def test_median_relative_absolute_error_2():
np.random.seed(1)
size = 105
np_y_pred = np.random.rand(size, 1)
np_y = np.random.rand(size, 1)
np.random.shuffle(np_y)
np_median_absolute_relative_error = np.median(
np.abs(np_y - np_y_pred) / np.abs(np_y - np_y.mean()))
m = MedianRelativeAbsoluteError()
y_pred = torch.from_numpy(np_y_pred)
y = torch.from_numpy(np_y)
m.reset()
batch_size = 16
n_iters = size // batch_size + 1
for i in range(n_iters + 1):
idx = i * batch_size
m.update((y_pred[idx:idx + batch_size], y[idx:idx + batch_size]))
assert np_median_absolute_relative_error == pytest.approx(m.compute())
def test_wrong_input_shapes():
m = MedianRelativeAbsoluteError()
with pytest.raises(ValueError):
m.update((torch.rand(4, 1, 2),
torch.rand(4, 1)))
with pytest.raises(ValueError):
m.update((torch.rand(4, 1),
torch.rand(4, 1, 2)))
with pytest.raises(ValueError):
m.update((torch.rand(4, 1, 2),
torch.rand(4,)))
with pytest.raises(ValueError):
m.update((torch.rand(4,),
torch.rand(4, 1, 2)))
def test_wrong_input_shapes():
m = MedianRelativeAbsoluteError()
with pytest.raises(ValueError, match=r"Predictions should be of shape"):
m.update((torch.rand(4, 1, 2), torch.rand(4, 1)))
with pytest.raises(ValueError, match=r"Targets should be of shape"):
m.update((torch.rand(4, 1), torch.rand(4, 1, 2)))
with pytest.raises(ValueError, match=r"Predictions should be of shape"):
m.update((
torch.rand(4, 1, 2),
torch.rand(4, ),
))
with pytest.raises(ValueError, match=r"Targets should be of shape"):
m.update((
torch.rand(4, ),
torch.rand(4, 1, 2),
))