def __init__(self, emb, class_num, loader, config):
self.class_num = class_num
self.loader = loader
self.config = config
self.model = DS_Model(emb, class_num, config)
self.model = self.model.to(config.device)
self.eval_tool = Eval(class_num, config)
self.plot_tool = Canvas(config)
def test(model, criterion, loader, config):
print('--------------------------------------')
print('start test ...')
_, _, test_loader = loader
model.load_state_dict(
torch.load(os.path.join(config.model_dir, 'model.pkl')))
eval_tool = Eval(config)
f1, test_loss = eval_tool.evaluate(model, criterion, test_loader)
print('test_loss: %.3f | micro f1 on test: %.4f' % (test_loss, f1))
def train(model, criterion, loader, config):
train_loader, dev_loader, _ = loader
optimizer = optim.SGD(model.parameters(), lr=config.lr)
print(model)
print('traning model parameters:')
for name, param in model.named_parameters():
if param.requires_grad:
print('%s : %s' % (name, str(param.data.shape)))
print('--------------------------------------')
print('start to train the model ...')
eval_tool = Eval(config)
max_f1 = -float('inf')
current_lr = config.lr
for epoch in range(1, config.epoch + 1):
for step, (data, label) in enumerate(train_loader):
model.train()
data = data.to(config.device)
label = label.to(config.device)
optimizer.zero_grad()
logits = model(data)
loss = criterion(logits, label)
loss.backward()
optimizer.step()
_, train_loss, _ = eval_tool.evaluate(model, criterion, train_loader)
f1, dev_loss, _ = eval_tool.evaluate(model, criterion, dev_loader)
print(
'[%03d] train_loss: %.3f | dev_loss: %.3f | micro f1 on dev: %.4f'
% (epoch, train_loss, dev_loss, f1),
end=' ')
file_write_loss = open('./output/Attention_CNN/loss.txt', 'a')
file_write_loss.writelines('%.6f \t %.6f \t %.6f \n' %
(train_loss, f1, dev_loss))
file_write_loss.close()
if f1 > max_f1:
max_f1 = f1
torch.save(model.state_dict(),
os.path.join(config.model_dir, 'model.pkl'))
print('>>> save models!')
else:
print()
# lr schedule
current_lr *= 0.95
change_lr(optimizer, current_lr)
def train(optims, max_epoch, policy, bsize, env, num_clicks, recom_number, max_length, origin_reward, capacity):
outputdir="model_output"
policy_new=os.path.join(outputdir, 'model_free_simple.pickle')
#weight = torch.FloatTensor(numlabel).fill_(1)
optim_fn, optim_params=get_optimizer(optims)
optimizer = optim_fn(filter(lambda p: p.requires_grad, policy.parameters()), **optim_params)
n_epochs=max_epoch
max_reward = 0
epoch = 1
best_model = None
rewards = [origin_reward]
while epoch <= n_epochs:
_ = train_gen_pg_each(policy, env, epoch, optimizer, num_clicks, recom_number, max_length, bsize, total_size = capacity)
print('saving policy at epoch {0}'.format(epoch))
if not os.path.exists(outputdir):
os.makedirs(outputdir)
torch.save(policy, policy_new)
#Eval the new policy
_, mean_reward = Eval(policy_new)
rewards.append(mean_reward)
# save model
if mean_reward >= max_reward:
best_model = policy
max_reward = mean_reward
epoch += 1
return best_model, rewards, max_reward
def evalTask(job):
assert (job.status == "finished")
ddir = job.args[1]
goldFile = file(ddir / "data")
trainPts = readDataFile(goldFile)
truth = readDataFile(goldFile)
matrixFile = file(ddir / "matrix")
matrix = readClMat(matrixFile)
propFile = file(str(job.results))
prop = readDataFile(propFile)
res = Eval(gold=truth, prop=prop, clMat=matrix, filename=job.args[1])
res.stats["time"] = jobTime(job)
return res
def evalTask(job):
assert(job.status == "finished")
ddir = job.args[1]
goldFile = file(ddir/"data")
trainPts = readDataFile(goldFile)
truth = readDataFile(goldFile)
matrixFile = file(ddir/"matrix")
matrix = readClMat(matrixFile)
propFile = file(str(job.results))
prop = readDataFile(propFile)
res = Eval(gold=truth, prop=prop, clMat=matrix, filename=job.args[1])
res.stats["time"] = jobTime(job)
return res
def run(self, hogwash_job):
self.replace_actions(hogwash_job) #bug in hogwash
proposal = readDataFile(file(self.exp))
goldFile = file(self.gold)
readDataFile(goldFile) #discard training data
gold = readDataFile(goldFile)
matrix = readClMat(file(self.mat))
evaluator = Eval(gold, proposal, matrix, self.exp)
return evaluator
def train(model, criterion, loader, config):
train_loader, dev_loader, _ = loader
optimizer = optim.Adadelta(model.parameters(),
lr=config.lr,
weight_decay=config.L2_decay)
print(model)
print('traning model parameters:')
for name, param in model.named_parameters():
if param.requires_grad:
print('%s : %s' % (name, str(param.data.shape)))
print('--------------------------------------')
print('start to train the model ...')
eval_tool = Eval(config)
max_f1 = -float('inf')
for epoch in range(1, config.epoch + 1):
for step, (data, label) in enumerate(train_loader):
model.train()
data = data.to(config.device)
label = label.to(config.device)
optimizer.zero_grad()
logits = model(data)
loss = criterion(logits, label)
loss.backward()
nn.utils.clip_grad_value_(model.parameters(), clip_value=5)
optimizer.step()
_, train_loss, _ = eval_tool.evaluate(model, criterion, train_loader)
f1, dev_loss, _ = eval_tool.evaluate(model, criterion, dev_loader)
print(
'[%03d] train_loss: %.3f | dev_loss: %.3f | micro f1 on dev: %.4f'
% (epoch, train_loss, dev_loss, f1),
end=' ')
if f1 > max_f1:
max_f1 = f1
torch.save(model.state_dict(),
os.path.join(config.model_dir, 'model.pkl'))
print('>>> save models!')
else:
print()
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)
class Runner(object):
def __init__(self, emb, class_num, loader, config):
self.class_num = class_num
self.loader = loader
self.config = config
self.model = DS_Model(emb, class_num, config)
self.model = self.model.to(config.device)
self.eval_tool = Eval(class_num, config)
self.plot_tool = Canvas(config)
def train(self):
train_loader, test_loader = self.loader
optimizer = optim.Adam(self.model.parameters(), lr=config.lr)
print(self.model)
print('traning model parameters:')
for name, param in self.model.named_parameters():
if param.requires_grad:
print('%s : %s' % (name, str(param.data.shape)))
print('--------------------------------------')
print('start to train the model ...')
max_auc = -float('inf')
for epoch in range(1, 1 + self.config.epoch):
train_loss = 0.0
data_iterator = tqdm(train_loader, desc='Train')
for step, (data, label, scope) in enumerate(data_iterator):
self.model.train()
data = data.to(self.config.device)
label = label.to(self.config.device)
optimizer.zero_grad()
loss, _ = self.model(data, scope, label)
train_loss += loss.item()
loss.backward()
optimizer.step()
train_loss = train_loss / len(train_loader)
auc, test_loss, precision, recall = self.eval_tool.evaluate(
self.model, test_loader)
print(
'[%03d] train_loss: %.3f | test_loss: %.3f | auc on test: %.3f'
% (epoch, train_loss, test_loss, auc),
end=' ')
if auc > max_auc:
max_auc = auc
torch.save(self.model.state_dict(),
os.path.join(self.config.model_dir, 'model.pkl'))
print('>>> save models!')
else:
print()
def test(self):
print('-------------------------------------')
print('start test ...')
_, test_loader = self.loader
self.model.load_state_dict(
torch.load(os.path.join(config.model_dir, 'model.pkl')))
auc, test_loss, precision, recall = self.eval_tool.evaluate(
self.model, test_loader)
print('test_loss: %.3f | auc on test: %.3f' % (test_loss, auc))
target_file = os.path.join(self.config.model_dir, 'pr.txt')
with open(target_file, 'w', encoding='utf-8') as fw:
for i in range(len(precision)):
fw.write('%.6f \t %.6f \n' % (precision[i], recall[i]))
self.plot_tool.plot(precision, recall, auc)
print("Testing")
agent.load_state_dict(torch.load(pretrained_agent))
print("Agent evaluation!")
_ = evaluate_agent(agent, 101, bsize, recom_length - 1, validSample,
testSample, device, 'test')
print("User model evaluation!")
#generator.load_state_dict(torch.load(pretrained_gen))
#Evaluate without EOS
generator.load_state_dict(torch.load(pretrained_gen))
eval_acc, eval_preck, eval_rewd, eval_loss = evaluate_user(
generator, 101, bsize, recom_length - 1, validSample, testSample,
loss_fn_target, loss_fn_reward, device, 'test')
#Save the whole policy model
torch.save(agent, 'model_output/agent.pickle')
if interact:
#Generate new samples from the environment
reward_orig, reward_optim = Eval('model_output/agent.pickle')
if e == 0:
rewards.append(reward_orig)
rewards.append(reward_optim)
'''
#Load the best model
generator.load_state_dict(torch.load(pretrained_gen))
discriminator.load_state_dict(torch.load(pretrained_dis))
agent.load_state_dict(torch.load(pretrained_agent))
'''
#Generate new data
subprocess.call(subprocess_cmd, shell=False)
save_plot(Epochs, 1, rewards, 'all_rewards.png')
len(set([pt.label for pt in test])), "clusters"
print "Objective value of truth: %.3g" % objective(test, clMat)
print
for algorithm in ["first", "best", "vote", "pivot"]:
evals = []
print "================", algorithm, "================="
print
for run in range(10):
cmd = "%s -a %s %s" % (tester, algorithm, classFileName)
output = StringIO()
status = bettersystem(cmd, stdout=output, stderr=StringIO())
assert(status == 0)
prop = readDataFile(output.getvalue().split("\n"))
score = Eval(test, prop, clMat, filename=("run%d" % run))
evals.append(score)
print "Run", run
print score
print algorithm
print "Best Objective:"
print min(evals, key=lambda x: x.stats["objective"])
print "Average:"
print reduce(lambda x,y: x+y, evals).normalized(len(evals))
print
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()
global device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
outputdir="model_output"
policy_new=os.path.join(outputdir, 'model_free_simple.pickle')
#Define the environment
num_clicks=100
recom_number = 20
args = get_args()
optim = args.optim
bsize = args.batch_size
#Load environment
env_path = "./model_output/environment.pickle"
env = torch.load(env_path)
#Load initial policy
policy_path = "./model_output/orig_policy.pickle"
policy = torch.load(policy_path)
torch.save(policy, policy_new)
#Training for model-free reinforcement learning
max_epoch = 200
max_length = 5
capacity = 10000
origin_reward, _ = Eval(policy_new)
_, rewards, max_reward = train(optim, max_epoch, policy, bsize, env, num_clicks, recom_number, max_length, origin_reward, capacity)
#Plot rewards
save_plot(max_epoch, 1, rewards, 'rewards_test.png')
#Write rewards
f=open('rewards_model_free.txt','ab+')
np.savetxt(f,rewards)
f.close()
