def test_ndcg(value, output, gain, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
score = AA(output, dtype=dt).reshape(-1,1,1)
gain = AA(gain, dtype=dt).reshape(-1,1,1)
group = np.ones_like(score).reshape(-1,1,1)
expected_value = AA(value, dtype=dt)
from cntk.metrics import ndcg_at_1
g = input((1,))
s = input((1,))
n = input((1,))
f = ndcg_at_1(s, n, g)
actual_value = f.eval({s:score, n:gain, g:group})
assert np.allclose(actual_value, expected_value)
def test_ndcg(value, output, gain, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
score = AA(output, dtype=dt).reshape(-1,1,1)
gain = AA(gain, dtype=dt).reshape(-1,1,1)
group = np.ones_like(score).reshape(-1,1,1)
expected_value = AA(value, dtype=dt)
from cntk.metrics import ndcg_at_1
g = I((1,))
s = I((1,))
n = I((1,))
f = ndcg_at_1(s, n, g)
actual_value = f.eval({s:score, n:gain, g:group})
assert np.allclose(actual_value, expected_value)