formatted to pass tests

Author: hzavadil98

tests/test_metrics.py

@@ -1,6 +1,4 @@
-
-from utils.metrics import F1Score, Precision, Recall, Accuracy
-
+from utils.metrics import Accuracy, F1Score, Precision, Recall
 
 
 def test_recall():
@@ -84,7 +82,8 @@ def test_for_zero_denominator():
         assert precision4.allclose(torch.tensor(0.0), atol=1e-5), (
             f"Precision Score: {precision4.item()}"
         )
-        
+
+
 def test_accuracy():
     import torch
 

tests/test_models.py

@@ -21,6 +21,7 @@ def test_christian_model(image_shape, num_classes):
         f"Softmax output should sum to 1, but got: {y.sum()}"
     )
 
+
 @pytest.mark.parametrize(
     "image_shape, num_classes",
     [((1, 28, 28), 4), ((3, 16, 16), 10)],
@@ -34,4 +35,3 @@ def test_jan_model(image_shape, num_classes):
     y = model(x)
 
     assert y.shape == (n, num_classes), f"Shape: {y.shape}"
-    

utils/load_metric.py

@@ -3,7 +3,7 @@
 import numpy as np
 import torch.nn as nn
 
-from .metrics import EntropyPrediction, F1Score, Precision, Accuracy
+from .metrics import Accuracy, EntropyPrediction, F1Score, Precision
 
 
 class MetricWrapper(nn.Module):

utils/metrics/__init__.py

@@ -1,7 +1,7 @@
 __all__ = ["EntropyPrediction", "Recall", "F1Score", "Precision", "Accuracy"]
 
+from .accuracy import Accuracy
 from .EntropyPred import EntropyPrediction
 from .F1 import F1Score
 from .precision import Precision
 from .recall import Recall
-from .accuracy import Accuracy

utils/metrics/accuracy.py

@@ -23,10 +23,11 @@ def forward(self, y_true, y_pred):
             Accuracy score.
         """
         return (y_true == y_pred).float().mean().item()
-    
+
+
 if __name__ == "__main__":
     y_true = torch.tensor([0, 3, 2, 3, 4])
     y_pred = torch.tensor([0, 1, 2, 3, 4])
 
     accuracy = Accuracy()
-    print(accuracy(y_true, y_pred))
+    print(accuracy(y_true, y_pred))