def test_pairwise_distance_with_nan():
arr = np.array([[1, 2, 3, 4, 1],
[1, 3, 1, 4, 1],
[1, 2, 3, 4, 1],
[2, 3, np.nan, 4, 1]])
assert arr.shape == (4, 5)
sim_counts = similarity_count_matrix(arr, np.isnan(arr))
expected_sim_counts = np.array([[0, 3, 5, 2],
[3, 0, 3, 3],
[5, 3, 0, 2],
[2, 3, 2, 0]])
assert np.array_equal(sim_counts, expected_sim_counts)
not_nan = pairwise_not_nan_counts(np.isnan(arr))
expected_nan_counts = np.array([[0, 5, 5, 4],
[5, 0, 5, 4],
[5, 5, 0, 4],
[4, 4, 4, 0]])
assert np.all(not_nan == expected_nan_counts)
dm = dist_matrix_with_nan(arr)
expected_dm = np.array([[0.0, 0.4, 0.0, 0.5],
[0.4, 0.0, 0.4, 0.25],
[0.0, 0.4, 0.0, 0.5],
[0.5, 0.25, 0.5, 0.0]])
assert np.allclose(dm, expected_dm)
def test_pairwise_distance_with_all_nan():
"""
If an item has all missing values then it should have a maximum distance
of 1.0 to all other items including other items with missing values.
"""
arr = np.array([[1, 2, 3, 4, 1],
[1, 3, 1, 4, 1],
[1, 2, np.nan, 4, 1],
[np.nan, np.nan, np.nan, np.nan, np.nan]])
assert arr.shape == (4, 5)
sim_counts = similarity_count_matrix(arr, np.isnan(arr))
expected_sim_counts = np.array([[0, 3, 4, 0],
[3, 0, 3, 0],
[4, 3, 0, 0],
[0, 0, 0, 0]])
assert np.array_equal(sim_counts, expected_sim_counts)
not_nan = pairwise_not_nan_counts(np.isnan(arr))
expected_nan_counts = np.array([[0, 5, 4, 0],
[5, 0, 4, 0],
[4, 4, 0, 0],
[0, 0, 0, 0]])
assert np.all(not_nan == expected_nan_counts)
dm = dist_matrix_with_nan(arr)
expected_dm = np.array([[0.0, 0.4, 0.0, 1.0],
[0.4, 0.0, 0.25, 1.0],
[0.0, 0.25, 0.0, 1.0],
[1.0, 1.0, 1.0, 0.0]])
assert np.allclose(dm, expected_dm)