def test_ndcg_0_over_0_error():
"""
ndcg with division by 0
"""
y_true = np.array([[0, 0, 0, 0]])
y_prob = np.array([[0.9, 0.8, 0.7, 0.6]])
expected = 1.0
actual = metriks.ndcg(y_true, y_prob, 1)
np.testing.assert_allclose([actual], [expected])
def test_combination():
"""
Mixture of all relevant, no relevant, and some relevant samples
"""
y_true = np.array([[0, 0, 0, 0], [0, 1, 1, 1], [1, 1, 1, 1]])
y_prob = np.array([[0.2, 0.9, 0.8, 0.4], [0.5, 0.8, 0.7, 0.4],
[0.9, 0.6, 0.7, 0.2]])
expected = 0.989156
actual = metriks.ndcg(y_true, y_prob)
np.testing.assert_allclose([actual], [expected])
def test_all_zeros():
"""
There are no relevant items in any sample
"""
y_true = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
y_prob = np.array([[0.2, 0.9, 0.8, 0.4], [0.5, 0.8, 0.7, 0.4],
[0.9, 0.6, 0.7, 0.2]])
expected = 1.0
actual = metriks.ndcg(y_true, y_prob)
np.testing.assert_allclose([actual], [expected])
def test_ndcg_perfect():
"""
All predicted rankings match the expected ranking
"""
y_true = np.array([[0, 1, 1, 0], [0, 1, 1, 1], [1, 1, 1, 0]])
y_prob = np.array([[0.2, 0.9, 0.8, 0.4], [0.4, 0.8, 0.7, 0.5],
[0.9, 0.6, 0.7, 0.2]])
expected = 1.0
actual = metriks.ndcg(y_true, y_prob)
np.testing.assert_equal([actual], [expected])
def test_ndcg_all_ones():
"""
Every item in each sample is relevant
"""
y_true = np.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
y_prob = np.array([[0.2, 0.9, 0.8, 0.4], [0.5, 0.8, 0.7, 0.4],
[0.9, 0.6, 0.7, 0.2]])
expected = 1.0
actual = metriks.ndcg(y_true, y_prob)
np.testing.assert_allclose([actual], [expected])
def test_ndcg_errors():
"""
Some samples predicted the order incorrectly
"""
y_true = np.array([[1, 1, 1, 0], [1, 0, 1, 1], [0, 1, 1, 0]])
y_prob = np.array([[0.2, 0.9, 0.8, 0.4], [0.5, 0.8, 0.7, 0.4],
[0.9, 0.6, 0.7, 0.2]])
expected = 0.7979077
actual = metriks.ndcg(y_true, y_prob)
np.testing.assert_allclose([actual], [expected])
def test_ndcg_after_1():
"""
ndcg with top 1 class removed
"""
y_true = np.array([[0, 1, 1, 0, 0, 1], [1, 1, 1, 0, 1, 0],
[1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1],
[1, 1, 0, 0, 1, 1]])
y_prob = np.array([[0.5, 0.9, 0.8, 0.4, 0.2, 0.2],
[0.9, 0.8, 0.2, 0.5, 0.2, 0.4],
[0.2, 0.9, 0.8, 0.4, 0.6, 0.5],
[0.2, 0.7, 0.8, 0.4, 0.6, 0.9],
[0.2, 0.2, 0.8, 0.5, 0.3, 0.9]])
expected = 0.8554782
actual = metriks.ndcg(y_true, y_prob, 1)
np.testing.assert_allclose([actual], [expected])
def test_ndcg_after_0():
"""
ndcg with no classes removed
"""
y_true = np.array([[0, 1, 1, 0, 0, 1], [0, 1, 1, 0, 1, 1],
[1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 1, 0],
[1, 1, 0, 0, 1, 0]])
y_prob = np.array([[0.5, 0.9, 0.8, 0.4, 0.2, 0.2],
[0.9, 0.8, 0.2, 0.5, 0.2, 0.4],
[0.2, 0.9, 0.8, 0.4, 0.6, 0.5],
[0.2, 0.7, 0.8, 0.4, 0.6, 0.9],
[0.2, 0.9, 0.8, 0.5, 0.3, 0.2]])
expected = 0.8395053
actual = metriks.ndcg(y_true, y_prob)
np.testing.assert_allclose([actual], [expected])
