def get_hartmann_metric(name="hartmann") -> Hartmann6Metric:
param_names = [f"x{idx + 1}" for idx in range(6)]
return Hartmann6Metric(name=name, param_names=param_names, noise_sd=0.01)
def get_hartmann_metric() -> Hartmann6Metric:
return Hartmann6Metric(name="hartmann",
param_names=["x1", "x2"],
noise_sd=0.01)
### Hartmann6 problem, D=100 and D=1000
# Relevant parameters were chosen randomly using
# x = np.arange(100)
# np.random.seed(10)
# np.random.shuffle(x)
# print(x[:6]) # [19 14 43 37 66 3]
hartmann6_100 = BenchmarkProblem(
name="Hartmann6, D=100",
optimal_value=-3.32237,
optimization_config=OptimizationConfig(
objective=Objective(
metric=Hartmann6Metric(
name="objective",
param_names=["x19", "x14", "x43", "x37", "x66", "x3"],
noise_sd=0.0,
),
minimize=True,
)
),
search_space=SearchSpace(
parameters=[
RangeParameter(
name=f"x{i}", parameter_type=ParameterType.FLOAT, lower=0.0, upper=1.0
)
for i in range(100)
]
),
)
metric=NegativeBraninMetric(
name="neg_branin", param_names=["x1", "x2"], noise_sd=5.0),
minimize=False,
)),
search_space=get_branin_search_space(),
)
# Hartmann 6 problems
hartmann6 = BenchmarkProblem(
name=hartmann6_function.name,
fbest=hartmann6_function.fmin,
optimization_config=OptimizationConfig(objective=Objective(
metric=Hartmann6Metric(
name=hartmann6_function.name,
param_names=[f"x{i}" for i in range(6)],
noise_sd=0.01,
),
minimize=True,
)),
search_space=SearchSpace(parameters=[
RangeParameter(
name=f"x{i}",
parameter_type=ParameterType.FLOAT,
lower=param_domain[0],
upper=param_domain[1],
) for i, param_domain in enumerate(hartmann6_function.domain)
]),
)
hartmann6_constrained = BenchmarkProblem(
def test_REMBOStrategy(self, mock_fit_gpytorch_model, mock_optimize_acqf):
# Construct a high-D test experiment with multiple metrics
hartmann_search_space = SearchSpace(
parameters=[
RangeParameter(
name=f"x{i}",
parameter_type=ParameterType.FLOAT,
lower=0.0,
upper=1.0,
)
for i in range(20)
]
)
exp = Experiment(
name="test",
search_space=hartmann_search_space,
optimization_config=OptimizationConfig(
objective=Objective(
metric=Hartmann6Metric(
name="hartmann6", param_names=[f"x{i}" for i in range(6)]
),
minimize=True,
),
outcome_constraints=[
OutcomeConstraint(
metric=L2NormMetric(
name="l2norm",
param_names=[f"x{i}" for i in range(6)],
noise_sd=0.2,
),
op=ComparisonOp.LEQ,
bound=1.25,
relative=False,
)
],
),
runner=SyntheticRunner(),
)
# Instantiate the strategy
gs = REMBOStrategy(D=20, d=6, k=4, init_per_proj=4)
# Check that arms and data are correctly segmented by projection
exp.new_batch_trial(generator_run=gs.gen(experiment=exp, n=2)).run()
self.assertEqual(len(gs.arms_by_proj[0]), 2)
self.assertEqual(len(gs.arms_by_proj[1]), 0)
exp.new_batch_trial(generator_run=gs.gen(experiment=exp, n=2)).run()
self.assertEqual(len(gs.arms_by_proj[0]), 2)
self.assertEqual(len(gs.arms_by_proj[1]), 2)
# Iterate until the first projection fits a GP
for _ in range(4):
exp.new_batch_trial(generator_run=gs.gen(experiment=exp, n=2)).run()
mock_fit_gpytorch_model.assert_not_called()
self.assertEqual(len(gs.arms_by_proj[0]), 4)
self.assertEqual(len(gs.arms_by_proj[1]), 4)
self.assertEqual(len(gs.arms_by_proj[2]), 2)
self.assertEqual(len(gs.arms_by_proj[3]), 2)
# Keep iterating until GP is used for gen
for i in range(4):
# First two trials will go towards 3rd and 4th proj. getting enough
if i < 1: # data for GP.
self.assertLess(len(gs.arms_by_proj[2]), 4)
if i < 2:
self.assertLess(len(gs.arms_by_proj[3]), 4)
exp.new_batch_trial(generator_run=gs.gen(experiment=exp, n=2)).run()
if i < 2:
mock_fit_gpytorch_model.assert_not_called()
else:
# After all proj. have > 4 arms' worth of data, GP can be fit.
self.assertFalse(any(len(x) < 4 for x in gs.arms_by_proj.values()))
mock_fit_gpytorch_model.assert_called()
self.assertTrue(len(gs.model_transitions) > 0)
gs2 = gs.clone_reset()
self.assertEqual(gs2.D, 20)
self.assertEqual(gs2.d, 6)