def test_restricted_mating_selection(ref_dirs, evaluator):
np.random.seed(200)
selection = RestrictedMating(func_comp=comp_by_cv_dom_then_random)
problem = C3DTLZ4(n_var=12, n_obj=3)
ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c3dtlz4', 'case2', 'preCA.x'))
CA = Population.create(ca_x)
evaluator.eval(problem, CA)
da_x = np.loadtxt(path_to_test_resource('ctaea', 'c3dtlz4', 'case2', 'preDA.x'))
DA = Population.create(da_x)
evaluator.eval(problem, DA)
Hm = Population.merge(CA, DA)
n_pop = len(CA)
_, rank = NonDominatedSorting().do(Hm.get('F'), return_rank=True)
Pc = (rank[:n_pop] == 0).sum() / len(Hm)
Pd = (rank[n_pop:] == 0).sum() / len(Hm)
P = selection.do(Hm, len(CA))
assert P.shape == (91, 2)
if Pc > Pd:
assert (P[:, 0] < n_pop).all()
else:
assert (P[:, 0] >= n_pop).all()
assert (P[:, 1] >= n_pop).any()
assert (P[:, 1] < n_pop).any()
def test_survival():
problem = DTLZ2(n_obj=3)
for k in range(1, 11):
print("TEST RVEA GEN", k)
ref_dirs = np.loadtxt(
path_to_test_resource('rvea', f"ref_dirs_{k}.txt"))
F = np.loadtxt(path_to_test_resource('rvea', f"F_{k}.txt"))
pop = Population.new(F=F)
algorithm = RVEA(ref_dirs)
algorithm.setup(problem, termination=('n_gen', 500))
algorithm.n_gen = k
algorithm.pop = pop
survival = APDSurvival(ref_dirs)
survivors = survival.do(problem,
algorithm.pop,
n_survive=len(pop),
algorithm=algorithm,
return_indices=True)
apd = pop[survivors].get("apd")
correct_apd = np.loadtxt(path_to_test_resource('rvea', f"apd_{k}.txt"))
np.testing.assert_allclose(apd, correct_apd)
def test_association(ref_dirs, evaluator):
problem = C1DTLZ3(n_var=12, n_obj=3)
ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', 'case3', 'preCA.x'))
CA = Population.create(ca_x)
evaluator.eval(problem, CA)
da_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', 'case3', 'preDA.x'))
DA = Population.create(da_x)
evaluator.eval(problem, DA)
off_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', 'case3', 'offspring.x'))
off = Population.create(off_x)
evaluator.eval(problem, off)
true_assoc = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', 'case3', 'feasible_rank0.txt'))
true_niche = true_assoc[:, 1]
true_id = true_assoc[:, 0]
sorted_id = np.argsort(true_id)
survival = CADASurvival(ref_dirs)
mixed = Population.merge(CA, off)
survival.ideal_point = np.min(np.vstack((DA.get("F"), mixed.get("F"))), axis=0)
fronts = NonDominatedSorting().do(mixed.get("F"), n_stop_if_ranked=len(ref_dirs))
I = np.concatenate(fronts)
niche, _ = survival._associate(mixed[I])
sorted_I = np.argsort(I)
assert (niche[sorted_I] == true_niche[sorted_id]).all()
def test_perp_dist():
np.random.seed(1)
F = np.random.random((100, 3))
weights = np.random.random((10, 3))
D = PerpendicularDistance(_type="python").do(F,
weights,
_type="many_to_many")
np.testing.assert_allclose(D,
np.loadtxt(path_to_test_resource("perp_dist")))
D = PerpendicularDistance(_type="cython").do(F,
weights,
_type="many_to_many")
np.testing.assert_allclose(D,
np.loadtxt(path_to_test_resource("perp_dist")))
def test_update_da(ref_dirs, evaluator):
problem = C1DTLZ3(n_var=12, n_obj=3)
for i in range(2):
ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', f'case{i + 1}', 'preCA.x'))
CA = Population.create(ca_x)
evaluator.eval(problem, CA)
da_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', f'case{i + 1}', 'preDA.x'))
DA = Population.create(da_x)
evaluator.eval(problem, DA)
off_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', f'case{i + 1}', 'offspring.x'))
off = Population.create(off_x)
evaluator.eval(problem, off)
survival = CADASurvival(ref_dirs)
mixed = Population.merge(CA, off)
survival.ideal_point = np.min(np.vstack((DA.get("F"), mixed.get("F"))), axis=0)
post_ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', f'case{i + 1}', 'postCA.x'))
CA = Population.create(post_ca_x)
evaluator.eval(problem, CA)
Hd = Population.merge(DA, off)
pDA = survival._updateDA(CA, Hd, 91)
true_S1 = [151, 35, 6, 63, 67, 24, 178, 106, 134, 172, 148, 159, 41,
173, 145, 77, 62, 40, 127, 61, 130, 27, 171, 115, 52, 176,
22, 75, 55, 87, 36, 149, 154, 47, 78, 170, 90, 15, 53, 175,
179, 165, 56, 89, 132, 82, 141, 39, 32, 25, 131, 14, 72, 65,
177, 140, 66, 143, 34, 81, 103, 99, 147, 168, 51, 26, 70, 94,
54, 97, 158, 107, 29, 120, 50, 108, 157, 11, 85, 174, 80, 0,
95, 13, 142, 101, 156, 19, 8, 98, 20]
true_S2 = [78, 173, 59, 21, 101, 52, 36, 94, 17, 20, 37, 96, 90, 129,
150, 136, 162, 70, 146, 75, 138, 154, 65, 179, 98, 32, 97,
11, 26, 107, 12, 128, 95, 170, 24, 171, 40, 180, 14, 44, 49,
43, 130, 23, 60, 79, 148, 62, 87, 56, 157, 73, 104, 45, 177,
74, 15, 152, 164, 28, 80, 113, 41, 33, 158, 57, 77, 34, 114,
118, 18, 54, 53, 145, 93, 115, 121, 174, 142, 39, 13, 105,
10, 69, 120, 55, 6, 153, 91, 137, 46]
if i == 0:
assert np.all(pDA == Hd[true_S1])
else:
assert np.all(pDA == Hd[true_S2])
def test_update(ref_dirs, evaluator):
problem = C3DTLZ4(n_var=12, n_obj=3)
ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c3dtlz4', 'case2', 'preCA.x'))
CA = Population.create(ca_x)
evaluator.eval(problem, CA)
da_x = np.loadtxt(path_to_test_resource('ctaea', 'c3dtlz4', 'case2', 'preDA.x'))
DA = Population.create(da_x)
evaluator.eval(problem, DA)
off_x = np.loadtxt(path_to_test_resource('ctaea', 'c3dtlz4', 'case2', 'offspring.x'))
off = Population.create(off_x)
evaluator.eval(problem, off)
post_ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c3dtlz4', 'case2', 'postCA.x'))
true_pCA = Population.create(post_ca_x)
evaluator.eval(problem, true_pCA)
post_da_x = np.loadtxt(path_to_test_resource('ctaea', 'c3dtlz4', 'case2', 'postDA.x'))
true_pDA = Population.create(post_da_x)
evaluator.eval(problem, true_pDA)
survival = CADASurvival(ref_dirs)
mixed = Population.merge(CA, off)
survival.ideal_point = np.array([0., 0., 0.])
pCA, pDA = survival.do(problem, mixed, DA, len(ref_dirs))
pCA_X = set([tuple(x) for x in pCA.get("X")])
tpCA_X = set([tuple(x) for x in true_pCA.get("X")])
pDA_X = set([tuple(x) for x in pDA.get("X")])
tpDA_X = set([tuple(x) for x in true_pDA.get("X")])
assert pCA_X == tpCA_X
assert pDA_X == tpDA_X
def load(name, suffix=[]):
path = path_to_test_resource("problems", *suffix)
X = anp.loadtxt(os.path.join(path, "%s.x" % name))
try:
F = anp.loadtxt(os.path.join(path, "%s.f" % name))
CV = None
if os.path.exists(os.path.join(path, "%s.cv" % name)):
CV = anp.loadtxt(os.path.join(path, "%s.cv" % name))
except:
return X, None, None
return X, F, CV
def data():
return pickle.load(open(path_to_test_resource('cnsga2_run_zdt4.dat'), 'rb'))
def test_update_ca(ref_dirs, evaluator):
problem = C1DTLZ3(n_var=12, n_obj=3)
ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', 'case3', 'preCA.x'))
CA = Population.create(ca_x)
evaluator.eval(problem, CA)
da_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', 'case3', 'preDA.x'))
DA = Population.create(da_x)
evaluator.eval(problem, DA)
off_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', 'case3', 'offspring.x'))
off = Population.create(off_x)
evaluator.eval(problem, off)
post_ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz3', 'case3', 'postCA.x'))
true_pCA = Population.create(post_ca_x)
evaluator.eval(problem, true_pCA)
survival = CADASurvival(ref_dirs)
mixed = Population.merge(CA, off)
survival.ideal_point = np.min(np.vstack((DA.get("F"), mixed.get("F"))), axis=0)
pCA = survival._updateCA(mixed, len(ref_dirs))
pX = set([tuple(x) for x in pCA.get("X")])
tpX = set([tuple(x) for x in true_pCA.get("X")])
assert pX == tpX
problem = C1DTLZ1(n_var=9, n_obj=3)
ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz1', 'preCA.x'))
CA = Population.create(ca_x)
evaluator.eval(problem, CA)
da_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz1', 'preDA.x'))
DA = Population.create(da_x)
evaluator.eval(problem, DA)
off_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz1', 'offspring.x'))
off = Population.create(off_x)
evaluator.eval(problem, off)
post_ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c1dtlz1', 'postCA.x'))
true_pCA = Population.create(post_ca_x)
evaluator.eval(problem, true_pCA)
survival = CADASurvival(ref_dirs)
mixed = Population.merge(CA, off)
survival.ideal_point = np.min(np.vstack((DA.get("F"), mixed.get("F"))), axis=0)
pCA = survival._updateCA(mixed, len(ref_dirs))
pX = set([tuple(x) for x in pCA.get("X")])
tpX = set([tuple(x) for x in true_pCA.get("X")])
assert pX == tpX
problem = C3DTLZ4(n_var=12, n_obj=3)
ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c3dtlz4', 'case1', 'preCA.x'))
CA = Population.create(ca_x)
evaluator.eval(problem, CA)
da_x = np.loadtxt(path_to_test_resource('ctaea', 'c3dtlz4', 'case1', 'preDA.x'))
DA = Population.create(da_x)
evaluator.eval(problem, DA)
off_x = np.loadtxt(path_to_test_resource('ctaea', 'c3dtlz4', 'case1', 'offspring.x'))
off = Population.create(off_x)
evaluator.eval(problem, off)
post_ca_x = np.loadtxt(path_to_test_resource('ctaea', 'c3dtlz4', 'case1', 'postCA.x'))
true_pCA = Population.create(post_ca_x)
evaluator.eval(problem, true_pCA)
survival = CADASurvival(ref_dirs)
mixed = Population.merge(CA, off)
survival.ideal_point = np.min(np.vstack((DA.get("F"), mixed.get("F"))), axis=0)
pCA = survival._updateCA(mixed, len(ref_dirs))
pX = set([tuple(x) for x in pCA.get("X")])
tpX = set([tuple(x) for x in true_pCA.get("X")])
assert pX == tpX
def ref_dirs():
return np.loadtxt(path_to_test_resource('ctaea', 'weights.txt'))
def load_file(f):
return np.loadtxt(
path_to_test_resource("kktpm", "%s_%s.txt" % (str_problem, f)))