def test_msrsm_parallel_st_miqp3_no_local_search(self):
"""Check solution of st_miqp3 with MSRSM and no local search.
Solver solution of global search problems."""
bb = tf.TestBlackBox('st_miqp3')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving st_miqp3 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(rbf='cubic',
global_search_method='genetic',
algorithm='MSRSM',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
num_cpus=4,
local_search_box_scaling=10000,
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_gutmann_parallel_ex8_1_4(self):
"""Check solution of ex8_1_4 with Gutmann's method, solver."""
bb = tf.TestBlackBox('ex8_1_4')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving ex8_1_4 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='Gutmann',
rbf='gaussian',
global_search_method='solver',
target_objval=optimum,
max_stalled_iterations=20,
eps_impr=0.05,
refinement_frequency=6,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
max_fraction_discarded=0.2,
num_cpus=2,
max_clock_time=1200,
rand_seed=seed)
alg = ra.RbfoptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve ex8_1_4 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_state_reload(self):
"""Check solution of ex8_1_4 after state save/reload."""
bb = tf.TestBlackBox('ex8_1_4')
optimum = bb._function.optimum_value
handle, filename = tempfile.mkstemp()
for seed in self.rand_seeds:
print()
print('Solving ex8_1_4 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='MSRSM',
rbf='linear',
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(5)
alg.save_to_file(filename)
alg_reload = ra.RbfoptAlgorithm.load_from_file(filename)
res = alg_reload.optimize()
msg = 'Could not solve ex8_1_4 after reload'
target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) >
settings.eps_zero else self.eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
os.close(handle)
os.remove(filename)
def test_prob03_fixed(self):
"""Check solution of prob03 with fixed variables."""
for seed in self.rand_seeds:
bb = tf.TestBlackBox('prob03')
optimum = bb._function.optimum_value
eps_opt = 0.0001
print()
print('Solving prob03 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='MSRSM',
target_objval=optimum,
eps_opt=eps_opt,
max_iterations=200,
max_evaluations=300,
rand_seed=seed)
alg = ra.RbfoptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Did not find global optimum in prob03'
target = optimum + (abs(optimum) * eps_opt if
abs(optimum) > settings.eps_zero else eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
msg = 'Used more evaluations than necessary in prob03'
self.assertLessEqual(res[3],
np.prod(bb.get_var_upper() -
bb.get_var_lower() + 1),
msg=msg)
msg = 'Used too many iterations in prob03'
self.assertLessEqual(
res[2],
2 * np.prod(bb.get_var_upper() - bb.get_var_lower() + 1),
msg=msg)
def test_categorical_vars(self):
"""Check solution of branin_cat with Gutmann.
Default parameters.
"""
bb = tf.TestBlackBox('branin_cat')
optimum = bb._function.optimum_value
eps_opt = 0.4
for seed in self.rand_seeds:
print()
print('Solving branin_cat with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='Gutmann',
target_objval=optimum,
eps_opt=eps_opt,
max_iterations=400,
max_evaluations=500,
do_infstep=True,
rand_seed=seed)
alg = ra.RbfoptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve branin_cat with Gutmann\'s algorithm'
target = optimum + (abs(optimum) * eps_opt if
abs(optimum) > settings.eps_zero else eps_opt)
self.assertLessEqual(res[0], target, msg=msg)
def test_gutmann_parallel_ex8_1_4_infstep(self):
"""Check solution of ex8_1_4 with Gutmann, infstep.
Genetic algorithm."""
bb = tf.TestBlackBox('ex8_1_4')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving ex8_1_4 with infstep and random seed ' +
'{:d}'.format(seed))
settings = RbfoptSettings(algorithm='Gutmann',
global_search_method='genetic',
rbf='multiquadric',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
num_cpus=2,
do_infstep=True,
refinement_frequency=5,
rand_seed=seed)
alg = ra.RbfoptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve ex8_1_4 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_ex8_1_4_log(self):
"""Check solution of ex8_1_4 with Gutmann, log scaling, infstep.
Sampling-based global search.
"""
bb = tf.TestBlackBox('ex8_1_4')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving ex8_1_4 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='Gutmann',
rbf='multiquadric',
global_search_method='sampling',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=200,
max_evaluations=300,
function_scaling='log',
do_infstep=True,
rand_seed=seed)
alg = ra.RbfoptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve ex8_1_4 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_distinct_init_points(self):
"""Verify that distinct points are accepted."""
bb = tf.TestBlackBox('branin')
optimum = bb._function.optimum_value
init_node_pos = [[0, 0], [1, 1], [0, 10], [5, 0]]
settings = RbfoptSettings(target_objval=optimum, max_iterations=10,
eps_opt=0.0, rand_seed=89123132)
alg = ra.RbfoptAlgorithm(settings, bb, init_node_pos=init_node_pos)
res = alg.optimize()
for node in init_node_pos:
self.assertIn(node, alg.all_node_pos,
msg='Initial point not found in all_node_pos')
def test_failed_init_points(self):
"""Verify that not enough points raises an error."""
bb = tf.TestBlackBox('branin')
optimum = bb._function.optimum_value
init_node_pos = [[0, 0], [1, 1], [0, 10], [5, 0]]
settings = RbfoptSettings(target_objval=optimum, max_iterations=10,
eps_opt=0.0)
alg = ra.RbfoptAlgorithm(settings, bb, init_node_pos=init_node_pos,
do_init_strategy=False)
res = alg.optimize()
for node in init_node_pos:
self.assertIn(node, alg.all_node_pos[:4],
msg='Initial point not found in all_node_pos')
def test_duplicate_init_points(self):
"""Verify that duplicate points are removed."""
bb = tf.TestBlackBox('branin')
optimum = bb._function.optimum_value
init_node_pos = [[0, 0], [0, 1], [0, 1.0e-12]]
settings = RbfoptSettings(target_objval=optimum, max_iterations=10,
eps_opt=0.0)
alg = ra.RbfoptAlgorithm(settings, bb, init_node_pos=init_node_pos,
do_init_strategy=False)
res = alg.optimize()
for node in init_node_pos[:2]:
self.assertIn(node, alg.all_node_pos,
msg='Initial point not found in all_node_pos')
self.assertNotIn(init_node_pos[-1], alg.all_node_pos[2:],
msg='Duplicate initial point found in all_node_pos')
def test_prob03_fixed(self):
"""Check solution of prob03 with fixed variables."""
for seed in self.rand_seeds:
bb = TestBlackBoxFixed('prob03', 5)
optimum = bb._function.optimum_value + bb.get_obj_shift()
print()
print('Solving prob03 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='MSRSM',
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 prob03 with fixed variables'
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_time_limit(self):
"""Verify that time limits are satisfied (MSRSM)."""
bb = tf.TestBlackBox('hartman6')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving hartman6 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='MSRSM',
global_search_method='solver',
target_objval=optimum,
eps_opt=0.0,
max_clock_time=2.0,
rand_seed=seed)
start_time = time.time()
alg = ra.RbfoptAlgorithm(settings, bb)
res = alg.optimize()
tot_time = time.time() - start_time
msg = 'Time limit exceeded with MSRSM algorithm'
self.assertLessEqual(tot_time, 5.0, msg=msg)
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_gutmann_ex8_1_4(self):
"""Check solution of ex8_1_4 with Gutmann's method, genetic."""
bb = tf.TestBlackBox('ex8_1_4')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving ex8_1_4 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='Gutmann',
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 ex8_1_4 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_msrsm_parallel_prob03(self):
"""Check solution of prob03 with the MSRSM algorithm, genetic."""
bb = tf.TestBlackBox('prob03')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving prob03 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='MSRSM',
global_search_method='genetic',
target_objval=optimum,
eps_opt=self.eps_opt,
max_iterations=500,
max_evaluations=500,
num_cpus=4,
rand_seed=seed)
alg = ra.RbfoptAlgorithm(settings, bb)
res = alg.optimize()
msg = 'Could not solve prob03 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_msrsm_parallel_ex8_1_4(self):
"""Check solution of ex8_1_4 with the MSRSM algorithm, sampling."""
bb = tf.TestBlackBox('ex8_1_4')
optimum = bb._function.optimum_value
for seed in self.rand_seeds:
print()
print('Solving ex8_1_4 with random seed ' + '{:d}'.format(seed))
settings = RbfoptSettings(algorithm='MSRSM',
global_search_method='sampling',
rbf='linear',
rbf_shape_parameter=0.01,
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 ex8_1_4 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_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)
