def test_mean_absolute_relative_error():
a = torch.rand(4)
b = torch.rand(4)
c = torch.rand(4)
d = torch.rand(4)
ground_truth = torch.rand(4)
m = MeanAbsoluteRelativeError()
m.update((a, ground_truth))
abs_error_a = torch.sum(
torch.abs(ground_truth - a) / torch.abs(ground_truth))
num_samples_a = a.size()[0]
sum_error = abs_error_a
sum_samples = num_samples_a
MARE_a = sum_error / sum_samples
assert m.compute() == approx(MARE_a.item())
m.update((b, ground_truth))
abs_error_b = torch.sum(
torch.abs(ground_truth - b) / torch.abs(ground_truth))
num_samples_b = b.size()[0]
sum_error += abs_error_b
sum_samples += num_samples_b
MARE_b = sum_error / sum_samples
assert m.compute() == approx(MARE_b.item())
m.update((c, ground_truth))
abs_error_c = torch.sum(
torch.abs(ground_truth - c) / torch.abs(ground_truth))
num_samples_c = c.size()[0]
sum_error += abs_error_c
sum_samples += num_samples_c
MARE_c = sum_error / sum_samples
assert m.compute() == approx(MARE_c.item())
m.update((d, ground_truth))
abs_error_d = torch.sum(
torch.abs(ground_truth - d) / torch.abs(ground_truth))
num_samples_d = d.size()[0]
sum_error += abs_error_d
sum_samples += num_samples_d
MARE_d = sum_error / sum_samples
assert m.compute() == approx(MARE_d.item())
def _test(metric_device):
metric_device = torch.device(metric_device)
m = MeanAbsoluteRelativeError(device=metric_device)
torch.manual_seed(10 + rank)
y_pred = torch.randint(1, 11, size=(10,), device=device).float()
y = torch.randint(1, 11, size=(10,), device=device).float()
m.update((y_pred, y))
# gather y_pred, y
y_pred = idist.all_gather(y_pred)
y = idist.all_gather(y)
np_y_pred = y_pred.cpu().numpy()
np_y = y.cpu().numpy()
res = m.compute()
abs_error = np.sum(abs(np_y - np_y_pred) / abs(np_y))
num_samples = len(y_pred)
np_res = abs_error / num_samples
assert np_res == approx(res)
def test_zero_sample():
m = MeanAbsoluteRelativeError()
with raises(NotComputableError):
m.compute()
def test_zero_sample():
m = MeanAbsoluteRelativeError()
with raises(
NotComputableError,
match=r"MeanAbsoluteRelativeError must have at least one sample"):
m.compute()