Adjust rolling api (#1594)

* Intermediate version

* Fix yaml template & Successfully run rolling

* Be compatible with benchmark

* Get same results with previous linear model

* Black formatting

* Update black

* Update the placeholder mechanism

* Update CI

* Update CI

* Upgrade Black

* Fix CI and simplify code

* Fix CI

* Move the data processing caching mechanism into utils.

* Adjusting DDG-DA

* Organize import

qlib/contrib/model/pytorch_lstm.py

@@ -142,7 +142,6 @@ class LSTM(Model):
         raise ValueError("unknown loss `%s`" % self.loss)
 
     def metric_fn(self, pred, label):
-
         mask = torch.isfinite(label)
 
         if self.metric in ("", "loss"):
@@ -151,7 +150,6 @@ class LSTM(Model):
         raise ValueError("unknown metric `%s`" % self.metric)
 
     def train_epoch(self, x_train, y_train):
-
         x_train_values = x_train.values
         y_train_values = np.squeeze(y_train.values)
 
@@ -161,7 +159,6 @@ class LSTM(Model):
         np.random.shuffle(indices)
 
         for i in range(len(indices))[:: self.batch_size]:
-
             if len(indices) - i < self.batch_size:
                 break
 
@@ -177,7 +174,6 @@ class LSTM(Model):
             self.train_optimizer.step()
 
     def test_epoch(self, data_x, data_y):
-
         # prepare training data
         x_values = data_x.values
         y_values = np.squeeze(data_y.values)
@@ -190,7 +186,6 @@ class LSTM(Model):
         indices = np.arange(len(x_values))
 
         for i in range(len(indices))[:: self.batch_size]:
-
             if len(indices) - i < self.batch_size:
                 break
 
@@ -212,7 +207,6 @@ class LSTM(Model):
         evals_result=dict(),
         save_path=None,
     ):
-
         df_train, df_valid, df_test = dataset.prepare(
             ["train", "valid", "test"],
             col_set=["feature", "label"],