adjusted accuracy test and test_metric_wrapper to work with new method names

Author: hzavadil98

CollaborativeCoding/metrics/accuracy.py

@@ -1,5 +1,6 @@
 import torch
 from torch import nn
+import numpy as np
 
 
 class Accuracy(nn.Module):
@@ -80,7 +81,7 @@ def _micro_acc(self):
 
     def __returnmetric__(self):
         if self.y_true == [] or self.y_pred == []:
-            return 0.0
+            return np.nan
         if isinstance(self.y_true,list):
             if len(self.y_true) == 1:
                 self.y_true = self.y_true[0]
@@ -96,28 +97,3 @@ def __reset__(self):
         return None
 
 
-if __name__ == "__main__":
-    # Test the accuracy metric
-    y_true = torch.tensor([0, 1, 2, 3, 4, 5])
-    y_pred = torch.tensor([0, 1, 2, 3, 4, 5])
-    accuracy = Accuracy(num_classes=6, macro_averaging=False)
-    accuracy(y_true, y_pred)
-    print(accuracy.__returnmetric__())  # 1.0
-    accuracy.__resetmetric__()
-    print(accuracy.__returnmetric__())  # 0.0
-    y_pred = torch.tensor([0, 1, 2, 3, 4, 4])
-    accuracy(y_true, y_pred)
-    print(accuracy.__returnmetric__())  # 0.8333333134651184
-    accuracy.__resetmetric__()
-    print(accuracy.__returnmetric__())  # 0.0
-    accuracy.macro_averaging = True
-    accuracy(y_true, y_pred)
-    y_true_1 = torch.tensor([0, 1, 2, 3, 4, 5])
-    y_pred_1 = torch.tensor([0, 1, 2, 3, 4, 4])
-    accuracy(y_true_1, y_pred_1)
-    print(accuracy.__returnmetric__())  # 0.9166666865348816
-    accuracy.macro_averaging = False
-    print(accuracy.__returnmetric__())  # 0.8333333134651184
-    accuracy.__resetmetric__()
-    print(accuracy.__returnmetric__())  # 0.0
-    print(accuracy.__resetmetric__())  # None

tests/test_metrics.py

@@ -3,7 +3,7 @@
 import pytest
 
 from CollaborativeCoding.load_metric import MetricWrapper
-from CollaborativeCoding.metrics import Accuracy, F1Score, Precision, Recall
+from CollaborativeCoding.metrics import Accuracy, F1Score, Precision, Recall, EntropyPrediction
 
 
 @pytest.mark.parametrize(
@@ -34,9 +34,9 @@ def test_metric_wrapper(metric, num_classes, macro_averaging):
     )
 
     metrics(y_true, logits)
-    score = metrics.accumulate()
-    metrics.reset()
-    empty_score = metrics.accumulate()
+    score = metrics.__getmetrics__()
+    metrics.__resetmetrics__()
+    empty_score = metrics.__getmetrics__()
 
     assert isinstance(score, dict), "Expected a dictionary output."
     assert metric in score, f"Expected {metric} metric in the output."
@@ -129,17 +129,30 @@ def test_precision():
 
 def test_accuracy():
     import torch
+    import numpy as np
 
-    accuracy = Accuracy(num_classes=5)
-
-    y_true = torch.tensor([0, 3, 2, 3, 4])
-    y_pred = torch.tensor([0, 1, 2, 3, 4])
-
-    accuracy_score = accuracy(y_true, y_pred)
+    # Test the accuracy metric
+    y_true = torch.tensor([0, 1, 2, 3, 4, 5])
+    y_pred = torch.tensor([0, 1, 2, 3, 4, 5])
+    accuracy = Accuracy(num_classes=6, macro_averaging=False)
+    accuracy(y_true, y_pred)
+    assert accuracy.__returnmetric__() == 1.0, "Expected accuracy to be 1.0"
+    accuracy.__reset__()
+    assert accuracy.__returnmetric__() is np.nan, "Expected accuracy to be 0.0"
+    y_pred = torch.tensor([0, 1, 2, 3, 4, 4])
+    accuracy(y_true, y_pred)
+    assert np.abs(accuracy.__returnmetric__() - 0.8333333134651184) < 1e-5, "Expected accuracy to be 0.8333333134651184"
+    accuracy.__reset__()
+    accuracy.macro_averaging = True
+    accuracy(y_true, y_pred)
+    y_true_1 = torch.tensor([0, 1, 2, 3, 4, 5])
+    y_pred_1 = torch.tensor([0, 1, 2, 3, 4, 4])
+    accuracy(y_true_1, y_pred_1)
+    assert np.abs(accuracy.__returnmetric__() - 0.8333333134651184) < 1e-5, "Expected accuracy to be 0.8333333134651186"
+    accuracy.macro_averaging = False
+    assert np.abs(accuracy.__returnmetric__() - 0.8333333134651184) < 1e-5, "Expected accuracy to be 0.8333333134651184"
+    accuracy.__reset__()
 
-    assert torch.abs(torch.tensor(accuracy_score - 0.8)) < 1e-5, (
-        f"Accuracy Score: {accuracy_score.item()}"
-    )
 
 
 def test_entropypred():