class Triple2vec_train(TrainEngine):
""" An instance class from the TrainEngine base class
"""
def __init__(self, config):
"""Constructor
Args:
config (dict): All the parameters for the model
"""
self.config = config
super(Triple2vec_train, self).__init__(self.config)
self.gpu_id, self.config["device_str"] = self.get_device()
def load_dataset(self):
"""Load dataset."""
split_data = load_split_dataset(self.config)
self.data = GroceryData(split_dataset=split_data, config=self.config)
self.config["model"]["n_users"] = self.data.n_users
self.config["model"]["n_items"] = self.data.n_items
def train(self):
self.load_dataset()
self.engine = Triple2vecEngine(self.config)
self.engine.data = self.data
self.train_data = self.data.sample_triple()
train_loader = DataLoader(
torch.LongTensor(self.train_data.to_numpy()).to(self.engine.device),
batch_size=self.config["model"]["batch_size"],
shuffle=True,
drop_last=True,
)
self.monitor = Monitor(
log_dir=self.config["system"]["run_dir"], delay=1, gpu_id=self.gpu_id
)
self.model_save_dir = os.path.join(
self.config["system"]["model_save_dir"], self.config["model"]["save_name"]
)
self._train(self.engine, train_loader, self.model_save_dir)
self.config["run_time"] = self.monitor.stop()
return self.eval_engine.best_valid_performance
def load_dataset(self):
"""Load dataset."""
split_data = load_split_dataset(self.config)
self.data = GroceryData(split_dataset=split_data, config=self.config)
self.config["model"]["n_users"] = self.data.n_users
self.config["model"]["n_items"] = self.data.n_items
class VBCAR_train(TrainEngine):
""" An instance class from the TrainEngine base class
"""
def __init__(self, config):
"""Constructor
Args:
config (dict): All the parameters for the model
"""
self.config = config
super(VBCAR_train, self).__init__(self.config)
def load_dataset(self):
"""Load dataset."""
split_data = load_split_dataset(self.config)
self.data = GroceryData(split_dataset=split_data, config=self.config)
self.config["model"]["n_users"] = self.data.n_users
self.config["model"]["n_items"] = self.data.n_items
def train(self):
"""Default train implementation
"""
self.load_dataset()
self.train_data = self.data.sample_triple()
self.config["model"]["alpha_step"] = (
1 - self.config["model"]["alpha"]) / (
self.config["model"]["max_epoch"])
self.config["user_fea"] = self.data.user_feature
self.config["item_fea"] = self.data.item_feature
self.engine = VBCAREngine(self.config)
self.engine.data = self.data
assert hasattr(self,
"engine"), "Please specify the exact model engine !"
self.monitor = Monitor(log_dir=self.config["system"]["run_dir"],
delay=1,
gpu_id=self.gpu_id)
print("Start training... ")
epoch_bar = tqdm(range(self.config["model"]["max_epoch"]),
file=sys.stdout)
for epoch in epoch_bar:
print(f"Epoch {epoch} starts !")
print("-" * 80)
if epoch > 0 and self.eval_engine.n_no_update == 0:
# previous epoch have already obtained better result
self.engine.save_checkpoint(model_dir=os.path.join(
self.config["system"]["model_save_dir"], "model.cpk"))
if self.eval_engine.n_no_update >= MAX_N_UPDATE:
print(
"Early stop criterion triggered, no performance update for {:} times"
.format(MAX_N_UPDATE))
break
data_loader = DataLoader(
torch.LongTensor(self.train_data.to_numpy()).to(
self.engine.device),
batch_size=self.config["model"]["batch_size"],
shuffle=True,
drop_last=True,
)
self.engine.train_an_epoch(data_loader, epoch_id=epoch)
self.eval_engine.train_eval(self.data.valid[0], self.data.test[0],
self.engine.model, epoch)
# anneal alpha
self.engine.model.alpha = min(
self.config["model"]["alpha"] +
math.exp(epoch - self.config["model"]["max_epoch"] + 20),
1,
)
"""Sets the learning rate to the initial LR decayed by 10 every 10 epochs"""
lr = self.config["model"]["lr"] * (0.5**(epoch // 10))
for param_group in self.engine.optimizer.param_groups:
param_group["lr"] = lr
self.config["run_time"] = self.monitor.stop()
return self.eval_engine.best_valid_performance