def test_rosen_python():
popsize = 31
dim = 5
testfun = Rosen(dim)
sdevs = [1.0] * dim
max_eval = 100000
limit = 0.00001
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
ret = cmaes.minimize(wrapper.eval,
testfun.bounds,
input_sigma=sdevs,
max_evaluations=max_eval,
popsize=popsize)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize >= ret.nfev) # too much function calls
assert (max_eval / popsize + 2 > ret.nit) # too much iterations
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 test_rosen_ask_tell():
popsize = 31
dim = 5
testfun = Rosen(dim)
sdevs = [1.0] * dim
max_eval = 100000
limit = 0.00001
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
es = cmaes.Cmaes(testfun.bounds, popsize=popsize, input_sigma=sdevs)
iters = max_eval // popsize
for j in range(iters):
xs = es.ask()
ys = [wrapper.eval(x) for x in xs]
stop = es.tell(ys)
if stop != 0:
break
ret = OptimizeResult(x=es.best_x,
fun=es.best_value,
nfev=wrapper.get_count(),
nit=es.iterations,
status=es.stop)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize >= ret.nfev) # too much function calls
assert (max_eval / popsize + 2 > ret.nit) # too much iterations
def test_eggholder_python():
popsize = 1000
dim = 2
testfun = Eggholder()
# use a wrapper to monitor function evaluations
sdevs = [1.0] * dim
max_eval = 100000
limit = -800
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
ret = cmaes.minimize(wrapper.eval,
testfun.bounds,
input_sigma=sdevs,
max_evaluations=max_eval,
popsize=popsize)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize >= ret.nfev) # too much function calls
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 test_rastrigin_python():
popsize = 100
dim = 3
testfun = Rastrigin(dim)
sdevs = [1.0] * dim
max_eval = 100000
limit = 0.0001
# stochastic optimization may fail the first time
for _ in range(5):
# use a wrapper to monitor function evaluations
wrapper = Wrapper(testfun.fun, dim)
ret = cmaes.minimize(wrapper.eval,
testfun.bounds,
input_sigma=sdevs,
max_evaluations=max_eval,
popsize=popsize)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize >= ret.nfev) # too much function calls
assert (max_eval / popsize + 2 > ret.nit) # too much iterations
assert (ret.status == 4) # wrong cma termination code
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 test_rosen_decpp_parallel():
popsize = 8
dim = 2
testfun = Rosen(dim)
max_eval = 10000
limit = 0.01
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
ret = decpp.minimize(wrapper.eval,
dim,
testfun.bounds,
max_evaluations=max_eval,
popsize=popsize,
workers=popsize)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize >= ret.nfev) # too much function calls
assert (max_eval // popsize + 2 > ret.nit) # too much iterations
#assert(ret.nfev == wrapper.get_count()) # wrong number of function calls returned
assert (almost_equal(ret.x, wrapper.get_best_x(),
eps=1E-2)) # wrong best X returned
assert (almost_equal(ret.fun, wrapper.get_best_y(),
eps=1E-2)) # wrong best y returned
def test_rosen_cpp_parallel():
popsize = 8
dim = 2
testfun = Rosen(dim)
sdevs = [1.0] * dim
max_eval = 10000
limit = 0.00001
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
ret = cmaescpp.minimize(wrapper.eval,
testfun.bounds,
input_sigma=sdevs,
max_evaluations=max_eval,
popsize=popsize,
workers=popsize)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize >= ret.nfev) # too much function calls
assert (max_eval // popsize + 2 > ret.nit) # too much iterations
assert (almost_equal(ret.x, wrapper.get_best_x())) # wrong best X returned
assert (almost_equal(ret.fun, wrapper.get_best_y(),
eps=1E-1)) # wrong best y returned
def test_rosen_ask_tell_de():
popsize = 8
dim = 2
testfun = Rosen(dim)
max_eval = 10000
limit = 0.00001
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
es = de.DE(dim, testfun.bounds, popsize=popsize)
iters = max_eval // popsize
for j in range(iters):
xs = es.ask()
ys = [wrapper.eval(x) for x in xs]
stop = es.tell(ys, xs)
if stop != 0:
break
ret = OptimizeResult(x=es.best_x,
fun=es.best_value,
nfev=wrapper.get_count(),
nit=es.iterations,
status=es.stop)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + 2 * popsize >= ret.nfev) # too much function calls
assert (max_eval / popsize + 2 > ret.nit) # too much iterations
# assert(almost_equal(ret.x, wrapper.get_best_x())) # wrong best X returned
assert (almost_equal(ret.fun, wrapper.get_best_y(),
eps=1E-1)) # wrong best y returned
def test_rosen_parallel():
# parallel execution slows down the test since we are using a test function
# which is very fast to evaluate
# popsize defines the maximal number of used threads
#windows cannot pickle function objects
if sys.platform.startswith('windows'):
return
popsize = 8
dim = 2
testfun = Rosen(dim)
sdevs = [1.0]*dim
max_eval = 10000
limit = 0.00001
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
ret = cmaes.minimize(wrapper.eval, testfun.bounds, input_sigma = sdevs,
max_evaluations = max_eval,
popsize=popsize, is_parallel=True)
if limit > ret.fun:
break
assert(limit > ret.fun) # optimization target not reached
assert(max_eval + popsize > ret.nfev) # too much function calls
assert(max_eval // popsize + 2 > ret.nit) # too much iterations
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 test_rosen_cpp():
if not sys.platform.startswith('linux'):
return
popsize = 31
dim = 5
testfun = Rosen(dim)
sdevs = [1.0] * dim
max_eval = 100000
limit = 0.00001
for i in range(5):
wrapper = Wrapper(testfun.func, dim)
ret = cmaescpp.minimize(wrapper.eval,
testfun.bounds,
input_sigma=sdevs,
max_evaluations=max_eval,
popsize=popsize)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize > ret.nfev) # too much function calls
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 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 test_eggholder_advanced_retry():
#windows cannot pickle function objects
if sys.platform.startswith('windows'):
return
dim = 2
testfun = Eggholder()
limit = -956
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
ret = advretry.minimize(wrapper.eval, testfun.bounds,
num_retries=300)
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 test_rosen_de_delayed():
popsize = 8
dim = 2
testfun = Rosen(dim)
max_eval = 10000
limit = 0.01
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
ret = de.minimize(wrapper.eval,
dim,
testfun.bounds,
max_evaluations=max_eval,
popsize=popsize,
workers=popsize)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize >= ret.nfev) # too much function calls
assert (max_eval // popsize + 2 > ret.nit) # too much iterations
assert (ret.nfev == wrapper.get_count()
) # wrong number of function calls returned
def test_rosen_delayed():
popsize = 8
dim = 2
testfun = Rosen(dim)
sdevs = [1.0] * dim
max_eval = 10000
limit = 0.00001
for _ in range(5):
wrapper = Wrapper(testfun.fun, dim)
ret = cmaes.minimize(wrapper.eval,
testfun.bounds,
input_sigma=sdevs,
max_evaluations=max_eval,
popsize=popsize,
workers=popsize,
delayed_update=True)
if limit > ret.fun:
break
assert (limit > ret.fun) # optimization target not reached
assert (max_eval + popsize >= ret.nfev) # too much function calls
assert (max_eval // popsize + 2 > ret.nit) # too much iterations
