def get_initial_evaluations(dim):
if dim == 1:
# Initial points used for figure 1 in the paper.
train_x = torch.Tensor([[0.93452506], [0.18872502], [0.89790337],
[0.95841797], [0.82335255], [0.45000000],
[0.50000000]])
train_y = torch.Tensor([
-0.4532849, -0.66614552, -0.92803395, 0.08880341, -0.27683621,
1.000000, 1.500000
])
elif dim == 2:
branin_fun = Branin(noise_std=None, negate=False)
train_x = draw_sobol_samples(bounds=torch.Tensor(
([0.0] * dim, [1.0] * dim)),
n=4,
q=1).squeeze(1)
train_y = branin_fun(train_x)
elif dim == 6:
hartman_fun = Hartmann()
train_x = draw_sobol_samples(bounds=torch.Tensor(
([0.0] * dim, [1.0] * dim)),
n=4,
q=1).squeeze(1)
train_y = hartman_fun(train_x)
return train_x, train_y
def get_initial_evaluations(dim,eval_type=1): if dim == 1: if eval_type == 1: train_x = torch.tensor([[0.1],[0.3],[0.65],[0.7],[0.9]],device=device, dtype=torch.float32, requires_grad=False) train_y = torch.tensor([-0.5,2.0,1.0,INF,INF],device=device, dtype=torch.float32, requires_grad=False) # We place inf to emphasize the absence of measurement train_l = torch.tensor([+1, +1, +1, -1, -1],device=device, requires_grad=False, dtype=torch.float32) elif eval_type == 2: train_x = torch.tensor([[0.7],[0.9]],device=device, dtype=torch.float32, requires_grad=False) train_y = torch.tensor([INF,INF],device=device, dtype=torch.float32, requires_grad=False) # We place inf to emphasize the absence of measurement train_l = torch.tensor([-1, -1],device=device, requires_grad=False, dtype=torch.float32) else: train_x = torch.tensor([[0.7],[0.9]],device=device, dtype=torch.float32, requires_grad=False) train_y = torch.tensor([-0.5,2.0],device=device, dtype=torch.float32, requires_grad=False) # We place inf to emphasize the absence of measurement train_l = torch.tensor([+1, +1],device=device, requires_grad=False, dtype=torch.float32) # Put them together: train_yl = torch.cat([train_y[:,None], train_l[:,None]],dim=1) elif dim == 2: branin_fun = Branin(noise_std=None, negate=False) train_x = draw_sobol_samples(bounds=torch.tensor(([0.0]*dim,[1.0]*dim)),n=4,q=1).squeeze(1) train_y = branin_fun(train_x) elif dim == 6: hartman_fun = Hartmann() train_x = draw_sobol_samples(bounds=torch.tensor(([0.0]*dim,[1.0]*dim)),n=4,q=1).squeeze(1) train_y = hartman_fun(train_x) return train_x, train_yl
def __init__(self, noise_std: Optional[float] = None, negate: bool = False) -> None:
r"""Constructor for Branin-Currin.
Arguments:
noise_std: Standard deviation of the observation noise.
negate: If True, negate the objectives.
"""
super().__init__(noise_std=noise_std, negate=negate)
self._branin = Branin()
def function_evaluate(xnext, model=None):
dim = xnext.shape[1]
if dim == 1:
predictive = model(xnext)
y_new = predictive.sample(torch.Size([1]))
elif dim == 2:
branin_fun = Branin(noise_std=None, negate=False)
y_new = branin_fun(xnext)
elif dim == 6:
hartman_fun = Hartmann()
y_new = hartman_fun(xnext)
return y_new
from ax.benchmark.benchmark_problem import BenchmarkProblem, SimpleBenchmarkProblem
from ax.utils.measurement.synthetic_functions import from_botorch
from ax.utils.testing.core_stubs import (
get_augmented_branin_optimization_config,
get_augmented_hartmann_optimization_config,
get_branin_search_space,
get_hartmann_search_space,
)
from botorch.test_functions.synthetic import Ackley, Branin
# Initialize the single-fidelity problems
ackley = SimpleBenchmarkProblem(f=from_botorch(Ackley()), noise_sd=0.0, minimize=True)
branin = SimpleBenchmarkProblem(f=from_botorch(Branin()), noise_sd=0.0, minimize=True)
single_fidelity_problem_group = [ackley, branin]
# Initialize the multi-fidelity problems
augmented_branin = BenchmarkProblem(
search_space=get_branin_search_space(with_fidelity_parameter=True),
optimization_config=get_augmented_branin_optimization_config(),
)
augmented_hartmann = BenchmarkProblem(
search_space=get_hartmann_search_space(with_fidelity_parameter=True),
optimization_config=get_augmented_hartmann_optimization_config(),
)
multi_fidelity_problem_group = [augmented_branin, augmented_hartmann]
# Gather all of the problems
MODULAR_BOTORCH_PROBLEM_GROUPS = {
class TestBranin(SyntheticTestFunctionBaseTestCase, BotorchTestCase):
functions = [Branin(), Branin(negate=True), Branin(noise_std=0.1)]
from ax.benchmark.benchmark_problem import BenchmarkProblem, SimpleBenchmarkProblem
from ax.utils.measurement.synthetic_functions import from_botorch
from ax.utils.testing.core_stubs import (
get_augmented_branin_optimization_config,
get_augmented_hartmann_optimization_config,
get_branin_search_space,
get_hartmann_search_space,
)
from botorch.test_functions.synthetic import Ackley, Branin
# Initialize the single-fidelity problems
ackley = SimpleBenchmarkProblem(f=from_botorch(Ackley()),
noise_sd=0.0,
minimize=True)
branin = SimpleBenchmarkProblem(f=from_botorch(Branin()),
noise_sd=0.0,
minimize=True)
single_fidelity_problem_group = [ackley, branin]
# Initialize the multi-fidelity problems
augmented_branin = BenchmarkProblem(
search_space=get_branin_search_space(with_fidelity_parameter=True),
optimization_config=get_augmented_branin_optimization_config(),
)
augmented_hartmann = BenchmarkProblem(
search_space=get_hartmann_search_space(with_fidelity_parameter=True),
optimization_config=get_augmented_hartmann_optimization_config(),
)
multi_fidelity_problem_group = [augmented_branin, augmented_hartmann]