added precision test to test_metrics.py

Author: Johanmkr

tests/test_metrics.py

@@ -1,4 +1,4 @@
-from utils.metrics import Recall, F1Score
+from utils.metrics import F1Score, Precision, Recall
 
 
 def test_recall():
@@ -30,3 +30,55 @@ def test_f1score():
     assert f1_metric.tp.sum().item() > 0, "Expected some true positives."
     assert f1_metric.fp.sum().item() > 0, "Expected some false positives."
     assert f1_metric.fn.sum().item() > 0, "Expected some false negatives."
+
+
+def test_precision_case1():
+    import torch
+
+    for boolean, true_precision in zip([True, False], [25.0 / 36, 7.0 / 10]):
+        true1 = torch.tensor([0, 1, 2, 1, 0, 2, 1, 0, 2, 1])
+        pred1 = torch.tensor([0, 2, 1, 1, 0, 2, 0, 0, 2, 1])
+        P = Precision(3, use_mean=boolean)
+        precision1 = P(true1, pred1)
+        assert precision1.allclose(torch.tensor(true_precision), atol=1e-5), (
+            f"Precision Score: {precision1.item()}"
+        )
+
+
+def test_precision_case2():
+    import torch
+
+    for boolean, true_precision in zip([True, False], [8.0 / 15, 6.0 / 15]):
+        true2 = torch.tensor([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4])
+        pred2 = torch.tensor([0, 0, 4, 3, 4, 0, 4, 4, 2, 3, 4, 1, 2, 4, 0])
+        P = Precision(5, use_mean=boolean)
+        precision2 = P(true2, pred2)
+        assert precision2.allclose(torch.tensor(true_precision), atol=1e-5), (
+            f"Precision Score: {precision2.item()}"
+        )
+
+
+def test_precision_case3():
+    import torch
+
+    for boolean, true_precision in zip([True, False], [3.0 / 4, 4.0 / 5]):
+        true3 = torch.tensor([0, 0, 0, 1, 0])
+        pred3 = torch.tensor([1, 0, 0, 1, 0])
+        P = Precision(2, use_mean=boolean)
+        precision3 = P(true3, pred3)
+        assert precision3.allclose(torch.tensor(true_precision), atol=1e-5), (
+            f"Precision Score: {precision3.item()}"
+        )
+
+
+def test_for_zero_denominator():
+    import torch
+
+    for boolean in [True, False]:
+        true4 = torch.tensor([1, 1, 1, 1, 1])
+        pred4 = torch.tensor([0, 0, 0, 0, 0])
+        P = Precision(2, use_mean=boolean)
+        precision4 = P(true4, pred4)
+        assert precision4.allclose(torch.tensor(0.0), atol=1e-5), (
+            f"Precision Score: {precision4.item()}"
+        )