black format

qlib/contrib/data/handler.py

@@ -226,7 +226,7 @@ class Alpha158(DataHandlerLP):
             data_loader=data_loader,
             infer_processors=infer_processors,
             learn_processors=learn_processors,
-            process_type=process_type
+            process_type=process_type,
         )
 
     def get_feature_config(self):

qlib/contrib/model/pytorch_alstm.py

@@ -146,7 +146,6 @@ class ALSTM(Model):
 
         raise ValueError("unknown metric `%s`" % self.metric)
 
-
     def train_epoch(self, x_train, y_train):
 
         x_train_values = x_train.values

qlib/contrib/model/pytorch_nn.py

@@ -22,6 +22,7 @@ from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, creat
 from ...log import get_module_logger, TimeInspector
 from ...workflow import R
 
+
 class DNNModelPytorch(Model):
     """DNN Model
 
@@ -349,7 +350,7 @@ class Net(nn.Module):
     def _weight_init(self):
         for m in self.modules():
             if isinstance(m, nn.Linear):
-                nn.init.kaiming_normal_(m.weight, a=0.1, mode='fan_in', nonlinearity='leaky_relu')
+                nn.init.kaiming_normal_(m.weight, a=0.1, mode="fan_in", nonlinearity="leaky_relu")
 
     def forward(self, x):
         cur_output = x

qlib/data/dataset/processor.py

@@ -100,7 +100,8 @@ class DropCol(Processor):
         else:
             mask = df.columns.isin(self.col_list)
         return df.loc[:, ~mask]
-        
+
+
 class TanhProcess(Processor):
     """ Use tanh to process noise data"""
 
@@ -133,6 +134,7 @@ class ProcessInf(Processor):
 
         return replace_inf(df)
 
+
 class Fillna(Processor):
     """Process NaN"""
 
@@ -270,6 +272,7 @@ class CSRankNorm(Processor):
         df[cols] = t
         return df
 
+
 class CSZFillna(Processor):
     """Cross Sectional Fill Nan"""
 
@@ -279,4 +282,4 @@ class CSZFillna(Processor):
     def __call__(self, df):
         cols = get_group_columns(df, self.fields_group)
         df[cols] = df[cols].groupby("datetime").apply(lambda x: x.fillna(x.mean()))
-        return df
+        return df