def prediction(self, path_test, path_train, path_bestModel):
self.hyperpara = Hyperparameter()
eval_test = Eval()
ner = NER()
reader = Reader()
traininsts = reader.readfiles(path_train)
testinsts = reader.readfiles(path_test)
ner.create_alphabet(traininsts)
self.hyperpara.tag_size = ner.hyperpara.tag_size
self.hyperpara.embedding_num = ner.hyperpara.embedding_num
self.model = BiLstm(self.hyperpara) # BiLstm模型
if self.hyperpara.loadModel == 1 and\
self.hyperpara.load_pattern == 1:
try:
self.model.load_state_dict(torch.load(path_bestModel))
except Exception:
print('模型参数不匹配')
else:
pass
elif self.hyperpara.loadModel == 1 and\
self.hyperpara.load_pattern == 0 :
try:
self.model = torch.load(path_bestModel)
except Exception:
print('模型参数不匹配')
else:
pass
testExamples = ner.change(testinsts)
for idx in range(len(testExamples)):
test_list = []
test_list.append(testExamples[idx])
x, y = ner.variable(test_list)
lstm_feats = self.model(x)
predict = ner.getMaxIndex(lstm_feats)
predictLabels = []
for idy in range(len(predict)):
predictLabels.append(ner.label_AlphaBet.list[predict[idy]])
testinsts[idx].evalPRF(predictLabels, eval_test)
a, e = testinsts[idx].extractA_and_E()
self.Attr.append(a)
self.Eval.append(e)
def getAEOcount(self,path):
a = 0
e = 0
o = 0
reader = Reader()
Inst = reader.readfiles(path)
for i in Inst:
for j in range(len(i.labels)):
if len(i.labels[j]) == 3:
if i.labels[j][2] == 'A':
a = a+1
elif i.labels[j][2] == 'E':
e = e+1
elif i.labels[j] == 'O':
o += 1
return a,e,o
def train(self, path_train, path_dev, path_test, path_PRF, path_model,
path_bestModel):
#读取训练集、测试集、开发集 并 建立字典
reader = Reader()
traininsts = reader.readfiles(path_train)
devinsts = reader.readfiles(path_dev)
testinsts = reader.readfiles(path_test)
print('Training Instance:', len(traininsts))
print('Dev Instance:', len(devinsts))
print('Test Instance:', len(testinsts))
self.create_alphabet(traininsts)
#字符串转成ID
trainExamples = self.change(traininsts) # e_train
devExamples = self.change(devinsts)
testExamples = self.change(testinsts)
self.model = BiLstm(self.hyperpara) # BiLstm模型
# 加载模型
if self.hyperpara.loadModel == 1 and\
self.hyperpara.load_pattern == 1:
try:
self.model.load_state_dict(torch.load(path_bestModel))
except Exception:
print('模型参数不匹配')
else:
pass
elif self.hyperpara.loadModel == 1 and\
self.hyperpara.load_pattern == 0 :
try:
self.model = torch.load(path_bestModel)
except Exception:
print('模型参数不匹配')
else:
pass
optimizer = torch.optim.Adam(self.model.parameters(),
lr=self.hyperpara.lr) # 优化器
total_num = len(trainExamples)
for epoch in range(1, self.hyperpara.epochs):
print("————————第{}轮迭代，共{}轮————————".format(epoch,
self.hyperpara.epochs))
total = 0
random.shuffle(trainExamples) # 随机打乱训练集顺序，能有效提高准确率
try:
part = total_num // self.hyperpara.batch
if total_num % self.hyperpara.batch != 0:
part += 1
except ZeroDivisionError:
print('batch数为0，除0错误')
else:
#开始训练
self.model.train()
for idx in range(part):
begin = idx * self.hyperpara.batch
end = (idx + 1) * self.hyperpara.batch
if end > total_num:
end = total_num
batch_list = []
# batch_list_len = []
for idy in range(begin, end):
batch_list.append(trainExamples[idy])
# batch_list_len.append(len(trainExamples[idy].wordIndexs))
optimizer.zero_grad()
x, y = self.variable(batch_list)
lstm_feats = self.model(x)
loss = F.cross_entropy(lstm_feats, y)
total += 1
loss.backward()
optimizer.step()
print('current:', total, ", loss:", loss.data[0])
#开发集测试效果
eval_dev = Eval()
eval_dev_A = Eval()
eval_dev_E = Eval()
for idx in range(len(devExamples)):
dev_list = []
dev_list.append(devExamples[idx])
x, y = self.variable(dev_list)
lstm_feats = self.model(x)
predict = self.getMaxIndex(lstm_feats)
predictLabels = []
for idy in range(len(predict)):
predictLabels.append(
self.label_AlphaBet.list[predict[idy]])
gold_ent, predict_ent = devinsts[idx].evalPRF(
predictLabels, eval_dev)
gold_ent_A, gold_ent_E, predict_ent_A, predict_ent_E = devinsts[
idx].getAE(gold_ent, predict_ent)
devinsts[idx].evalAEPRF(gold_ent_A, predict_ent_A, eval_dev_A)
devinsts[idx].evalAEPRF(gold_ent_E, predict_ent_E, eval_dev_E)
line = ''
print('Dev: ', end="")
d_precision, d_recall, d_fscore = eval_dev.getFscore()
line = line + str(epoch) + '.dev:\nP:' + (
'%.2f' % (d_precision * 100)) + ' R:' + (
'%.2f' %
(d_recall * 100)) + ' F:' + ('%.2f' %
(d_fscore * 100)) + '\n'
print("precision:", d_precision * 100, ", recall: ",
d_recall * 100, ", fscore:", d_fscore * 100)
d_precision, d_recall, d_fscore = eval_dev_A.getFscore()
line = line + 'A_P:' + ('%.2f' % (d_precision * 100)) + ' A_R:' + (
'%.2f' %
(d_recall * 100)) + ' A_F:' + ('%.2f' %
(d_fscore * 100)) + '\n'
print("precision:", d_precision * 100, ", recall: ",
d_recall * 100, ", fscore:", d_fscore * 100)
d_precision, d_recall, d_fscore = eval_dev_E.getFscore()
line = line + 'E_P:' + ('%.2f' % (d_precision * 100)) + ' E_R:' + (
'%.2f' %
(d_recall * 100)) + ' E_F:' + ('%.2f' %
(d_fscore * 100)) + '\n'
print("precision:", d_precision * 100, ", recall: ",
d_recall * 100, ", fscore:", d_fscore * 100)
#测试集测试效果
eval_test = Eval()
eval_test_A = Eval()
eval_test_E = Eval()
for idx in range(len(testExamples)):
test_list = []
test_list.append(testExamples[idx])
x, y = self.variable(test_list)
lstm_feats = self.model(x)
predict = self.getMaxIndex(lstm_feats)
predictLabels = []
for idy in range(len(predict)):
predictLabels.append(
self.label_AlphaBet.list[predict[idy]])
gold_ent, predict_ent = testinsts[idx].evalPRF(
predictLabels, eval_test)
gold_ent_A, gold_ent_E, predict_ent_A, predict_ent_E = testinsts[
idx].getAE(gold_ent, predict_ent)
testinsts[idx].evalAEPRF(gold_ent_A, predict_ent_A,
eval_test_A)
testinsts[idx].evalAEPRF(gold_ent_E, predict_ent_E,
eval_test_E)
print('Test: ', end="")
t_precision, t_recall, t_fscore = eval_test.getFscore()
line = line + 'test:\nP:' + (
'%.2f' % (t_precision * 100)) + ' R:' + (
'%.2f' %
(t_recall * 100)) + ' F:' + ('%.2f' %
(t_fscore * 100)) + '\n'
print("precision:", t_precision * 100, ", recall: ",
t_recall * 100, ", fscore:", t_fscore * 100)
t_precision, t_recall, t_fscore = eval_test_A.getFscore()
line = line + 'A_P:' + ('%.2f' % (t_precision * 100)) + ' A_R:' + (
'%.2f' %
(t_recall * 100)) + ' A_F:' + ('%.2f' %
(t_fscore * 100)) + '\n'
print("precision:", t_precision * 100, ", recall: ",
t_recall * 100, ", fscore:", t_fscore * 100)
t_precision, t_recall, t_fscore = eval_test_E.getFscore()
line = line + 'E_P:' + ('%.2f' % (t_precision * 100)) + ' E_R:' + (
'%.2f' %
(t_recall * 100)) + ' E_F:' + ('%.2f' %
(t_fscore * 100)) + '\n'
print("precision:", t_precision * 100, ", recall: ",
t_recall * 100, ", fscore:", t_fscore * 100)
#保存模型
if self.hyperpara.save_pattern == 0:
torch.save(self.model.state_dict(),
path_model + str(epoch) + '.pkl')
elif self.hyperpara.save_pattern == 1:
torch.save(self.model, path_model + str(epoch) + '.pkl')
try:
file = open(path_PRF, 'a+', encoding='utf-8')
except IOError:
print('文件读取异常')
else:
file.write(line)
file.close()
