formatting

icu_benchmarks/imputation/diffwave.py

@@ -330,7 +330,7 @@ def forward(self, input_data):
         cond = self.cond_conv(cond)
         h += cond
 
-        out = torch.tanh(h[:, :self.res_channels, :]) * torch.sigmoid(h[:, self.res_channels:, :])
+        out = torch.tanh(h[:, : self.res_channels, :]) * torch.sigmoid(h[:, self.res_channels:, :])
 
         res = self.res_conv(out)
         assert x.shape == res.shape

icu_benchmarks/models/custom_metrics.py

@@ -422,30 +422,30 @@ def norm_function(arr):
             x_original = dataloader.dataset.data["reals"].clone()
 
             dataloader.dataset.add_noise()
-            x_preturb = dataloader.dataset.data["reals"].clone()
-            y_pred_preturb = model.model.predict(dataloader)
+            x_perturb = dataloader.dataset.data["reals"].clone()
+            y_pred_perturb = model.model.predict(dataloader)
             Attention_weights = model.interpertations(dataloader)
-            att_preturb = Attention_weights["attention"]
+            att_perturb = Attention_weights["attention"]
             # Calculate the absolute difference
-            difference = torch.abs(y_pred_preturb - y_pred)
+            difference = torch.abs(y_pred_perturb - y_pred)
 
             # Find where the difference is less than or equal to a thershold
             close_indices = torch.nonzero(difference <= thershold).squeeze()[:, 0].to(device)
 
             RIS = relative_stability_objective(
                 x_original.detach(),
-                x_preturb.detach(),
+                x_perturb.detach(),
                 attribution,
-                att_preturb,
+                att_perturb,
                 close_indices=close_indices,
                 input=True,
                 attention=True,
             )
             ROS = relative_stability_objective(
                 y_pred,
-                y_pred_preturb,
+                y_pred_perturb,
                 attribution,
-                att_preturb,
+                att_perturb,
                 close_indices=close_indices,
                 input=False,
                 attention=True,
@@ -458,16 +458,16 @@ def norm_function(arr):
             with torch.no_grad():
                 noise = torch.randn_like(x["encoder_cont"]) * 0.01
                 x["encoder_cont"] += noise
-            y_pred_preturb = model(model.prep_data(x)).detach()
+            y_pred_perturb = model(model.prep_data(x)).detach()
             if explain_method == "Random":
-                att_preturb = np.random.normal(size=[64, 24, 53])
+                att_perturb = np.random.normal(size=[64, 24, 53])
 
             else:
-                att_preturb, features_attrs, timestep_attrs = model.explantation2(x, explain_method)
+                att_perturb, features_attrs, timestep_attrs = model.explantation2(x, explain_method)
 
                 #
             # Calculate the absolute difference
-            difference = torch.abs(y_pred_preturb - y_pred)
+            difference = torch.abs(y_pred_perturb - y_pred)
 
             # Find where the difference is less than or equal to a thershold
             close_indices = torch.nonzero(difference <= thershold).squeeze()[:, 0].to(device)
@@ -476,15 +476,15 @@ def norm_function(arr):
                 x_original.detach(),
                 x["encoder_cont"].detach(),
                 attribution,
-                att_preturb,
+                att_perturb,
                 close_indices=close_indices,
                 input=True,
             )
             ROS = relative_stability_objective(
                 y_pred,
-                y_pred_preturb,
+                y_pred_perturb,
                 attribution,
-                att_preturb,
+                att_perturb,
                 close_indices=close_indices,
                 input=False,
             )