def from_config(cls, config):
classname = cls.__name__
model = GPClassificationModel.from_config(config)
lb = config.gettensor(classname, "lb")
ub = config.gettensor(classname, "ub")
restarts = config.getint(classname, "restarts", fallback=10)
samps = config.getint(classname, "samps", fallback=1000)
assert lb.shape[0] == ub.shape[0], "bounds are of different shapes!"
dim = lb.shape[0]
acqf = config.getobj("experiment",
"acqf",
fallback=MCLevelSetEstimation)
acqf_name = acqf.__name__
default_extra_acqf_args = {
"beta": 3.98,
"target": 0.75,
"objective": ProbitObjective,
}
extra_acqf_args = {
k: config.getobj(acqf_name,
k,
fallback_type=float,
fallback=v,
warn=False)
for k, v in default_extra_acqf_args.items()
}
extra_acqf_args = _prune_extra_acqf_args(acqf, extra_acqf_args)
if ("objective" in extra_acqf_args.keys()
and extra_acqf_args["objective"] is not None):
extra_acqf_args["objective"] = extra_acqf_args["objective"]()
return cls(
lb=lb,
ub=ub,
restarts=restarts,
samps=samps,
dim=dim,
acqf=acqf,
model=model,
extra_acqf_args=extra_acqf_args,
)
def test_hyperparam_consistency(self):
# verify that creating the model `from_config` or with `__init__` has the same hyperparams
m1 = GPClassificationModel(lb=[1, 2], ub=[3, 4])
m2 = GPClassificationModel.from_config(
config=Config(config_dict={"common": {"lb": "[1,2]", "ub": "[3,4]"}})
)
self.assertTrue(isinstance(m1.covar_module, type(m2.covar_module)))
self.assertTrue(
isinstance(m1.covar_module.base_kernel, type(m2.covar_module.base_kernel))
)
self.assertTrue(isinstance(m1.mean_module, type(m2.mean_module)))
m1priors = list(m1.covar_module.named_priors())
m2priors = list(m2.covar_module.named_priors())
for p1, p2 in zip(m1priors, m2priors):
name1, parent1, prior1, paramtransforms1, priortransforms1 = p1
name2, parent2, prior2, paramtransforms2, priortransforms2 = p2
self.assertTrue(name1 == name2)
self.assertTrue(isinstance(parent1, type(parent2)))
self.assertTrue(isinstance(prior1, type(prior2)))