def BPR_train_original(dataset, recommend_model, loss_class, epoch, neg_k=1, w=None):
Recmodel = recommend_model
Recmodel.train()
bpr: utils.BPRLoss = loss_class
allusers = list(range(dataset.n_users))
S, sam_time = utils.UniformSample_original(allusers, dataset)
print(f"BPR[sample time][{sam_time[0]:.1f}={sam_time[1]:.2f}+{sam_time[2]:.2f}]")
users = torch.Tensor(S[:, 0]).long()
posItems = torch.Tensor(S[:, 1]).long()
negItems = torch.Tensor(S[:, 2]).long()
users = users.to(world.device)
posItems = posItems.to(world.device)
negItems = negItems.to(world.device)
users, posItems, negItems = utils.shuffle(users, posItems, negItems)
total_batch = len(users) // world.config['bpr_batch_size'] + 1
aver_loss = 0.
for (batch_i,
(batch_users,
batch_pos,
batch_neg)) in enumerate(utils.minibatch(users,
posItems,
negItems,
batch_size=world.config['bpr_batch_size'])):
cri = bpr.stageOne(batch_users, batch_pos, batch_neg)
aver_loss += cri
if world.tensorboard:
w.add_scalar(f'BPRLoss/BPR', cri, epoch * int(len(users) / world.config['bpr_batch_size']) + batch_i)
aver_loss = aver_loss / total_batch
return f"[BPR[aver loss{aver_loss:.3e}]"
def _sample_train_data_at(self, epoch):
epoch_path = self.train_temp + 'S_epoch_' + str(epoch) + '.npy'
if os.path.exists(epoch_path):
S = np.load(epoch_path)
else:
allusers = list(range(self.dataset.n_users))
S, sam_time = utils.UniformSample_original(allusers, self.dataset)
print(
f"BPR[sample time][{sam_time[0]:.1f}={sam_time[1]:.2f}+{sam_time[2]:.2f}]"
)
np.save(epoch_path, S)
users = torch.Tensor(S[:, 0]).long()
posItems = torch.Tensor(S[:, 1]).long()
negItems = torch.Tensor(S[:, 2]).long()
users = users.to(world.device)
posItems = posItems.to(world.device)
negItems = negItems.to(world.device)
return users, posItems, negItems
def BPR_train_original(dataset,
Recmodel,
bpr: utils.BPRLoss,
epoch,
w=None,
val=-1):
if not args.simutaneously:
# train alternatively
Recmodel.train()
for m in Recmodel.modules():
if isinstance(m, nn.Embedding):
m.requires_grad_(val <= 0)
elif hasattr(m, 'weight'):
m.requires_grad_(not (val <= 0))
print('mapping fixed') if val <= 0 else print('embedding fixed')
# for m in Recmodel.modules(): # test separate training
# if hasattr(m, 'weight'):
# if hasattr(m, 'bias') and m.bias is not None:
# print(m, 'weight:', m.weight.requires_grad, ' bias:', m.bias.requires_grad)
# else:
# print(m, 'weight', m.weight.requires_grad)
S, sam_time = utils.UniformSample_original(dataset, val) # bpr sample
print(f"sample time:{sam_time[0]:.1f}={sam_time[1]:.2f}+{sam_time[2]:.2f}")
users = torch.Tensor(S[:, 0]).long().to(args.device)
posItems = torch.Tensor(S[:, 1]).long().to(args.device)
negItems = torch.Tensor(S[:, 2]).long().to(args.device)
users, posItems, negItems = utils.shuffle(users, posItems, negItems)
total_batch = len(users) // args.bpr_batch_size + 1
aver_loss = 0.
for (batch_i, (batch_users, batch_pos, batch_neg)) \
in enumerate(utils.minibatch(users, posItems, negItems, batch_size=args.bpr_batch_size)):
# train on different graph
cri = bpr.stageOne(dataset.graph[val + 1], batch_users, batch_pos,
batch_neg)
aver_loss += cri
if args.tensorboard:
w.add_scalar(
f'BPRLoss/BPR', cri,
epoch * int(len(users) / args.bpr_batch_size) + batch_i)
aver_loss = aver_loss / total_batch
return aver_loss
def BPR_train_original(dataset,
recommend_model,
loss_class,
epoch,
neg_k=1,
w=None):
Recmodel = recommend_model
Recmodel.train()
bpr: utils.BPRLoss = loss_class
with timer(name="Sample"):
S = utils.UniformSample_original(dataset)
users = torch.Tensor(S[:, 0]).long()
posItems = torch.Tensor(S[:, 1]).long()
negItems = torch.Tensor(S[:, 2]).long()
users = users.to(world.device)
posItems = posItems.to(world.device)
negItems = negItems.to(world.device)
users, posItems, negItems = utils.shuffle(users, posItems, negItems)
total_batch = len(users) // world.config['bpr_batch_size'] + 1
aver_loss = 0.
for (batch_i, (batch_users, batch_pos, batch_neg)) in enumerate(
utils.minibatch(users,
posItems,
negItems,
batch_size=world.config['bpr_batch_size'])):
cri = bpr.stageOne(batch_users, batch_pos, batch_neg)
aver_loss += cri
if False: #world.tensorboard:
w.add_scalar(
f'BPRLoss/BPR', cri,
epoch * int(len(users) / world.config['bpr_batch_size']) +
batch_i)
aver_loss = aver_loss / total_batch
time_info = timer.dict()
timer.zero()
return f"loss{aver_loss:.3f}-{time_info}"