Fixing some stuff

src/data/dataset.py

@@ -71,7 +71,6 @@ class NrvDataset(Dataset):
         self.nrv = torch.tensor(dataframe["nrv"].values).float().reshape(-1)
         self.datetime = dataframe["datetime"]
 
-        print(dataframe.columns)
         self.history_features, self.forecast_features = self.preprocess_data(dataframe)
 
     def skip_samples(self, dataframe):

src/data/preprocessing.py

@@ -253,7 +253,7 @@ class DataProcessor:
         return self.get_dataloader(train_dataset, shuffle=shuffle)
 
     def get_test_dataloader(
-        self, transform: bool = True, predict_sequence_length: int = 96
+        self, transform: bool = True, predict_sequence_length: int = 96, full_day_skip: bool = False
     ):
         test_df = self.all_features.copy()
 
@@ -287,19 +287,19 @@ class DataProcessor:
         test_dataset = NrvDataset(
             test_df,
             data_config=self.data_config,
-            full_day_skip=self.full_day_skip,
+            full_day_skip=self.full_day_skip or full_day_skip,
             predict_sequence_length=predict_sequence_length,
             lstm=self.lstm,
         )
         return self.get_dataloader(test_dataset, shuffle=False)
 
     def get_dataloaders(
-        self, transform: bool = True, predict_sequence_length: int = 96
+        self, transform: bool = True, predict_sequence_length: int = 96, full_day_skip: bool = False
     ):
         return self.get_train_dataloader(
             transform=transform, predict_sequence_length=predict_sequence_length
         ), self.get_test_dataloader(
-            transform=transform, predict_sequence_length=predict_sequence_length
+            transform=transform, predict_sequence_length=predict_sequence_length, full_day_skip=full_day_skip
         )
 
     def inverse_transform(self, input_data):