def initialize_model(train_x, train_obj, state_dict=None):
# define models for objective
model_obj = FixedNoiseGP(train_x, train_obj,
train_yvar.expand_as(train_obj)).to(train_x)
# combine into a multi-output GP model
model = ModelListGP(model_obj)
mll = SumMarginalLogLikelihood(model.likelihood, model)
# load state dict if it is passed
if state_dict is not None:
model.load_state_dict(state_dict)
return mll, model
def initialize_model(x, z, state_dict=None):
n = z.shape[-1]
gp_models = []
for i in range(n):
y = z[..., i].unsqueeze(-1)
gp_model = SingleTaskGP(train_X=x, train_Y=y)
gp_model.likelihood.noise_covar.register_constraint(
"raw_noise", GreaterThan(1e-5))
gp_models.append(gp_model)
model_list = ModelListGP(*gp_models)
mll = SumMarginalLogLikelihood(model_list.likelihood, model_list)
if state_dict is not None:
model_list.load_state_dict(state_dict)
return mll, model_list
def initialize_model(train_x, train_obj, train_con, state_dict=None):
if problem.num_constraints == 1:
# define models for objective and constraint
# model_obj = SingleTaskGP(train_x, train_obj, outcome_transform=Standardize(m=train_obj.shape[-1]))
# model_con = SingleTaskGP(train_x, train_con, outcome_transform=Standardize(m=train_con.shape[-1]))
model_obj = SingleTaskGP(train_x, train_obj)
model_con = SingleTaskGP(train_x, train_con)
# combine into a multi-output GP model
model = ModelListGP(model_obj, model_con)
mll = SumMarginalLogLikelihood(model.likelihood, model)
else:
train_y = torch.cat([train_obj, train_con], dim=-1)
model = SingleTaskGP(
train_x,
train_y,
outcome_transform=Standardize(m=train_y.shape[-1]))
mll = ExactMarginalLogLikelihood(model.likelihood, model)
# load state dict if it is passed
if state_dict is not None:
model.load_state_dict(state_dict)
return mll, model