def nominate_delegates(self):
return [
x.v for x in list(
paretofront_layers(
self.individuals, lambda indv: self.driver.
calculate_objectives(indv)))[0]
]
def test_paretofront_layers_moremore(self):
""" Previous tests were not enough, some errors did show in e.g. kursawe. """
def mk_indiv():
return [random.uniform(a, b) for a, b in kursawe.dims]
def eval_fits(indiv):
return indiv, [f(indiv) for f in kursawe.fitnesses]
random.seed()
pop = [eval_fits(mk_indiv()) for _ in range(400)]
layers = paretofront_layers(pop, operator.itemgetter(1))
for layA, layB in itertools.combinations(layers, 2):
for indA_1, indA_2 in itertools.product(layA, repeat=2):
try:
self.assertEqual(0, domination_cmp(indA_1[1], indA_2[1]))
except AssertionError as e:
print("ERR!\n\nA = {0}\nB = {1}\nf(A) = {2}\nf(B) = {3}".format(indA_1[0],
indA_2[0],
indA_1[1],
indA_2[1]))
raise e
for indA in layA:
for indB in layB:
try:
self.assertFalse(dominates(indB[1], indA[1]))
except AssertionError as e:
print("ERR!\n\nA = {0}\nB = {1}\nf(A) = {2}\nf(B) = {3}".format(indA[0],
indB[0],
indA[1],
indB[1]))
raise e
def emigrate(self):
logger = logging.getLogger(__name__)
def fitfun_res(ind):
return [f(ind) for f in self.outer.fitnesses]
current_population = self.driver.population
refugees = []
for _ in range(self.outer.migrants_number):
pareto_layers = [
l
for l in ea_utils.paretofront_layers(current_population,
fitfun_res=fitfun_res)
]
weights = [1 / (i + 1) for i in range(len(pareto_layers))]
chosen_layer = weighted_choice(zip(pareto_layers, weights))
refugee = random.choice(chosen_layer)
refugees.append(refugee)
if refugee not in current_population:
logger.error("DUPA WSZECHCZASÓW")
current_population.remove(refugee)
logger.debug("after emigrate: " + str(len(self.driver.population)))
self.all_refugees.extend(refugees)
return self.driver.message_adapter.emigrate(refugees)
def test_paretofront_layers_even_more(self):
fitnesses = [lambda xs: (xs[0] - 2) ** 2, lambda xs: (xs[1] + 3) ** 2]
def fits(xs):
return [f(xs) for f in fitnesses]
random.seed()
mkd = lambda: random.choice([0, 1, 2, 3])
#noinspection PyUnusedLocal
pop = [[mkd() for j in range(3)]
for i in range(1000)]
res = paretofront_layers(pop, fits)
ind_a = [2, 3, 2]
ind_b = [2, 2, 1]
self.assertNotEqual(0, domination_cmp(fits(ind_a), fits(ind_b)))
self.assertNotEqual(0, domination_cmp(fits(ind_b), fits(ind_a)))
for layer in res:
for ind_a, ind_b in itertools.combinations(layer, 2):
fa, fb = fits(ind_a), fits(ind_b)
self.assertEqual(0, domination_cmp(fa, fb))
self.assertEqual(0, domination_cmp(fa, fa))
self.assertEqual(0, domination_cmp(fb, fb))
self.assertFalse(dominates(fa, fb))
self.assertFalse(dominates(fa, fa))
self.assertFalse(dominates(fb, fb))
def test_paretofront_layers_moremore(self):
""" Previous tests were not enough, some errors did show in e.g. kursawe. """
def mk_indiv():
return [random.uniform(a, b) for a, b in kursawe.dims]
def eval_fits(indiv):
return indiv, [f(indiv) for f in kursawe.fitnesses]
random.seed()
pop = [eval_fits(mk_indiv()) for _ in range(400)]
layers = paretofront_layers(pop, operator.itemgetter(1))
for layA, layB in itertools.combinations(layers, 2):
for indA_1, indA_2 in itertools.product(layA, repeat=2):
try:
self.assertEqual(0, domination_cmp(indA_1[1], indA_2[1]))
except AssertionError as e:
print("ERR!\n\nA = {0}\nB = {1}\nf(A) = {2}\nf(B) = {3}".
format(indA_1[0], indA_2[0], indA_1[1], indA_2[1]))
raise e
for indA in layA:
for indB in layB:
try:
self.assertFalse(dominates(indB[1], indA[1]))
except AssertionError as e:
print(
"ERR!\n\nA = {0}\nB = {1}\nf(A) = {2}\nf(B) = {3}".
format(indA[0], indB[0], indA[1], indB[1]))
raise e
def rank(individuals: "Iterator Individual", calc_objective):
"""
:param individuals: Grupa indywiduów.
:param calc_objective: Określa jak otrzymać wektor wyników.
:return: Itertator: posortowane dane wejściowe od najlepszych do najgorszych.
"""
return iter(indiv
for eqv_class in paretofront_layers(individuals,
fitfun_res=calc_objective)
for indiv in eqv_class)
def test_paretofront_layers(self):
def identity(x):
return [x]
indvs = [[0, 0], [1, 1], [0, 1], [1, 0], [1, 1]]
res = list(paretofront_layers(indvs, identity))
self.assertEqual(sum(len(layer) for layer in res), len(indvs))
self.assertListEqual(res[0], [[0, 0]])
self.assertTrue([0, 1] in res[1])
self.assertTrue([1, 0] in res[2])
self.assertListEqual(res[3], [[1, 1], [1, 1]])
def rank(individuals: 'Iterator Individual', calc_objective):
"""
:param individuals: Grupa indywiduów.
:param calc_objective: Określa jak otrzymać wektor wyników.
:return: Itertator: posortowane dane wejściowe od najlepszych do najgorszych.
"""
return iter(indiv
for eqv_class
in paretofront_layers(individuals,
fitfun_res=calc_objective)
for indiv
in eqv_class
)
def test_paretofront_layers(self):
def identity(x):
return [x]
indvs = [[0, 0], [1, 1], [0, 1], [1, 0], [1, 1]]
res = list(paretofront_layers(indvs, identity))
self.assertEqual(sum(len(layer)
for layer in res),
len(indvs))
self.assertListEqual(res[0], [[0, 0]])
self.assertTrue([0, 1] in res[1])
self.assertTrue([1, 0] in res[2])
self.assertListEqual(res[3], [[1, 1], [1, 1]])
def test_paretofront_layers_even_more(self):
fitnesses = [lambda xs: (xs[0] - 2)**2, lambda xs: (xs[1] + 3)**2]
def fits(xs):
return [f(xs) for f in fitnesses]
random.seed()
mkd = lambda: random.choice([0, 1, 2, 3])
#noinspection PyUnusedLocal
pop = [[mkd() for j in range(3)] for i in range(1000)]
res = paretofront_layers(pop, fits)
ind_a = [2, 3, 2]
ind_b = [2, 2, 1]
self.assertNotEqual(0, domination_cmp(fits(ind_a), fits(ind_b)))
self.assertNotEqual(0, domination_cmp(fits(ind_b), fits(ind_a)))
for layer in res:
for ind_a, ind_b in itertools.combinations(layer, 2):
fa, fb = fits(ind_a), fits(ind_b)
self.assertEqual(0, domination_cmp(fa, fb))
self.assertEqual(0, domination_cmp(fa, fa))
self.assertEqual(0, domination_cmp(fb, fb))
self.assertFalse(dominates(fa, fb))
self.assertFalse(dominates(fa, fa))
self.assertFalse(dominates(fb, fb))
def nominate_delegates(self):
return [x.v for x in
list(paretofront_layers(self.individuals, lambda indv: self.driver.calculate_objectives(indv)))[
0]]
