def test_binary_average_precision_base():
"""
Tests for catalyst.binary_average_precision metric.
"""
outputs = torch.Tensor([0.1, 0.4, 0.35, 0.8])
targets = torch.Tensor([0, 0, 1, 1])
assert torch.isclose(
binary_average_precision(outputs, targets), torch.tensor(0.8333), atol=1e-3
)
def test_binary_average_precision_weighted():
"""
Tests for catalyst.binary_average_precision metric.
"""
target = torch.Tensor([0, 1, 0, 1])
output = torch.Tensor([0.1, 0.2, 0.3, 4])
weight = torch.Tensor([0.5, 1.0, 2.0, 0.1])
ap = binary_average_precision(outputs=output, targets=target, weights=weight)
val = (1 * 0.1 / 0.1 + 0 * 2.0 / 2.1 + 1.1 * 1 / 3.1 + 0 * 1 / 4) / 2.0
assert math.fabs(ap - val) < 0.01, "ap test1 failed"
ap = binary_average_precision(outputs=output, targets=target, weights=None)
val = (1 * 1.0 / 1.0 + 0 * 1.0 / 2.0 + 2 * 1.0 / 3.0 + 0 * 1.0 / 4.0) / 2.0
assert math.fabs(ap - val) < 0.01, "ap test2 failed"
target = torch.Tensor([0, 1, 0, 1])
output = torch.Tensor([4, 3, 2, 1])
weight = torch.Tensor([1, 2, 3, 4])
ap = binary_average_precision(outputs=output, targets=target, weights=weight)
val = (0 * 1.0 / 1.0 + 1.0 * 2.0 / 3.0 + 2.0 * 0 / 6.0 + 6.0 * 1.0 / 10.0) / 2.0
assert math.fabs(ap - val) < 0.01, "ap test3 failed"
ap = binary_average_precision(outputs=output, targets=target, weights=None)
val = (0 * 1.0 + 1 * 1.0 / 2.0 + 0 * 1.0 / 3.0 + 2 * 1.0 / 4.0) / 2.0
assert math.fabs(ap - val) < 0.01, "ap test4 failed"
target = torch.Tensor([0, 1, 0, 1])
output = torch.Tensor([1, 4, 2, 3])
weight = torch.Tensor([1, 2, 3, 4])
ap = binary_average_precision(outputs=output, targets=target, weights=weight)
val = (4 * 1.0 / 4.0 + 6 * 1.0 / 6.0 + 0 * 6.0 / 9.0 + 0 * 6.0 / 10.0) / 2.0
assert math.fabs(ap - val) < 0.01, "ap test5 failed"
ap = binary_average_precision(outputs=output, targets=target, weights=None)
val = (1 * 1.0 + 2 * 1.0 / 2.0 + 0 * 1.0 / 3.0 + 0 * 1.0 / 4.0) / 2.0
assert math.fabs(ap - val) < 0.01, "ap test6 failed"
target = torch.Tensor([0, 0, 0, 0])
output = torch.Tensor([1, 4, 2, 3])
weight = torch.Tensor([1.0, 0.1, 0.0, 0.5])
ap = binary_average_precision(outputs=output, targets=target, weights=weight)
val = 0.0
assert math.fabs(ap - val) < 0.01, "ap test7 failed"
ap = binary_average_precision(outputs=output, targets=target, weights=None)
val = 0.0
assert math.fabs(ap - val) < 0.01, "ap test8 failed"
target = torch.Tensor([1, 1, 0])
output = torch.Tensor([3, 1, 2])
weight = torch.Tensor([1, 0.1, 3])
ap = binary_average_precision(outputs=output, targets=target, weights=weight)
val = (1 * 1.0 / 1.0 + 1 * 0.0 / 4.0 + 1.1 / 4.1) / 2.0
assert math.fabs(ap - val) < 0.01, "ap test9 failed"
ap = binary_average_precision(outputs=output, targets=target, weights=None)
val = (1 * 1.0 + 0 * 1.0 / 2.0 + 2 * 1.0 / 3.0) / 2.0
assert math.fabs(ap - val) < 0.01, "ap test10 failed"
# Test multiple K's
target = torch.Tensor([[0, 1, 0, 1], [0, 1, 0, 1]]).transpose(0, 1)
output = torch.Tensor([[0.1, 0.2, 0.3, 4], [4, 3, 2, 1]]).transpose(0, 1)
weight = torch.Tensor([[1.0, 0.5, 2.0, 3.0]]).transpose(0, 1)
ap = binary_average_precision(outputs=output, targets=target, weights=weight)
assert (
math.fabs(
ap.sum()
- torch.Tensor(
[
(1 * 3.0 / 3.0 + 0 * 3.0 / 5.0 + 3.5 * 1 / 5.5 + 0 * 3.5 / 6.5) / 2.0,
(0 * 1.0 / 1.0 + 1 * 0.5 / 1.5 + 0 * 0.5 / 3.5 + 1 * 3.5 / 6.5) / 2.0,
]
).sum()
)
< 0.01
), "ap test11 failed"
ap = binary_average_precision(outputs=output, targets=target, weights=None)
assert (
math.fabs(
ap.sum()
- torch.Tensor(
[
(1 * 1.0 + 0 * 1.0 / 2.0 + 2 * 1.0 / 3 + 0 * 1.0 / 4.0) / 2.0,
(0 * 1.0 + 1 * 1.0 / 2.0 + 0 * 1.0 / 3.0 + 2.0 * 1.0 / 4.0) / 2.0,
]
).sum()
)
< 0.01
), "ap test12 failed"