def test_binary_precision():
"""
precision = tp/tp+fp
"""
y_pred = torch.tensor([0.8, 0.1, 0.7, 0.1])
metric_test_case(y_pred, torch.tensor([1, 1, 1, 1]), BinaryPrecision(), 1)
metric_test_case(y_pred, torch.tensor([1, 0, 1, 0]), BinaryPrecision(), 1)
metric_test_case(y_pred, torch.tensor([0, 0, 0, 0]), BinaryPrecision(), 0)
metric_test_case(y_pred, torch.tensor([0, 1, 0, 1]), BinaryPrecision(), 0)
# fp fn tp fn
metric_test_case(y_pred, torch.tensor([0, 1, 1, 1]), BinaryPrecision(),
0.5)
# tp tp
metric_test_case(y_pred, torch.tensor([0, 1, 1, 1]), BinaryPrecision(0.65),
0.5)
# tp tp
metric_test_case(y_pred, torch.tensor([0, 1, 1, 1]), BinaryPrecision(0.75),
0)
# tp fn tp tn
metric_test_case(y_pred, torch.tensor([1, 1, 1, 0]), BinaryPrecision(), 1)
def test_inverted_binary_precision():
"""
precision = tp/tp+fp
"""
y_pred = torch.tensor([0.8, 0.1, 0.7, 0.1])
metric_test_case(y_pred, torch.tensor([1, 1, 1, 1]), BinaryPrecision(positive_label=0), 0)
metric_test_case(y_pred, torch.tensor([1, 0, 1, 0]), BinaryPrecision(positive_label=0), 1)
metric_test_case(y_pred, torch.tensor([0, 0, 0, 0]), BinaryPrecision(positive_label=0), 1)
metric_test_case(y_pred, torch.tensor([0, 1, 0, 1]), BinaryPrecision(positive_label=0), 0)
metric_test_case(y_pred, torch.tensor([0, 0, 0, 1]), BinaryPrecision(positive_label=0), 0.5)
metric_test_case(y_pred, torch.tensor([0, 1, 1, 1]), BinaryPrecision(positive_label=0), 0)
metric_test_case(y_pred, torch.tensor([0, 0, 1, 1]), BinaryPrecision(0.65, positive_label=0), 0.5)
metric_test_case(y_pred, torch.tensor([0, 0, 0, 1]), BinaryPrecision(0.75, positive_label=0), 2 / 3)