def test_ei():
np.random.seed(0)
ackley = Ackley(dim=1)
X = np.expand_dims([-15, -10, 0, 1, 20], axis=1)
fX = np.array([ackley.eval(x) for x in X])
gp = GPRegressor(dim=ackley.dim, lb=ackley.lb, ub=ackley.ub)
gp.add_points(X, fX)
# Find the global optimizer of EI
x_true = -1.7558
x_next = ei_ga(X=X,
Xpend=None,
dtol=0.0,
ei_tol=0,
fX=fX,
num_pts=1,
opt_prob=ackley,
surrogate=gp)
assert np.isclose(x_next, x_true, atol=1e-2)
# Find the optimizer at least distance 5 from other points
x_true = 10.6656
x_next = ei_ga(X=X,
Xpend=None,
dtol=5.0,
ei_tol=0,
fX=fX,
num_pts=1,
opt_prob=ackley,
surrogate=gp)
assert np.isclose(x_next, x_true, atol=1e-2)
def test_srbf():
np.random.seed(0)
ackley = Ackley(dim=1)
X = np.expand_dims([-15, -10, 0, 1, 20], axis=1)
fX = np.array([ackley.eval(x) for x in X])
gp = GPRegressor(dim=1)
gp.add_points(X, fX)
# Find the next point with w = 0.25
x_true = 10.50
x_next = candidate_uniform(
num_pts=1, X=X, Xpend=None, fX=fX, num_cand=10000,
surrogate=gp, opt_prob=ackley, weights=[0.25])
assert np.isclose(x_next, x_true, atol=1e-2)
x_next = candidate_srbf(
num_pts=1, X=X, Xpend=None, fX=fX, num_cand=10000,
surrogate=gp, opt_prob=ackley, weights=[0.25],
sampling_radius=0.5)
assert np.isclose(x_next, x_true, atol=1e-2)
# Find the next point with w = 0.75
x_true = -3.395
x_next = candidate_uniform(
num_pts=1, X=X, Xpend=None, fX=fX, num_cand=10000,
surrogate=gp, opt_prob=ackley, weights=[0.75])
assert np.isclose(x_next, x_true, atol=1e-2)
x_next = candidate_srbf(
num_pts=1, X=X, Xpend=None, fX=fX, num_cand=10000,
surrogate=gp, opt_prob=ackley, weights=[0.75],
sampling_radius=0.5)
assert np.isclose(x_next, x_true, atol=1e-2)
def test_ei():
np.random.seed(0)
ackley = Ackley(dim=1)
X = np.expand_dims([-15, -10, 0, 1, 20], axis=1)
fX = np.array([ackley.eval(x) for x in X])
gp = GPRegressor(dim=1)
gp.add_points(X, fX)
# Find the global optimizer of EI
x_true = -3.0556
x_next = expected_improvement_ga(
X=X, Xpend=None, dtol=0.0, ei_tol=0,
fX=fX, num_pts=1, opt_prob=ackley, surrogate=gp)
assert np.isclose(x_next, x_true, atol=1e-2)
x_next = expected_improvement_uniform(
X=X, Xpend=None, dtol=0.0, ei_tol=0,
fX=fX, num_pts=1, opt_prob=ackley, surrogate=gp,
num_cand=10000)
assert np.isclose(x_next, x_true, atol=1e-2)
# Find the optimizer at least distance 5 from other points
x_true = 11.14
x_next = expected_improvement_ga(
X=X, Xpend=None, dtol=5.0, ei_tol=0,
fX=fX, num_pts=1, opt_prob=ackley, surrogate=gp)
assert np.isclose(x_next, x_true, atol=1e-2)
x_next = expected_improvement_uniform(
X=X, Xpend=None, dtol=5.0, ei_tol=0,
fX=fX, num_pts=1, opt_prob=ackley, surrogate=gp,
num_cand=10000)
assert np.isclose(x_next, x_true, atol=1e-2)
def test_lcb_serial():
max_evals = 50
gp = GPRegressor(dim=ackley.dim)
slhd = SymmetricLatinHypercube(
dim=ackley.dim, num_pts=2*(ackley.dim+1))
# Create a strategy and a controller
controller = SerialController(ackley.eval)
controller.strategy = LCBStrategy(
max_evals=max_evals, opt_prob=ackley, exp_design=slhd,
surrogate=gp, asynchronous=True)
controller.run()
check_strategy(controller)
def test_lcb_async():
max_evals = 50
gp = GPRegressor(dim=ackley.dim)
slhd = SymmetricLatinHypercube(
dim=ackley.dim, num_pts=2*(ackley.dim+1))
# Create a strategy and a controller
controller = ThreadController()
controller.strategy = LCBStrategy(
max_evals=max_evals, opt_prob=ackley, exp_design=slhd,
surrogate=gp, asynchronous=True, batch_size=None)
for _ in range(num_threads):
worker = BasicWorkerThread(controller, ackley.eval)
controller.launch_worker(worker)
controller.run()
check_strategy(controller)
def example_lower_confidence_bounds():
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/example_lower_confidence_bounds.log"):
os.remove("./logfiles/example_lower_confidence_bounds.log")
logging.basicConfig(
filename="./logfiles/example_lower_confidence_bounds.log",
level=logging.INFO)
num_threads = 4
max_evals = 100
hart6 = Hartman6()
gp = GPRegressor(dim=hart6.dim)
slhd = SymmetricLatinHypercube(dim=hart6.dim, num_pts=2 * (hart6.dim + 1))
# Create a strategy and a controller
controller = ThreadController()
controller.strategy = LCBStrategy(max_evals=max_evals,
opt_prob=hart6,
exp_design=slhd,
surrogate=gp,
asynchronous=True)
print("Number of threads: {}".format(num_threads))
print("Maximum number of evaluations: {}".format(max_evals))
print("Strategy: {}".format(controller.strategy.__class__.__name__))
print("Experimental design: {}".format(slhd.__class__.__name__))
print("Surrogate: {}".format(gp.__class__.__name__))
# Launch the threads and give them access to the objective function
for _ in range(num_threads):
worker = BasicWorkerThread(controller, hart6.eval)
controller.launch_worker(worker)
# Run the optimization strategy
result = controller.run()
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
def test_gp():
X = make_grid(30) # Make uniform grid with 30 x 30 points
gp = GPRegressor(dim=2, lb=np.zeros(2), ub=np.ones(2))
assert isinstance(gp, Surrogate)
fX = f(X)
gp.add_points(X, fX)
# Derivative at random points
np.random.seed(0)
Xs = np.random.rand(10, 2)
fhx = gp.predict(Xs)
fx = f(Xs)
assert np.max(np.abs(fx - fhx)) < 1e-2
# Derivative at previous points
# Reset the surrogate
gp.reset()
assert gp.num_pts == 0 and gp.dim == 2
def run(self):
"""
Run the optimization
@return: Nothing
"""
self.problem = VoltageOptimizationProblem(
self.circuit,
self.options,
self.max_iter,
callback=self.progress_signal.emit)
# # (1) Optimization problem
# # print(data.info)
#
# # (2) Experimental design
# # Use a symmetric Latin hypercube with 2d + 1 samples
# exp_des = SymmetricLatinHypercube(dim=self.problem.dim, npts=2 * self.problem.dim + 1)
#
# # (3) Surrogate model
# # Use a cubic RBF interpolant with a linear tail
# surrogate = RBFInterpolant(kernel=CubicKernel, tail=LinearTail, maxp=self.max_eval)
#
# # (4) Adaptive sampling
# # Use DYCORS with 100d candidate points
# adapt_samp = CandidateDYCORS(data=self, numcand=100 * self.dim)
#
# # Use the serial controller (uses only one thread)
# controller = SerialController(self.objfunction)
#
# # (5) Use the sychronous strategy without non-bound constraints
# strategy = SyncStrategyNoConstraints(worker_id=0,
# data=self,
# maxeval=self.max_eval,
# nsamples=1,
# exp_design=exp_des,
# response_surface=surrogate,
# sampling_method=adapt_samp)
#
# controller.strategy = strategy
#
# # Run the optimization strategy
# result = controller.run()
#
# # Print the final result
# print('Best value found: {0}'.format(result.value))
# print('Best solution found: {0}'.format(np.array_str(result.params[0], max_line_width=np.inf, precision=5,
# suppress_small=True)))
num_threads = 4
surrogate_model = GPRegressor(dim=self.problem.dim)
sampler = SymmetricLatinHypercube(dim=self.problem.dim,
num_pts=2 * (self.problem.dim + 1))
# Create a strategy and a controller
controller = ThreadController()
controller.strategy = SRBFStrategy(max_evals=self.max_iter,
opt_prob=self.problem,
exp_design=sampler,
surrogate=surrogate_model,
asynchronous=True,
batch_size=num_threads)
print("Number of threads: {}".format(num_threads))
print("Maximum number of evaluations: {}".format(self.max_iter))
print("Strategy: {}".format(controller.strategy.__class__.__name__))
print("Experimental design: {}".format(sampler.__class__.__name__))
print("Surrogate: {}".format(surrogate_model.__class__.__name__))
# Launch the threads and give them access to the objective function
for _ in range(num_threads):
worker = BasicWorkerThread(controller, self.problem.eval)
controller.launch_worker(worker)
# Run the optimization strategy
result = controller.run()
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=4,
suppress_small=True)))
self.solution = result.params[0]
# Extract function values from the controller
self.optimization_values = np.array(
[o.value for o in controller.fevals])
# send the finnish signal
self.progress_signal.emit(0.0)
self.progress_text.emit('Done!')
self.done_signal.emit()
def pysot_cube(objective,
n_trials,
n_dim,
with_count=False,
method=None,
design=None):
""" Minimize
:param objective:
:param n_trials:
:param n_dim:
:param with_count:
:return:
"""
logging.getLogger('pySOT').setLevel(logging.ERROR)
num_threads = 1
asynchronous = True
max_evals = n_trials
gp = GenericProblem(dim=n_dim, objective=objective)
if design == 'latin':
exp_design = LatinHypercube(dim=n_dim, num_pts=2 * (n_dim + 1))
elif design == 'symmetric':
exp_design = SymmetricLatinHypercube(dim=n_dim,
num_pts=2 * (n_dim + 1))
elif design == 'factorial':
exp_design = TwoFactorial(dim=n_dim)
else:
raise ValueError('design should be latin, symmetric or factorial')
# Create a strategy and a controller
# SRBFStrategy, EIStrategy, DYCORSStrategy,RandomStrategy, LCBStrategy
controller = ThreadController()
if method.lower() == 'srbf':
surrogate = RBFInterpolant(dim=n_dim,
lb=np.array([0.0] * n_dim),
ub=np.array([1.0] * n_dim),
kernel=CubicKernel(),
tail=LinearTail(n_dim))
controller.strategy = SRBFStrategy(max_evals=max_evals,
opt_prob=gp,
exp_design=exp_design,
surrogate=surrogate,
asynchronous=asynchronous)
elif method.lower() == 'ei':
surrogate = GPRegressor(dim=n_dim,
lb=np.array([0.0] * n_dim),
ub=np.array([1.0] * n_dim))
controller.strategy = EIStrategy(max_evals=max_evals,
opt_prob=gp,
exp_design=exp_design,
surrogate=surrogate,
asynchronous=asynchronous)
elif method.lower() == 'dycors':
surrogate = RBFInterpolant(dim=n_dim,
lb=np.array([0.0] * n_dim),
ub=np.array([1.0] * n_dim),
kernel=CubicKernel(),
tail=LinearTail(n_dim))
controller.strategy = DYCORSStrategy(max_evals=max_evals,
opt_prob=gp,
exp_design=exp_design,
surrogate=surrogate,
asynchronous=asynchronous)
elif method.lower() == 'lcb':
surrogate = GPRegressor(dim=n_dim,
lb=np.array([0.0] * n_dim),
ub=np.array([1.0] * n_dim))
controller.strategy = LCBStrategy(max_evals=max_evals,
opt_prob=gp,
exp_design=exp_design,
surrogate=surrogate,
asynchronous=asynchronous)
elif method.lower() == 'random':
controller.strategy = RandomStrategy(max_evals=max_evals,
opt_prob=gp)
else:
raise ValueError("Didn't recognize method passed to pysot")
# Launch the threads and give them access to the objective function
for _ in range(num_threads):
worker = BasicWorkerThread(controller, gp.eval)
controller.launch_worker(worker)
# Run the optimization strategy
result = controller.run()
best_x = result.params[0].tolist()
return (result.value, best_x,
gp.feval_count) if with_count else (result.value, best_x)
def setup_backend(
self,
params,
strategy="SRBF",
surrogate="RBF",
design=None,
):
self.opt_problem = BBoptOptimizationProblem(params)
design_kwargs = dict(dim=self.opt_problem.dim)
_coconut_case_match_to_1 = design
_coconut_case_match_check_1 = False
if _coconut_case_match_to_1 is None:
_coconut_case_match_check_1 = True
if _coconut_case_match_check_1:
self.exp_design = EmptyExperimentalDesign(**design_kwargs)
if not _coconut_case_match_check_1:
if _coconut_case_match_to_1 == "latin_hypercube":
_coconut_case_match_check_1 = True
if _coconut_case_match_check_1:
self.exp_design = LatinHypercube(num_pts=2 *
(self.opt_problem.dim + 1),
**design_kwargs)
if not _coconut_case_match_check_1:
if _coconut_case_match_to_1 == "symmetric_latin_hypercube":
_coconut_case_match_check_1 = True
if _coconut_case_match_check_1:
self.exp_design = SymmetricLatinHypercube(
num_pts=2 * (self.opt_problem.dim + 1), **design_kwargs)
if not _coconut_case_match_check_1:
if _coconut_case_match_to_1 == "two_factorial":
_coconut_case_match_check_1 = True
if _coconut_case_match_check_1:
self.exp_design = TwoFactorial(**design_kwargs)
if not _coconut_case_match_check_1:
_coconut_match_set_name_design_cls = _coconut_sentinel
_coconut_match_set_name_design_cls = _coconut_case_match_to_1
_coconut_case_match_check_1 = True
if _coconut_case_match_check_1:
if _coconut_match_set_name_design_cls is not _coconut_sentinel:
design_cls = _coconut_case_match_to_1
if _coconut_case_match_check_1 and not (callable(design_cls)):
_coconut_case_match_check_1 = False
if _coconut_case_match_check_1:
self.exp_design = design_cls(**design_kwargs)
if not _coconut_case_match_check_1:
raise TypeError(
"unknown experimental design {_coconut_format_0!r}".format(
_coconut_format_0=(design)))
surrogate_kwargs = dict(dim=self.opt_problem.dim,
lb=self.opt_problem.lb,
ub=self.opt_problem.ub)
_coconut_case_match_to_2 = surrogate
_coconut_case_match_check_2 = False
if _coconut_case_match_to_2 == "RBF":
_coconut_case_match_check_2 = True
if _coconut_case_match_check_2:
self.surrogate = RBFInterpolant(
kernel=LinearKernel() if design is None else CubicKernel(),
tail=ConstantTail(self.opt_problem.dim)
if design is None else LinearTail(self.opt_problem.dim),
**surrogate_kwargs)
if not _coconut_case_match_check_2:
if _coconut_case_match_to_2 == "GP":
_coconut_case_match_check_2 = True
if _coconut_case_match_check_2:
self.surrogate = GPRegressor(**surrogate_kwargs)
if not _coconut_case_match_check_2:
_coconut_match_set_name_surrogate_cls = _coconut_sentinel
_coconut_match_set_name_surrogate_cls = _coconut_case_match_to_2
_coconut_case_match_check_2 = True
if _coconut_case_match_check_2:
if _coconut_match_set_name_surrogate_cls is not _coconut_sentinel:
surrogate_cls = _coconut_case_match_to_2
if _coconut_case_match_check_2 and not (callable(surrogate_cls)):
_coconut_case_match_check_2 = False
if _coconut_case_match_check_2:
self.surrogate = surrogate_cls(**surrogate_kwargs)
if not _coconut_case_match_check_2:
raise TypeError("unknown surrogate {_coconut_format_0!r}".format(
_coconut_format_0=(surrogate)))
strategy_kwargs = dict(max_evals=sys.maxsize,
opt_prob=self.opt_problem,
exp_design=self.exp_design,
surrogate=self.surrogate,
asynchronous=True,
batch_size=1)
_coconut_case_match_to_3 = strategy
_coconut_case_match_check_3 = False
if _coconut_case_match_to_3 == "SRBF":
_coconut_case_match_check_3 = True
if _coconut_case_match_check_3:
self.strategy = SRBFStrategy(**strategy_kwargs)
if not _coconut_case_match_check_3:
if _coconut_case_match_to_3 == "EI":
_coconut_case_match_check_3 = True
if _coconut_case_match_check_3:
self.strategy = EIStrategy(**strategy_kwargs)
if not _coconut_case_match_check_3:
if _coconut_case_match_to_3 == "DYCORS":
_coconut_case_match_check_3 = True
if _coconut_case_match_check_3:
self.strategy = DYCORSStrategy(**strategy_kwargs)
if not _coconut_case_match_check_3:
if _coconut_case_match_to_3 == "LCB":
_coconut_case_match_check_3 = True
if _coconut_case_match_check_3:
self.strategy = LCBStrategy(**strategy_kwargs)
if not _coconut_case_match_check_3:
_coconut_match_set_name_strategy_cls = _coconut_sentinel
_coconut_match_set_name_strategy_cls = _coconut_case_match_to_3
_coconut_case_match_check_3 = True
if _coconut_case_match_check_3:
if _coconut_match_set_name_strategy_cls is not _coconut_sentinel:
strategy_cls = _coconut_case_match_to_3
if _coconut_case_match_check_3 and not (callable(strategy_cls)):
_coconut_case_match_check_3 = False
if _coconut_case_match_check_3:
self.strategy = strategy_cls(**strategy_kwargs)
if not _coconut_case_match_check_3:
raise TypeError("unknown strategy {_coconut_format_0!r}".format(
_coconut_format_0=(strategy)))
class PySOTBackend(StandardBackend):
"""The pySOT backend uses pySOT for black box optimization."""
backend_name = "pySOT"
implemented_funcs = ("choice", "randrange", "uniform")
strategy = None
@override
def setup_backend(
self,
params,
strategy="SRBF",
surrogate="RBF",
design=None,
):
self.opt_problem = BBoptOptimizationProblem(params)
design_kwargs = dict(dim=self.opt_problem.dim)
_coconut_case_match_to_1 = design
_coconut_case_match_check_1 = False
if _coconut_case_match_to_1 is None:
_coconut_case_match_check_1 = True
if _coconut_case_match_check_1:
self.exp_design = EmptyExperimentalDesign(**design_kwargs)
if not _coconut_case_match_check_1:
if _coconut_case_match_to_1 == "latin_hypercube":
_coconut_case_match_check_1 = True
if _coconut_case_match_check_1:
self.exp_design = LatinHypercube(num_pts=2 *
(self.opt_problem.dim + 1),
**design_kwargs)
if not _coconut_case_match_check_1:
if _coconut_case_match_to_1 == "symmetric_latin_hypercube":
_coconut_case_match_check_1 = True
if _coconut_case_match_check_1:
self.exp_design = SymmetricLatinHypercube(
num_pts=2 * (self.opt_problem.dim + 1), **design_kwargs)
if not _coconut_case_match_check_1:
if _coconut_case_match_to_1 == "two_factorial":
_coconut_case_match_check_1 = True
if _coconut_case_match_check_1:
self.exp_design = TwoFactorial(**design_kwargs)
if not _coconut_case_match_check_1:
_coconut_match_set_name_design_cls = _coconut_sentinel
_coconut_match_set_name_design_cls = _coconut_case_match_to_1
_coconut_case_match_check_1 = True
if _coconut_case_match_check_1:
if _coconut_match_set_name_design_cls is not _coconut_sentinel:
design_cls = _coconut_case_match_to_1
if _coconut_case_match_check_1 and not (callable(design_cls)):
_coconut_case_match_check_1 = False
if _coconut_case_match_check_1:
self.exp_design = design_cls(**design_kwargs)
if not _coconut_case_match_check_1:
raise TypeError(
"unknown experimental design {_coconut_format_0!r}".format(
_coconut_format_0=(design)))
surrogate_kwargs = dict(dim=self.opt_problem.dim,
lb=self.opt_problem.lb,
ub=self.opt_problem.ub)
_coconut_case_match_to_2 = surrogate
_coconut_case_match_check_2 = False
if _coconut_case_match_to_2 == "RBF":
_coconut_case_match_check_2 = True
if _coconut_case_match_check_2:
self.surrogate = RBFInterpolant(
kernel=LinearKernel() if design is None else CubicKernel(),
tail=ConstantTail(self.opt_problem.dim)
if design is None else LinearTail(self.opt_problem.dim),
**surrogate_kwargs)
if not _coconut_case_match_check_2:
if _coconut_case_match_to_2 == "GP":
_coconut_case_match_check_2 = True
if _coconut_case_match_check_2:
self.surrogate = GPRegressor(**surrogate_kwargs)
if not _coconut_case_match_check_2:
_coconut_match_set_name_surrogate_cls = _coconut_sentinel
_coconut_match_set_name_surrogate_cls = _coconut_case_match_to_2
_coconut_case_match_check_2 = True
if _coconut_case_match_check_2:
if _coconut_match_set_name_surrogate_cls is not _coconut_sentinel:
surrogate_cls = _coconut_case_match_to_2
if _coconut_case_match_check_2 and not (callable(surrogate_cls)):
_coconut_case_match_check_2 = False
if _coconut_case_match_check_2:
self.surrogate = surrogate_cls(**surrogate_kwargs)
if not _coconut_case_match_check_2:
raise TypeError("unknown surrogate {_coconut_format_0!r}".format(
_coconut_format_0=(surrogate)))
strategy_kwargs = dict(max_evals=sys.maxsize,
opt_prob=self.opt_problem,
exp_design=self.exp_design,
surrogate=self.surrogate,
asynchronous=True,
batch_size=1)
_coconut_case_match_to_3 = strategy
_coconut_case_match_check_3 = False
if _coconut_case_match_to_3 == "SRBF":
_coconut_case_match_check_3 = True
if _coconut_case_match_check_3:
self.strategy = SRBFStrategy(**strategy_kwargs)
if not _coconut_case_match_check_3:
if _coconut_case_match_to_3 == "EI":
_coconut_case_match_check_3 = True
if _coconut_case_match_check_3:
self.strategy = EIStrategy(**strategy_kwargs)
if not _coconut_case_match_check_3:
if _coconut_case_match_to_3 == "DYCORS":
_coconut_case_match_check_3 = True
if _coconut_case_match_check_3:
self.strategy = DYCORSStrategy(**strategy_kwargs)
if not _coconut_case_match_check_3:
if _coconut_case_match_to_3 == "LCB":
_coconut_case_match_check_3 = True
if _coconut_case_match_check_3:
self.strategy = LCBStrategy(**strategy_kwargs)
if not _coconut_case_match_check_3:
_coconut_match_set_name_strategy_cls = _coconut_sentinel
_coconut_match_set_name_strategy_cls = _coconut_case_match_to_3
_coconut_case_match_check_3 = True
if _coconut_case_match_check_3:
if _coconut_match_set_name_strategy_cls is not _coconut_sentinel:
strategy_cls = _coconut_case_match_to_3
if _coconut_case_match_check_3 and not (callable(strategy_cls)):
_coconut_case_match_check_3 = False
if _coconut_case_match_check_3:
self.strategy = strategy_cls(**strategy_kwargs)
if not _coconut_case_match_check_3:
raise TypeError("unknown strategy {_coconut_format_0!r}".format(
_coconut_format_0=(strategy)))
@override
def tell_data(self, new_data, new_losses):
"""Special method that allows fast updating of the backend with new examples."""
points, values = self.opt_problem.get_points_values(
new_data, new_losses)
for i in range(points.shape[0]):
X = np.copy(points[i, :])
self.strategy.X = np.vstack((self.strategy.X, X))
self.strategy._X = np.vstack((self.strategy._X, X))
self.strategy.fX = np.vstack((self.strategy.fX, values[i]))
self.strategy._fX = np.vstack((self.strategy._fX, values[i]))
assert self.surrogate is self.strategy.surrogate, (
self.surrogate, self.strategy.surrogate)
self.surrogate.add_points(X, values[i])
@override
def get_next_values(self):
"""Special method to get the next set of values to evaluate."""
assert self.strategy._X.shape[0] > 0, self.strategy._X
assert self.surrogate.num_pts > 0, self.surrogate.num_pts
while True:
proposal = self.strategy.propose_action()
assert proposal, proposal
if proposal.action == "terminate":
proposal.accept()
elif proposal.action == "eval":
self.opt_problem.eval(*proposal.args)
self.strategy.pending_evals -= 1
self.strategy.remove_pending(proposal.args[0])
break
else:
proposal.reject()
assert self.opt_problem.got_values is not None, "pySOT optimization produced no values"
return self.opt_problem.got_values