Fixed policy evaluation for autoregressive

src/trainers/trainer.py

@@ -8,6 +8,7 @@ from plotly.subplots import make_subplots
 from clearml.config import running_remotely
 from torchinfo import summary
 
+
 class Trainer:
     def __init__(
         self,
@@ -95,13 +96,15 @@ class Trainer:
             loader = test_loader
 
         np.random.seed(42)
-        actual_indices = np.random.choice(loader.dataset.full_day_valid_indices, num_samples, replace=False)
+        actual_indices = np.random.choice(
+            loader.dataset.full_day_valid_indices, num_samples, replace=False
+        )
         indices = {}
         for i in actual_indices:
             indices[i] = loader.dataset.valid_indices.index(i)
 
         print(actual_indices)
-        
+
         return indices
 
     def train(self, epochs: int, remotely: bool = False, task: Task = None):
@@ -190,9 +193,7 @@ class Trainer:
                             # )
 
                         if hasattr(self, "calculate_crps_from_samples"):
-                            self.calculate_crps_from_samples(
-                                task, full_day_skip_test_loader, epoch
-                            )
+                            self.calculate_crps_from_samples(task, test_loader, epoch)
 
             if task:
                 self.finish_training(task=task)
@@ -259,7 +260,6 @@ class Trainer:
             self.model = torch.load("checkpoint.pt")
         self.model.eval()
 
-        
         # set full day skip
         self.data_processor.set_full_day_skip(True)
         train_loader, test_loader = self.data_processor.get_dataloaders(
@@ -361,7 +361,6 @@ class Trainer:
             for trace in sub_fig.data:
                 fig.add_trace(trace, row=row, col=col)
 
-
             # loss = self.criterion(predictions.to(self.device), target.squeeze(-1).to(self.device)).item()
 
             # fig['layout']['annotations'][i].update(text=f"{loss.__class__.__name__}: {loss:.6f}")