def test_median_absolute_percentage_error():
# See https://github.com/torch/torch7/pull/182
# For even number of elements, PyTorch returns middle element
# NumPy returns average of middle elements
# Size of dataset will be odd for these tests
size = 51
np_y_pred = np.random.rand(size,)
np_y = np.random.rand(size,)
np_median_absolute_percentage_error = 100.0 * np.median(np.abs(np_y - np_y_pred) / np.abs(np_y))
m = MedianAbsolutePercentageError()
y_pred = torch.from_numpy(np_y_pred)
y = torch.from_numpy(np_y)
m.reset()
m.update((y_pred, y))
assert np_median_absolute_percentage_error == pytest.approx(m.compute())
def test_median_absolute_percentage_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_percentage_error = 100.0 * np.median(np.abs(np_y - np_y_pred) / np.abs(np_y))
m = MedianAbsolutePercentageError()
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):
idx = i * batch_size
m.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size]))
assert np_median_absolute_percentage_error == pytest.approx(m.compute())
