def _get_model(self, dtype=torch.float):
state_dict = {
"mean_module.constant":
torch.tensor([-0.0066]),
"covar_module.raw_outputscale":
torch.tensor(1.0143),
"covar_module.base_kernel.raw_lengthscale":
torch.tensor([[-0.99]]),
"covar_module.base_kernel.lengthscale_prior.concentration":
torch.tensor(3.0),
"covar_module.base_kernel.lengthscale_prior.rate":
torch.tensor(6.0),
"covar_module.outputscale_prior.concentration":
torch.tensor(2.0),
"covar_module.outputscale_prior.rate":
torch.tensor(0.1500),
}
train_x = torch.linspace(0, 1, 10, device=self.device,
dtype=dtype).unsqueeze(-1)
train_y = torch.sin(train_x * (2 * math.pi))
noise = torch.tensor(NEI_NOISE, device=self.device, dtype=dtype)
train_y += noise
train_yvar = torch.full_like(train_y, 0.25**2)
model = FixedNoiseGP(train_X=train_x,
train_Y=train_y,
train_Yvar=train_yvar)
model.load_state_dict(state_dict)
model.to(train_x)
model.eval()
return model
def _setUp(self, double=False):
dtype = torch.double if double else torch.float
train_x = torch.linspace(0, 1, 10, device=self.device,
dtype=dtype).unsqueeze(-1)
train_y = torch.sin(train_x * (2 * math.pi))
train_yvar = torch.tensor(0.1**2, device=self.device)
noise = torch.tensor(NOISE, device=self.device, dtype=dtype)
self.train_x = train_x
self.train_y = train_y + noise
self.train_yvar = train_yvar
self.bounds = torch.tensor([[0.0], [1.0]],
device=self.device,
dtype=dtype)
model_st = SingleTaskGP(self.train_x, self.train_y)
self.model_st = model_st.to(device=self.device, dtype=dtype)
self.mll_st = ExactMarginalLogLikelihood(self.model_st.likelihood,
self.model_st)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=OptimizationWarning)
self.mll_st = fit_gpytorch_model(self.mll_st,
options={"maxiter": 5},
max_retries=1)
model_fn = FixedNoiseGP(self.train_x, self.train_y,
self.train_yvar.expand_as(self.train_y))
self.model_fn = model_fn.to(device=self.device, dtype=dtype)
self.mll_fn = ExactMarginalLogLikelihood(self.model_fn.likelihood,
self.model_fn)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=OptimizationWarning)
self.mll_fn = fit_gpytorch_model(self.mll_fn,
options={"maxiter": 5},
max_retries=1)
def _setUp(self, double=False, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
dtype = torch.double if double else torch.float
train_x = torch.linspace(0, 1, 10, device=device, dtype=dtype).unsqueeze(-1)
train_y = torch.sin(train_x * (2 * math.pi)).squeeze(-1)
train_yvar = torch.tensor(0.1 ** 2, device=device)
noise = torch.tensor(NOISE, device=device, dtype=dtype)
self.train_x = train_x
self.train_y = train_y + noise
self.train_yvar = train_yvar
self.bounds = torch.tensor([[0.0], [1.0]], device=device, dtype=dtype)
model_st = SingleTaskGP(self.train_x, self.train_y)
self.model_st = model_st.to(device=device, dtype=dtype)
self.mll_st = ExactMarginalLogLikelihood(
self.model_st.likelihood, self.model_st
)
self.mll_st = fit_gpytorch_model(self.mll_st, options={"maxiter": 5})
model_fn = FixedNoiseGP(
self.train_x, self.train_y, self.train_yvar.expand_as(self.train_y)
)
self.model_fn = model_fn.to(device=device, dtype=dtype)
self.mll_fn = ExactMarginalLogLikelihood(
self.model_fn.likelihood, self.model_fn
)
self.mll_fn = fit_gpytorch_model(self.mll_fn, options={"maxiter": 5})
def _setUp(self, double=False, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
dtype = torch.double if double else torch.float
train_x = torch.linspace(0, 1, 10, device=device, dtype=dtype).unsqueeze(-1)
train_y = torch.sin(train_x * (2 * math.pi)).squeeze(-1)
train_yvar = torch.tensor(0.1 ** 2, device=device)
noise = torch.tensor(NOISE, device=device, dtype=dtype)
self.train_x = train_x
self.train_y = train_y + noise
self.train_yvar = train_yvar
self.bounds = torch.tensor([[0.0], [1.0]], device=device, dtype=dtype)
model_st = SingleTaskGP(self.train_x, self.train_y)
self.model_st = model_st.to(device=device, dtype=dtype)
self.mll_st = ExactMarginalLogLikelihood(
self.model_st.likelihood, self.model_st
)
self.mll_st = fit_gpytorch_model(self.mll_st, options={"maxiter": 5})
model_fn = FixedNoiseGP(
self.train_x, self.train_y, self.train_yvar.expand_as(self.train_y)
)
self.model_fn = model_fn.to(device=device, dtype=dtype)
self.mll_fn = ExactMarginalLogLikelihood(
self.model_fn.likelihood, self.model_fn
)
self.mll_fn = fit_gpytorch_model(self.mll_fn, options={"maxiter": 5})
def _get_model(self, cuda=False, dtype=torch.float):
device = torch.device("cuda") if cuda else torch.device("cpu")
state_dict = {
"mean_module.constant": torch.tensor([-0.0066]),
"covar_module.raw_outputscale": torch.tensor(1.0143),
"covar_module.base_kernel.raw_lengthscale": torch.tensor([[-0.99]]),
"covar_module.base_kernel.lengthscale_prior.concentration": torch.tensor(
3.0
),
"covar_module.base_kernel.lengthscale_prior.rate": torch.tensor(6.0),
"covar_module.outputscale_prior.concentration": torch.tensor(2.0),
"covar_module.outputscale_prior.rate": torch.tensor(0.1500),
}
train_x = torch.linspace(0, 1, 10, device=device, dtype=dtype)
train_y = torch.sin(train_x * (2 * math.pi))
noise = torch.tensor(NEI_NOISE, device=device, dtype=dtype)
train_y += noise
train_yvar = torch.full_like(train_y, 0.25 ** 2)
train_x = train_x.view(-1, 1)
model = FixedNoiseGP(train_X=train_x, train_Y=train_y, train_Yvar=train_yvar)
model.load_state_dict(state_dict)
model.to(train_x)
model.eval()
return model