def test_restricted_mating_selection(self):
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_resources('ctaea', 'c3dtlz4', 'case2', 'preCA.x'))
CA = Population.create(ca_x)
self.evaluator.eval(problem, CA)
da_x = np.loadtxt(path_to_test_resources('ctaea', 'c3dtlz4', 'case2', 'preDA.x'))
DA = Population.create(da_x)
self.evaluator.eval(problem, DA)
Hm = CA.merge(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_update(self):
problem = C3DTLZ4(n_var=12, n_obj=3)
ca_x = np.loadtxt(
path_to_test_resources('ctaea', 'c3dtlz4', 'case2', 'preCA.x'))
CA = Population.create(ca_x)
self.evaluator.eval(problem, CA)
da_x = np.loadtxt(
path_to_test_resources('ctaea', 'c3dtlz4', 'case2', 'preDA.x'))
DA = Population.create(da_x)
self.evaluator.eval(problem, DA)
off_x = np.loadtxt(
path_to_test_resources('ctaea', 'c3dtlz4', 'case2', 'offspring.x'))
off = Population.create(off_x)
self.evaluator.eval(problem, off)
post_ca_x = np.loadtxt(
path_to_test_resources('ctaea', 'c3dtlz4', 'case2', 'postCA.x'))
true_pCA = Population.create(post_ca_x)
self.evaluator.eval(problem, true_pCA)
post_da_x = np.loadtxt(
path_to_test_resources('ctaea', 'c3dtlz4', 'case2', 'postDA.x'))
true_pDA = Population.create(post_da_x)
self.evaluator.eval(problem, true_pDA)
survival = CADASurvival(self.ref_dirs)
mixed = Population.merge(CA, off)
survival.ideal_point = np.array([0., 0., 0.])
pCA, pDA = survival.do(problem, mixed, DA, len(self.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 test_update_ca(self):
problem = C1DTLZ3(n_var=12, n_obj=3)
ca_x = np.loadtxt(path_to_test_resources('ctaea', 'c1dtlz3', 'case3', 'preCA.x'))
CA = Population.create(ca_x)
self.evaluator.eval(problem, CA)
da_x = np.loadtxt(path_to_test_resources('ctaea', 'c1dtlz3', 'case3', 'preDA.x'))
DA = Population.create(da_x)
self.evaluator.eval(problem, DA)
off_x = np.loadtxt(path_to_test_resources('ctaea', 'c1dtlz3', 'case3', 'offspring.x'))
off = Population.create(off_x)
self.evaluator.eval(problem, off)
post_ca_x = np.loadtxt(path_to_test_resources('ctaea', 'c1dtlz3', 'case3', 'postCA.x'))
true_pCA = Population.create(post_ca_x)
self.evaluator.eval(problem, true_pCA)
survival = CADASurvival(self.ref_dirs)
mixed = CA.merge(off)
survival.ideal_point = np.min(np.vstack((DA.get("F"), mixed.get("F"))), axis=0)
pCA = survival._updateCA(mixed, len(self.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_resources('ctaea', 'c1dtlz1', 'preCA.x'))
CA = Population.create(ca_x)
self.evaluator.eval(problem, CA)
da_x = np.loadtxt(path_to_test_resources('ctaea', 'c1dtlz1', 'preDA.x'))
DA = Population.create(da_x)
self.evaluator.eval(problem, DA)
off_x = np.loadtxt(path_to_test_resources('ctaea', 'c1dtlz1', 'offspring.x'))
off = Population.create(off_x)
self.evaluator.eval(problem, off)
post_ca_x = np.loadtxt(path_to_test_resources('ctaea', 'c1dtlz1', 'postCA.x'))
true_pCA = Population.create(post_ca_x)
self.evaluator.eval(problem, true_pCA)
survival = CADASurvival(self.ref_dirs)
mixed = CA.merge(off)
survival.ideal_point = np.min(np.vstack((DA.get("F"), mixed.get("F"))), axis=0)
pCA = survival._updateCA(mixed, len(self.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_resources('ctaea', 'c3dtlz4', 'case1', 'preCA.x'))
CA = Population.create(ca_x)
self.evaluator.eval(problem, CA)
da_x = np.loadtxt(path_to_test_resources('ctaea', 'c3dtlz4', 'case1', 'preDA.x'))
DA = Population.create(da_x)
self.evaluator.eval(problem, DA)
off_x = np.loadtxt(path_to_test_resources('ctaea', 'c3dtlz4', 'case1', 'offspring.x'))
off = Population.create(off_x)
self.evaluator.eval(problem, off)
post_ca_x = np.loadtxt(path_to_test_resources('ctaea', 'c3dtlz4', 'case1', 'postCA.x'))
true_pCA = Population.create(post_ca_x)
self.evaluator.eval(problem, true_pCA)
survival = CADASurvival(self.ref_dirs)
mixed = CA.merge(off)
survival.ideal_point = np.min(np.vstack((DA.get("F"), mixed.get("F"))), axis=0)
pCA = survival._updateCA(mixed, len(self.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