def LOO_run(opt, model, gpu, optimizer, train_loader, test_dataset, model_save_path):
max_epoch, early_stop, es_epoch = opt.max_epoch, opt.early_stop, opt.es_epoch
save = False
if model_save_path != None:
save= True
template = {'best_score':-999, 'best_result':-1, 'final_result':-1}
eval_dict = {5: deepcopy(template), 10:deepcopy(template), 20:deepcopy(template), 'early_stop':0, 'early_stop_max':early_stop, 'final_epoch':0}
# begin training
for epoch in range(max_epoch):
tic1 = time.time()
train_loader.dataset.negative_sampling()
epoch_loss = []
for batch_user, batch_pos_item, batch_neg_item in train_loader:
# Convert numpy arrays to torch tensors
batch_user = batch_user.to(gpu)
batch_pos_item = batch_pos_item.to(gpu)
batch_neg_item = batch_neg_item.to(gpu)
# Forward Pass
model.train()
output = model(batch_user, batch_pos_item, batch_neg_item)
batch_loss = model.get_loss(output)
epoch_loss.append(batch_loss)
# Backward and optimize
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
epoch_loss = float(torch.mean(torch.stack(epoch_loss)))
toc1 = time.time()
# evaluation
if epoch < es_epoch:
verbose = 25
else:
verbose = 1
if epoch % verbose == 0:
is_improved, eval_results, elapsed = evaluation(model, gpu, eval_dict, epoch, test_dataset)
LOO_print_result(epoch, max_epoch, epoch_loss, eval_results, is_improved=is_improved, train_time = toc1-tic1, test_time = elapsed)
if is_improved:
if save:
torch.save(model.state_dict(), model_save_path)
if (eval_dict['early_stop'] >= eval_dict['early_stop_max']):
break
print("BEST EPOCH::", eval_dict['final_epoch'])
print_final_result(eval_dict)
def LOO_URRD_run(opt,
model,
gpu,
optimizer,
train_loader,
test_dataset,
model_save_path=None):
max_epoch, early_stop, es_epoch = opt.max_epoch, opt.early_stop, opt.es_epoch
save = False
if model_save_path != None: save = True
train_loss_arr = []
template = {'best_score': -999, 'best_result': -1, 'final_result': -1}
eval_dict = {
5: deepcopy(template),
10: deepcopy(template),
20: deepcopy(template),
'early_stop': 0,
'early_stop_max': early_stop,
'final_epoch': 0
}
for epoch in range(max_epoch):
tic1 = time.time()
train_loader.dataset.negative_sampling()
train_loader.dataset.URRD_sampling()
epoch_loss = []
for batch_user, batch_pos_item, batch_neg_item in train_loader:
# Convert numpy arrays to torch tensors
batch_user = batch_user.to(gpu)
batch_pos_item = batch_pos_item.to(gpu)
batch_neg_item = batch_neg_item.to(gpu)
# Forward Pass
model.train()
# Base Loss
output = model(batch_user, batch_pos_item, batch_neg_item)
base_loss = model.get_loss(output)
# URRD Loss
batch_user = batch_user.unique()
interesting_items, uninteresting_items = train_loader.dataset.get_samples(
batch_user)
interesting_items = interesting_items.to(gpu).type(
torch.cuda.LongTensor)
uninteresting_items = uninteresting_items.to(gpu).type(
torch.cuda.LongTensor)
interesting_prediction = model.forward_multi_items(
batch_user, interesting_items)
uninteresting_prediction = model.forward_multi_items(
batch_user, uninteresting_items)
URRD_loss = relaxed_ranking_loss(interesting_prediction,
uninteresting_prediction)
# batch loss
batch_loss = base_loss + opt.URRD_lmbda * URRD_loss
epoch_loss.append(batch_loss)
# Backward and optimize
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
epoch_loss = float(torch.mean(torch.stack(epoch_loss)))
train_loss_arr.append(epoch_loss)
toc1 = time.time()
# evaluation
if epoch < es_epoch:
verbose = 25
else:
verbose = 1
if epoch % verbose == 0:
is_improved, eval_results, elapsed = evaluation(
model, gpu, eval_dict, epoch, test_dataset)
LOO_print_result(epoch,
max_epoch,
epoch_loss,
eval_results,
is_improved=is_improved,
train_time=toc1 - tic1,
test_time=elapsed)
if is_improved:
if save:
torch.save(model.state_dict(), model_save_path)
if (eval_dict['early_stop'] >= eval_dict['early_stop_max']):
break
print("BEST EPOCH::", eval_dict['final_epoch'])
print_final_result(eval_dict)