def test_rank_and_crowding_distance(self):
for i, D in enumerate(self.data):
survivor_and_last_front = np.where(D['rank'] != -1.0)[0]
crowding = D['crowding'][survivor_and_last_front]
rank = D['rank'][survivor_and_last_front].astype(np.int)
F = D['F'][survivor_and_last_front, :]
fronts, _rank = NonDominatedSorting().do(F, return_rank=True)
_rank += 1
_crowding = np.full(len(F), np.nan)
for front in fronts:
_crowding[front] = calc_crowding_distance(F[front])
_crowding[np.isinf(_crowding)] = 1e14
is_equal = np.all(rank == _rank)
if not is_equal:
index = np.where(rank == _rank)
print(index)
print(D['rank'][index])
print(D['F'][index])
self.assertTrue(is_equal)
is_equal = np.all(np.abs(_crowding - crowding) < 0.001)
if not is_equal:
index = np.where(np.abs(_crowding - crowding) > 0.001)[0]
index = index[np.argsort(rank[index])]
# only an error if it is not a duplicate F value
for i_not_equal in index:
if len(
np.where(np.all(F[i_not_equal, :] == F,
axis=1))[0]) == 1:
print("-" * 30)
print("Generation: ", i)
print("Is rank equal: ", np.all(rank == _rank))
print(index)
print(rank[index])
print(F[index])
print(
np.concatenate(
[_crowding[:, None], crowding[:, None]],
axis=1)[index, :])
print()
self.assertTrue(is_equal)
def test_crowding_distance_norm_equals_zero(self):
F = np.array([[1.0, 1.5, 0.5, 1.0], [1.0, 0.5, 1.5, 1.0],
[1.0, 0.0, 2.0, 1.5]])
cd = calc_crowding_distance(F)
self.assertTrue(
np.all(np.isclose(cd, np.array([np.inf, 0.75, np.inf]))))
def test_crowding_distance_two_duplicates(self):
F = np.array([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [0.5, 1.5],
[0.0, 2.0]])
cd = calc_crowding_distance(F)
self.assertTrue(
np.all(np.isclose(cd, np.array([np.inf, 0.0, 0.0, 1.0, np.inf]))))
def test_normalization(self):
F = np.array([[0, 0], [0.5, 50], [1.0, 100]])
cd = calc_crowding_distance(F)
self.assertEqual(cd[1], 1)