def train(self):
"""Train the model."""
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"])
if self.config["model"]["loss"] == "bpr":
train_loader = self.data.instance_bpr_loader(
batch_size=self.config["model"]["batch_size"],
device=self.config["model"]["device_str"],
)
elif self.config["model"]["loss"] == "bce":
train_loader = self.data.instance_bce_loader(
num_negative=self.config["model"]["num_negative"],
batch_size=self.config["model"]["batch_size"],
device=self.config["model"]["device_str"],
)
else:
raise ValueError(
f"Unsupported loss type {self.config['loss']}, try other options: 'bpr' or 'bce'"
)
self.engine = NGCFEngine(self.config)
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 __init__(self, config):
"""Initialize NGCF_train Class.
Args:
config (dict): All the parameters for the model.
"""
self.config = config
super(NGCF_train, self).__init__(self.config)
self.load_dataset()
self.build_data_loader()
self.engine = NGCFEngine(self.config["model"])
def __init__(self, config):
"""Constructor
Args:
config (dict): All the parameters for the model
"""
self.config = config
super(NGCF_train, self).__init__(self.config)
self.load_dataset()
self.build_data_loader()
self.engine = NGCFEngine(self.config)