def test_optima_noisy(self):
"""Ensure that noisy functions keep the optimum."""
for name in self.function_list:
bb = tf.TestBlackBox(name)
noisybb = tf.TestNoisyBlackBox(bb, 0.0, 0.1)
self.assertAlmostEqual(
noisybb.evaluate(bb._function.optimum_point),
bb._function.optimum_value, delta=0.1,
msg='Noisy function ' + name + ': absolute error exceeded')
for name in self.function_list:
bb = tf.TestBlackBox(name)
noisybb = tf.TestNoisyBlackBox(bb, 0.1, 0.0)
funval = noisybb.evaluate(bb._function.optimum_point)
if (abs(bb._function.optimum_value) > 1.0e-8):
error = (abs(funval - bb._function.optimum_value) /
abs(bb._function.optimum_value))
else:
error = abs(funval - bb._function.optimum_value)
self.assertLessEqual(error, 0.1,
msg='Noisy function ' + name +
': relative error exceeded')
# -- end function
def test_msrsm_st_miqp3_noisy(self):
"""Check solution of noisy st_miqp3 with MSRSM, genetic."""
bb = tf.TestNoisyBlackBox(tf.TestBlackBox('st_miqp3'), 0.1, 0.01)
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving st_miqp3 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='MSRSM',
global_search_method='genetic',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
rand_seed=seed)
alg = ra.RbfoptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve st_miqp3 with MSRSM algorithm'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
def test_msrsm_branin_noisy_with_init(self):
"""Check solution of noisy branin with MSRSM, sampling."""
bb = tf.TestNoisyBlackBox(tf.TestBlackBox('branin'), 0.1, 0.01)
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving branin with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='MSRSM',
global_search_method='sampling',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
rand_seed=seed)
init_node_pos = [[0, 0], [-2, 2], [5, 10], [-2.5, 1]]
alg = ra.RbfoptAlgorithm(settings, bb, init_node_pos)
res = alg.optimize()
msg = ('Could not solve noisy branin with init and ' +
'MSRSM algorithm')
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
def test_gutmann_parallel_st_miqp3_noisy(self):
"""Check solution of noisy st_miqp3 with Gutmann, solver."""
bb = tf.TestNoisyBlackBox(tf.TestBlackBox('st_miqp3'), 0.1, 0.01)
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving st_miqp3 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='Gutmann',
global_search_method='solver',
rbf_shape_parameter=0.01,
rbf='gaussian',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
num_cpus=2,
rand_seed=seed)
alg = ra.RbfoptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve st_miqp3 with Gutmann\'s algorithm'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
def test_gutmann_branin_cat_noisy_with_init(self):
"""Check solution of noisy branin_cat with Gutmann, solver."""
bb = tf.TestNoisyBlackBox(tf.TestBlackBox('branin_cat'), 0.1, 0.01)
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving branin_cat with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='Gutmann',
global_search_method='solver',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
rand_seed=seed)
init_node_pos = [[0, 0, 0], [-2, 2, 1], [5, 10, 2]]
init_node_val = [bb._function.evaluate(x) for x in init_node_pos]
alg = ra.RbfoptAlgorithm(settings, bb, init_node_pos,
init_node_val)
res = alg.optimize()
msg = ('Could not solve noisy branin with init and ' +
'Gutmann\'s algorithm')
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
