epoch % 10 == 0):
serializers.save_npz(
'./savedWN/ModelA0_OOKBtest_' + args.pooling_method, m)
dump_current_scores_of_devtest(args, m, xp)
def initilize_dataset():
global candidate_heads, gold_heads, candidate_tails, gold_tails
global glinks, grelations, gedges
global train_data, dev_data, test_data, trfreq
# get properties of knowledge graph
# 读取训练集
tool.trace('load train')
grelations = defaultdict(set)
glinks = defaultdict(set)
train_data = set()
for line in tool.read(args.train_file):
h, r, t = list(map(int, line.strip().split('\t')))
# train_data包含了所有三元组
train_data.add((
h,
r,
t,
))
# trfreq键值为关系ID，值为有该关系的三元组数量
trfreq[r] += 1
# grelations存储了所有关系，键值为(头实体，尾实体)，值为关系ID
grelations[(h, t)].add(r)
# 存储邻居
glinks[t].add(h)
glinks[h].add(t)
# gold_可以用于查找每个三元组的头实体和尾实体
gold_heads[(r, t)].add(h)
gold_tails[(h, r)].add(t)
# candidate_以关系作为键值，每个键对应的值为与该关系相连的头实体或尾实体
candidate_heads[r].add(h)
candidate_tails[r].add(t)
# 与glinks的存储一样，但glinks的键值包含了所有实体，而这个的键值只有头实体或尾实体
tail_per_head[h].add(t)
head_per_tail[t].add(h)
# 引入规则
if r in premise:
conclusion = premise[r]
for r_, flag in conclusion:
if flag == 1:
grelations[(h, t)].add(r_)
train_data.add((
h,
r_,
t,
))
trfreq[r_] += 1
gold_heads[(r_, t)].add(h)
gold_tails[(h, r_)].add(t)
candidate_heads[r_].add(h)
candidate_tails[r_].add(t)
else:
grelations[(t, h)].add(r_)
train_data.add((
t,
r_,
h,
))
trfreq[r_] += 1
gold_heads[(r_, h)].add(t)
gold_tails[(t, r_)].add(h)
candidate_heads[r_].add(t)
candidate_tails[r_].add(h)
tail_per_head[t].add(h)
head_per_tail[h].add(t)
for e in glinks:
glinks[e] = list(glinks[e])
for r in candidate_heads:
candidate_heads[r] = list(candidate_heads[r])
for r in candidate_tails:
candidate_tails[r] = list(candidate_tails[r])
# 改写值，每个键值对应的为与它有关系的实体数量
for h in tail_per_head:
tail_per_head[h] = len(tail_per_head[h]) + 0.0
for t in head_per_tail:
head_per_tail[t] = len(head_per_tail[t]) + 0.0
# 构造辅助集
tool.trace('set axiaulity')
# switch standard setting or OOKB setting
# 2个实验，标准三元组分类实验和OOKB实验
if args.train_file == args.auxiliary_file:
tool.trace('standard setting, use: edges=links')
gedges = glinks
else:
# ookb的实验只修改grelation和gedges?
tool.trace('OOKB esetting, use: different edges')
gedges = defaultdict(set)
for line in tool.read(args.auxiliary_file):
h, r, t = list(map(int, line.strip().split('\t')))
grelations[(h, t)].add(r)
# 引入规则
if r in premise:
conclusion = premise[r]
for r_, flag in conclusion:
if flag == 1:
grelations[(h, t)].add(r_)
else:
grelations[(t, h)].add(r_)
gedges[t].add(h)
gedges[h].add(t)
for e in gedges:
gedges[e] = list(gedges[e])
for (h, t) in grelations:
grelations[(h, t)] = list(grelations[(h, t)])
# if len(grelations[(h,t)]) != 1:
# print("len(grelations[(h,t)])",len(grelations[(h,t)]),h,t,grelations[(h,t)])
print("grelations", len(grelations))
train_data = list(train_data)
print("train_data", len(train_data))
for r in trfreq:
trfreq[r] = args.train_size / (float(trfreq[r]) * len(trfreq))
# load dev
# 读取验证集，验证集包含正三元组和负三元组
tool.trace('load dev')
dev_data = list()
for line in open(args.dev_file):
h, r, t, l = list(map(int, line.strip().split('\t')))
# 过滤掉验证集中含OOKB实体的三元组(只在做OOKB的实验中会成立？)
if h not in glinks or t not in glinks: continue
dev_data.append((
h,
r,
t,
l,
))
print('dev size:', len(dev_data))
# load test
# 读取测试集，测试集包含正三元组和负三元组
tool.trace('load test')
test_data = list()
for line in open(args.test_file):
h, r, t, l = list(map(int, line.strip().split('\t')))
# 过滤掉测试集中含OOKB实体的三元组(做OOKB的实验不是过滤掉不含的吗？)
# 标准三元组分类的实验中没有OOKB的实体？
if h not in glinks or t not in glinks: continue
test_data.append((
h,
r,
t,
l,
))
print('test size:', len(test_data))
def initilize_dataset():
global candidate_heads, gold_heads, candidate_tails, gold_tails
global glinks, grelations, gedges, grelationsT, grelationsEdges
# get properties of knowledge graph
tool.trace('load train')
grelations = dict()
grelationsT = defaultdict(set)
glinks = defaultdict(set)
for line in tool.read(args.train_file):
h, r, t = list(map(int, line.strip().split('\t')))
grelations[(h, t)] = r
grelationsT[r].add((h, t))
glinks[t].add(h)
glinks[h].add(t)
gold_heads[(r, t)].add(h)
gold_tails[(h, r)].add(t)
candidate_heads[r].add(h)
candidate_tails[r].add(t)
tail_per_head[h].add(t)
head_per_tail[t].add(h)
for e in glinks:
glinks[e] = list(glinks[e])
for r in grelationsT:
grelationsT[r] = list(grelationsT[r])
for r in candidate_heads:
candidate_heads[r] = list(candidate_heads[r])
for r in candidate_tails:
candidate_tails[r] = list(candidate_tails[r])
for h in tail_per_head:
tail_per_head[h] = len(tail_per_head[h]) + 0.0
for t in head_per_tail:
head_per_tail[t] = len(head_per_tail[t]) + 0.0
tool.trace('set axiaulity')
# switch standard setting or OOKB setting
if args.train_file == args.auxiliary_file:
tool.trace('standard setting, use: edges=links')
gedges = glinks
grelationsEdges = grelationsT
else:
tool.trace('OOKB esetting, use: different edges')
gedges = defaultdict(set)
grelationsEdges = defaultdict(set)
for line in tool.read(args.auxiliary_file):
h, r, t = list(map(int, line.strip().split('\t')))
grelationsEdges[r].add((h, t))
#grelations[(h,t)]=r
gedges[t].add(h)
gedges[h].add(t)
for e in gedges:
gedges[e] = list(gedges[e])
for r in grelationsEdges:
grelationsEdges[r] = list(grelationsEdges[r])
global train_data, dev_data, test_data, trfreq
# load train
train_data = set()
for line in open(args.train_file):
h, r, t = list(map(int, line.strip().split('\t')))
train_data.add((
h,
r,
t,
))
trfreq[r] += 1
train_data = list(train_data)
for r in trfreq:
trfreq[r] = args.train_size / (float(trfreq[r]) * len(trfreq))
# load dev
tool.trace('load dev')
dev_data = list()
for line in open(args.dev_file):
h, r, t, l = list(map(int, line.strip().split('\t')))
if h not in glinks or t not in glinks: continue
dev_data.append((
h,
r,
t,
l,
))
print('dev size:', len(dev_data))
# load test
tool.trace('load test')
test_data = list()
for line in open(args.test_file):
h, r, t, l = list(map(int, line.strip().split('\t')))
#if h not in glinks and h < args.entity_size: continue
#if t not in glinks and t < args.entity_size: continue
if h not in glinks or t not in glinks: continue
test_data.append((
h,
r,
t,
l,
))
print('test size:', len(test_data))
def main(args):
global candidate_heads, gold_heads, candidate_tails, gold_tails, black_set
xp = XP(args)
args.rel_size, args.entity_size = get_sizes(args)
print('relation size:', args.rel_size, 'entity size:', args.entity_size)
m = get_model(args)
opt = get_opt(args)
opt.setup(m)
relations = dict()
links = defaultdict(set)
for line in tool.read(args.train_file):
items = list(map(int, line.strip().split('\t')))
if len(items) == 4:
h, r, t, l = items
if l == 0: continue
else:
h, r, t = items
relations[(h, t)] = r
links[t].add(h)
links[h].add(t)
gold_heads[(r, t)].add(h)
gold_tails[(h, r)].add(t)
candidate_heads[r].add(h)
candidate_tails[r].add(t)
tail_per_head[h].add(t)
head_per_tail[t].add(h)
for e in links:
links[e] = list(links[e])
for p in gold_heads:
if len(candidate_heads[p[0]] - gold_heads[p]) == 0:
p = (-p[0], p[1])
black_set.add(p)
for p in gold_tails:
if len(candidate_tails[p[1]] - gold_tails[p]) == 0:
black_set.add(p)
print('black list size:', len(black_set))
for r in candidate_heads:
candidate_heads[r] = list(candidate_heads[r])
for r in candidate_tails:
candidate_tails[r] = list(candidate_tails[r])
for h in tail_per_head:
tail_per_head[h] = len(tail_per_head[h]) + 0.0
for t in head_per_tail:
head_per_tail[t] = len(head_per_tail[t]) + 0.0
if args.train_file == args.auxiliary_file:
tool.trace('use: edges=links')
edges = links
else:
tool.trace('use: different edges')
edges = defaultdict(set)
for line in tool.read(args.auxiliary_file):
items = list(map(int, line.strip().split('\t')))
if len(items) == 4:
h, r, t, l = items
if l == 0: continue
else:
h, r, t = items
relations[(h, t)] = r
edges[t].add(h)
edges[h].add(t)
for e in edges:
edges[e] = list(edges[e])
for epoch in range(args.epoch_size):
opt.alpha = args.beta0 / (1.0 + args.beta1 * epoch)
trLoss, Ntr = train(args, m, xp, opt, links, relations, edges)
evaluate(args, m, xp, links, relations, edges)
tool.trace('epoch:', epoch, 'tr Loss:', tool.dress(trLoss), Ntr)
