def test_unequal_ndcg():
# Set up data
prediction = np.array([0.3, 0.3, 0.2])
ground_truth = np.array([3.0, 3.0, 2.0, 1.0, 1.0, 2.3])
# This should raise a ValueError because the lists aren't of equal length
ndcg(prediction, ground_truth)
def test_ndcg_negative_predictions():
# Set up data
prediction = np.array([-0.1, -0.3, 1.9, -0.9, -0.2])
ground_truth = np.array([0.0, 1.0, 1.0, 0.0, 0.0])
# Compute and assert nDCG value
assert_equal(ndcg(prediction, ground_truth).data, 0.8772153153380493)
def test_empty_ndcg():
# Set up data
prediction = np.array([])
ground_truth = np.array([])
# Assert nDCG of empty lists
assert_equal(ndcg(prediction, ground_truth).data, 0.0)
def test_ndcg_no_preferences():
# Set up data
prediction = np.array([0.3, 0.3, 0.2, 2.14, 0.23])
ground_truth = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
# Compute and assert nDCG value
assert_equal(ndcg(prediction, ground_truth).data, 1.0)
def test_ndcg():
# Set up data
prediction = np.array([0.1, 0.9, 0.2, 3.0, 0.15])
ground_truth = np.array([3.0, 3.0, 2.0, 1.0, 1.0])
# Compute and assert nDCG value
assert_equal(ndcg(prediction, ground_truth).data, 0.73213389587665278)
def test_ndcg_at_k():
# Set up data
prediction = np.array([0.3, 0.3, 0.2, 2.14, 0.23])
ground_truth = np.array([3.0, 3.0, 2.0, 1.0, 1.0])
# Compute and assert nDCG@3 value
assert_equal(ndcg(prediction, ground_truth, k=3).data, 0.69031878315427031)
def test_ndcg_minimal():
# Set up data
prediction = np.arange(10).astype(dtype=np.float32)
ground_truth = np.flip(prediction, axis=0)
# Compute and assert nDCG value
assert_equal(ndcg(prediction, ground_truth).data, 0.39253964576233569)
def test_ndcg_perfect():
# Set up data
prediction = np.array([4.0, 3.0, 2.0, 1.0, 0.0])
ground_truth = np.array([3.0, 3.0, 2.0, 1.0, 1.0])
# Compute and assert nDCG value
assert_equal(ndcg(prediction, ground_truth).data, 1.0)
def test_ndcg_3():
# Set up data
prediction = np.array([0.1, 0.9, 0.2, 0.15, 3.0])
ground_truth = np.array([3.0, 3.0, 2.0, 1.0, 2.0])
# Compute and assert nDCG value
assert_equal(ndcg(prediction, ground_truth).data, 0.8259562683091511)