Black(new version) Format

qlib/contrib/model/double_ensemble.py

@@ -160,7 +160,7 @@ class DEnsembleModel(Model, FeatureInt):
         h_avg = h.groupby("bins")["h_value"].mean()
         weights = pd.Series(np.zeros(N, dtype=float))
         for i_b, b in enumerate(h_avg.index):
-            weights[h["bins"] == b] = 1.0 / (self.decay ** k_th * h_avg[i_b] + 0.1)
+            weights[h["bins"] == b] = 1.0 / (self.decay**k_th * h_avg[i_b] + 0.1)
         return weights
 
     def feature_selection(self, df_train, loss_values):

qlib/contrib/model/pytorch_adarnn.py

@@ -682,9 +682,9 @@ class MMD_loss(nn.Module):
         if fix_sigma:
             bandwidth = fix_sigma
         else:
-            bandwidth = torch.sum(L2_distance.data) / (n_samples ** 2 - n_samples)
+            bandwidth = torch.sum(L2_distance.data) / (n_samples**2 - n_samples)
         bandwidth /= kernel_mul ** (kernel_num // 2)
-        bandwidth_list = [bandwidth * (kernel_mul ** i) for i in range(kernel_num)]
+        bandwidth_list = [bandwidth * (kernel_mul**i) for i in range(kernel_num)]
         kernel_val = [torch.exp(-L2_distance / bandwidth_temp) for bandwidth_temp in bandwidth_list]
         return sum(kernel_val)
 

qlib/contrib/model/pytorch_tra.py

@@ -742,7 +742,7 @@ def evaluate(pred):
     score = pred.score
     label = pred.label
     diff = score - label
-    MSE = (diff ** 2).mean()
+    MSE = (diff**2).mean()
     MAE = (diff.abs()).mean()
     IC = score.corr(label, method="spearman")
     return {"MSE": MSE, "MAE": MAE, "IC": IC}

qlib/contrib/model/pytorch_utils.py

@@ -27,11 +27,11 @@ def count_parameters(models_or_parameters, unit="m"):
         counts = sum(v.numel() for v in models_or_parameters)
     unit = unit.lower()
     if unit in ("kb", "k"):
-        counts /= 2 ** 10
+        counts /= 2**10
     elif unit in ("mb", "m"):
-        counts /= 2 ** 20
+        counts /= 2**20
     elif unit in ("gb", "g"):
-        counts /= 2 ** 30
+        counts /= 2**30
     elif unit is not None:
         raise ValueError("Unknown unit: {:}".format(unit))
     return counts

qlib/contrib/model/tcn.py

@@ -55,7 +55,7 @@ class TemporalConvNet(nn.Module):
         layers = []
         num_levels = len(num_channels)
         for i in range(num_levels):
-            dilation_size = 2 ** i
+            dilation_size = 2**i
             in_channels = num_inputs if i == 0 else num_channels[i - 1]
             out_channels = num_channels[i]
             layers += [