Fix config and format

examples/benchmarks/HATS/worflow_config_hats.yaml

@@ -27,7 +27,7 @@ port_analysis_config: &port_analysis_config
 task:
     model:
         class: HATS
-        module_path: qlib.contrib.model.pytorch_gats
+        module_path: qlib.contrib.model.pytorch_hats
         kwargs:
             d_feat: 6
             hidden_size: 64

qlib/contrib/model/pytorch_sfm.py

@@ -21,6 +21,7 @@ from ...model.base import Model
 from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 
+
 class SFM_Model(nn.Module):
     def __init__(self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu"):
         super().__init__()
@@ -71,7 +72,7 @@ class SFM_Model(nn.Module):
 
         for ts in range(time_step):
             x = input[:, ts, :]
-            if(len(self.states)==0): #hasn't initialized yet
+            if len(self.states) == 0:  # hasn't initialized yet
                 self.init_states(x)
             self.get_constants(x)
             p_tm1 = self.states[0]
@@ -89,8 +90,9 @@ class SFM_Model(nn.Module):
             x_c = torch.matmul(x * B_W[0], self.W_c) + self.b_c
             x_o = torch.matmul(x * B_W[0], self.W_o) + self.b_o
 
-            i = self.inner_activation(x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)) # not sure whether I am doing in the right unsquuze
-            
+            i = self.inner_activation(
+                x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)
+            )  # not sure whether I am doing in the right unsquuze
 
             ste = self.inner_activation(x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste))
             fre = self.inner_activation(x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre))
@@ -152,13 +154,14 @@ class SFM_Model(nn.Module):
 
     def get_constants(self, x):
         constants = []
-        constants.append([torch.tensor(1.).to(self.device) for _ in range(6)])
-        constants.append([torch.tensor(1.).to(self.device) for _ in range(7)])
+        constants.append([torch.tensor(1.0).to(self.device) for _ in range(6)])
+        constants.append([torch.tensor(1.0).to(self.device) for _ in range(7)])
         array = np.array([float(ii) / self.freq_dim for ii in range(self.freq_dim)])
         constants.append(torch.tensor(array).to(self.device))
 
         self.states[5:] = constants
 
+
 class SFM(Model):
     """SFM Model
 
@@ -221,7 +224,7 @@ class SFM(Model):
         self.lr_decay_steps = lr_decay_steps
         self.optimizer = optimizer.lower()
         self.loss_type = loss
-        self.device = 'cuda:%d'%(GPU) if torch.cuda.is_available() else 'cpu'
+        self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu"
         self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
@@ -277,7 +280,7 @@ class SFM(Model):
             freq_dim=self.freq_dim,
             dropout_W=self.dropout_W,
             dropout_U=self.dropout_U,
-            device = self.device
+            device=self.device,
         )
         if optimizer.lower() == "adam":
             self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr)
@@ -303,14 +306,7 @@ class SFM(Model):
         self._fitted = False
         self.sfm_model.to(self.device)
 
-    def fit(
-        self,
-        dataset: DatasetH,
-        evals_result=dict(),
-        verbose=True,
-        save_path=None,
-        **kwargs
-    ):
+    def fit(self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, **kwargs):
 
         df_train, df_valid = dataset.prepare(
             ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
@@ -399,7 +395,7 @@ class SFM(Model):
                 # update learning rate
                 self.scheduler.step(cur_loss_val)
 
-        if self.device != 'cpu':
+        if self.device != "cpu":
             torch.cuda.empty_cache()
 
     def get_loss(self, pred, target, loss_type):
@@ -432,11 +428,11 @@ class SFM(Model):
 
             x_batch = torch.from_numpy(x_values[begin:end]).float()
 
-            if self.device != 'cpu':
+            if self.device != "cpu":
                 x_batch = x_batch.to(self.device)
 
             with torch.no_grad():
-                if self.device != 'cpu':
+                if self.device != "cpu":
                     pred = self.sfm_model(x_batch).detach().cpu().numpy()
                 else:
                     pred = self.sfm_model(x_batch).detach().cpu().numpy()
@@ -461,8 +457,10 @@ class SFM(Model):
             self.sfm_model.load_state_dict(torch.load(_model_path))
         self._fitted = True
 
+
 class AverageMeter(object):
     """Computes and stores the average and current value"""
+
     def __init__(self):
         self.reset()