updated precision test to fit new micro_averaging argument in dataloader

Author: Johanmkr

tests/test_metrics.py

@@ -38,7 +38,7 @@ def test_precision_case1():
     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)
+        P = Precision(3, micro_averaging=boolean)
         precision1 = P(true1, pred1)
         assert precision1.allclose(torch.tensor(true_precision), atol=1e-5), (
             f"Precision Score: {precision1.item()}"
@@ -51,7 +51,7 @@ def test_precision_case2():
     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)
+        P = Precision(5, micro_averaging=boolean)
         precision2 = P(true2, pred2)
         assert precision2.allclose(torch.tensor(true_precision), atol=1e-5), (
             f"Precision Score: {precision2.item()}"
@@ -64,7 +64,7 @@ def test_precision_case3():
     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)
+        P = Precision(2, micro_averaging=boolean)
         precision3 = P(true3, pred3)
         assert precision3.allclose(torch.tensor(true_precision), atol=1e-5), (
             f"Precision Score: {precision3.item()}"
@@ -77,7 +77,7 @@ def test_for_zero_denominator():
     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)
+        P = Precision(2, micro_averaging=boolean)
         precision4 = P(true4, pred4)
         assert precision4.allclose(torch.tensor(0.0), atol=1e-5), (
             f"Precision Score: {precision4.item()}"