Auto-format: Applied ruff format and isort

Author: github-actions[bot]

utils/__init__.py

@@ -1,4 +1,4 @@
+from .createfolders import createfolders
+from .load_data import load_data
 from .load_metric import MetricWrapper
 from .load_model import load_model
-from .load_data import load_data
-from .createfolders import createfolders

utils/createfolders.py

@@ -1,41 +1,56 @@
-import os 
-from tempfile import TemporaryDirectory
 import argparse
+import os
+from tempfile import TemporaryDirectory
+
+
+def createfolders(args) -> None:
+    """
+    Creates folders for storing data, results, model weights.
 
-def createfolders(args) -> None: 
-    '''
-    Creates folders for storing data, results, model weights. 
-    
     Parameters
     ----------
     args
         ArgParse object containing string paths to be created
-    
-    '''
-    
+
+    """
+
     if not os.path.exists(args.datafolder):
         os.makedirs(args.datafolder)
-        print(f'Created a folder at {args.datafolder}')
-        
+        print(f"Created a folder at {args.datafolder}")
+
     if not os.path.exists(args.resultfolder):
         os.makedirs(args.resultfolder)
-        print(f'Created a folder at {args.resultfolder}')
-        
+        print(f"Created a folder at {args.resultfolder}")
+
     if not os.path.exists(args.modelfolder):
         os.makedirs(args.modelfolder)
-        print(f'Created a folder at {args.modelfolder}')
-
+        print(f"Created a folder at {args.modelfolder}")
 
 
 def test_createfolders():
-    with TemporaryDirectory(dir = 'tmp/') as temp_dir:
+    with TemporaryDirectory(dir="tmp/") as temp_dir:
         parser = argparse.ArgumentParser()
-        #Structuture related values
-        parser.add_argument('--datafolder', type=str, default=os.path.join(temp_dir, 'Data/'), help='Path to where data will be saved during training.')
-        parser.add_argument('--resultfolder', type=str, default=os.path.join(temp_dir, 'Results/'), help='Path to where results will be saved during evaluation.')
-        parser.add_argument('--modelfolder', type=str, default=os.path.join(temp_dir, 'Experiments/'), help='Path to where model weights will be saved at the end of training.')
-        
+        # Structuture related values
+        parser.add_argument(
+            "--datafolder",
+            type=str,
+            default=os.path.join(temp_dir, "Data/"),
+            help="Path to where data will be saved during training.",
+        )
+        parser.add_argument(
+            "--resultfolder",
+            type=str,
+            default=os.path.join(temp_dir, "Results/"),
+            help="Path to where results will be saved during evaluation.",
+        )
+        parser.add_argument(
+            "--modelfolder",
+            type=str,
+            default=os.path.join(temp_dir, "Experiments/"),
+            help="Path to where model weights will be saved at the end of training.",
+        )
+
         args = parser.parse_args()
         createfolders(args)
-        
-    return 
+
+    return

utils/dataloaders/svhn.py

@@ -1,11 +1,12 @@
 from torch.utils.data import Dataset
 
+
 class SVHN(Dataset):
     def __init__(self):
         super().__init__()
-    
+
     def __len__(self):
-        return 
-    
+        return
+
     def __getitem__(self, index):
-        return
+        return

utils/load_data.py

@@ -1,5 +1,7 @@
 from torch.utils.data import Dataset
 
-def load_data(dataset:str) -> Dataset:
-    
-    raise ValueError(f'Dataset: {dataset} not implemented. \nCheck the documentation for implemented metrics, or check your spelling')
+
+def load_data(dataset: str) -> Dataset:
+    raise ValueError(
+        f"Dataset: {dataset} not implemented. \nCheck the documentation for implemented metrics, or check your spelling"
+    )

utils/load_metric.py

@@ -1,6 +1,7 @@
-import copy 
-import numpy as np 
-import torch.nn as nn 
+import copy
+
+import numpy as np
+import torch.nn as nn
 from metrics import EntropyPrediction
 
 
@@ -11,12 +12,11 @@ def __init__(self, *metrics):
 
         for metric in metrics:
             self.metrics[metric] = self._get_metric(metric)
-        
+
         self.tmp_scores = copy.deepcopy(self.metrics)
         for key in self.tmp_scores:
             self.tmp_scores[key] = []
 
-
     def _get_metric(self, key):
         """
         Get the metric function based on the key
@@ -31,28 +31,28 @@ def _get_metric(self, key):
         """
 
         match key.lower():
-            case 'entropy':
+            case "entropy":
                 return EntropyPrediction()
-            case 'f1':
+            case "f1":
                 raise NotImplementedError("F1 score not implemented yet")
-            case 'recall':
+            case "recall":
                 raise NotImplementedError("Recall score not implemented yet")
-            case 'precision':
+            case "precision":
                 raise NotImplementedError("Precision score not implemented yet")
-            case 'accuracy':
+            case "accuracy":
                 raise NotImplementedError("Accuracy score not implemented yet")
             case _:
                 raise ValueError(f"Metric {key} not supported")
 
     def __call__(self, y_true, y_pred):
         for key in self.metrics:
             self.tmp_scores[key].append(self.metrics[key](y_true, y_pred))
-        
+
     def __getmetrics__(self):
         return_metrics = {}
         for key in self.metrics:
             return_metrics[key] = np.mean(self.tmp_scores[key])
-        
+
         return return_metrics
 
     def __resetvalues__(self):

utils/load_model.py

@@ -1,9 +1,11 @@
-import torch.nn as nn 
+import torch.nn as nn
 from models import MagnusModel
 
-def load_model(modelname:str) -> nn.Module:
-    
-    if modelname == 'MagnusModel':
+
+def load_model(modelname: str) -> nn.Module:
+    if modelname == "MagnusModel":
         return MagnusModel()
     else:
-        raise ValueError(f'Model: {modelname} has not been implemented. \nCheck the documentation for implemented metrics, or check your spelling')
+        raise ValueError(
+            f"Model: {modelname} has not been implemented. \nCheck the documentation for implemented metrics, or check your spelling"
+        )

utils/metrics/EntropyPred.py

@@ -1,10 +1,9 @@
-import torch.nn as nn 
+import torch.nn as nn
 
 
 class EntropyPrediction(nn.Module):
     def __init__(self):
         super().__init__()
-        
+
     def __call__(self, y_true, y_false):
-        
-        return
+        return

utils/metrics/__init__.py

@@ -1 +1 @@
-from .EntropyPred import EntropyPrediction
+from .EntropyPred import EntropyPrediction

utils/models/__init__.py

@@ -1 +1 @@
-from .magnus_model import MagnusModel
+from .magnus_model import MagnusModel

utils/models/magnus_model.py

@@ -1,8 +1,9 @@
-import torch.nn as nn 
+import torch.nn as nn
+
 
 class MagnusModel(nn.Module):
     def __init__(self):
         super().__init__()
-        
+
     def forward(self, x):
-        return 
+        return