def _test_optimizer(opt, problem, num_retries=32, num=1):
log = logger()
log.info(problem.name + ' ' + opt.name)
for _ in range(num):
minimize(problem.fun,
problem.bounds,
math.inf,
num_retries,
log,
optimizer=opt)
def test_retry_cpp(problem, num):
ret = retry.minimize(problem.fun,
bounds=problem.bounds,
num_retries=num,
max_evaluations=50000,
logger=logger(),
useCpp=True)
def test_retry_cma_python(problem, num):
best = math.inf
t0 = time.perf_counter();
for i in range(num):
ret = retry.minimize(problem.fun, bounds = problem.bounds,
num_retries = 2000, optimizer = Cma_python(100000))
best = min(ret.fun, best)
print("{0}: time = {1:.1f} best = {2:.1f} f(xmin) = {3:.1f}"
.format(i+1, dtime(t0), best, ret.fun))
def test_retry(problem, num, log=None):
best = math.inf
t0 = time.perf_counter();
for i in range(num):
ret = retry.minimize(problem.fun, bounds = problem.bounds,
num_retries = 2000, max_evaluations = 100000, logger=log)
best = min(ret.fun, best)
print("{0}: time = {1:.1f} best = {2:.1f} f(xmin) = {3:.1f}"
.format(i+1, dtime(t0), best, ret.fun))
def messengerFullLoop():
while True:
problem = MessFull()
logger().info(problem.name + ' cmaes c++')
ret = minimize(problem.fun,
bounds=problem.bounds,
num_retries=40000,
max_evaluations=50000,
logger=logger(),
useCpp=True)
def test_optimizer(fun, n):
best = math.inf
t0 = time.perf_counter()
for i in range(n):
max_x = i + 12
bounds = Bounds(lowerbound, [max_x + 0.99] * 4)
ret = retry.minimize(fun,
bounds,
max_evaluations=10000,
num_retries=100)
best = print_result(ret, best, t0, max_x)
def _test_problem(problem,
max_evals=50000,
num_retries=2000,
num=20,
log=logger(),
useCpp=False):
log.info(problem.name + ' cmaes ' + ('c++' if useCpp else 'python'))
for i in range(num):
ret = minimize(problem.fun,
bounds=problem.bounds,
num_retries=num_retries,
max_evaluations=max_evals,
useCpp=useCpp,
logger=log)
def test_de_cma_parallel_retry(problem):
# parallel optimization retry using a DE -> CMA-ES sequence
t0 = time.perf_counter()
evals = 0
for i in range(1000):
ret = retry.minimize(problem.fun,
problem.bounds,
logger=logger(),
optimizer=de_cma(50000),
value_limit=10)
evals += ret.nfev
print("{0}: time = {1:.1f} fun = {2:.3f} nfev = {3}".format(
i + 1, dtime(t0), ret.fun, evals))
return ret
def test_eggholder_retry():
dim = 2
testfun = Eggholder()
limit = -956
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
ret = retry.minimize(wrapper.eval, testfun.bounds, num_retries=100)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (ret.nfev == wrapper.get_count()
) # wrong number of function calls returned
assert (almost_equal(ret.x, wrapper.get_best_x())) # wrong best X returned
assert (ret.fun == wrapper.get_best_y()) # wrong best y returned
def optimize():
solo_mgar = solo_mgar_udp([7000, 8000])
prob = pg.problem(solo_mgar)
fprob = single_objective(prob)
# logger().info('solar orbiter' + ' de -> cmaes c++ smart retry')
# ret = advretry.minimize(fprob.fun, bounds=fprob.bounds, num_retries = 60000,
# logger = logger(), optimizer=de_cma(1500))
logger().info('solar orbiter' + ' BiteOpt parallel retry')
ret = retry.minimize(fprob.fun,
bounds=fprob.bounds,
num_retries=32000,
logger=logger(),
optimizer=Bite_cpp(120000, M=6))
return ret
def test_eggholder_retry():
#windows cannot pickle function objects
if sys.platform.startswith('windows'):
return
dim = 2
testfun = Eggholder()
limit = -956
for i in range(5):
wrapper = Wrapper(testfun.func, dim)
ret = retry.minimize(wrapper.eval,
testfun.bounds,
num_retries=100,
useCpp=False,
logger=None)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (ret.nfev == wrapper.get_count()
) # wrong number of function calls returned
assert (almost_equal(ret.x, wrapper.get_best_x())) # wrong best X returned
assert (ret.fun == wrapper.get_best_y()) # wrong best y returned
def parallel_improve(opt):
return retry.minimize(fitness(), bounds, optimizer=opt)
def parallel_retry(opt=Bite_cpp(100000, M=8)):
return retry.minimize(fitness(), bounds, optimizer=opt)
def test_retry_python(problem, num):
ret = retry.minimize(problem.fun,
bounds=problem.bounds,
num_retries=num,
max_evaluations=50000,
logger=logger())
