def get_task_runner(self, param, **kw):
blackbox = BraninConstrained(noise_std=param.noise_std)
bounds = Tensor(blackbox._bounds)
objective = ClassicAugmentedLagrangianMCObjective(
objective=lambda y: y[..., 0],
constraints=list(lambda y, i=j: y[..., i] for j in range(1, blackbox.out_dim)),
r=param['r']
)
sampler = SobolQMCNormalSampler(
num_samples=param.num_mc_samples,
seed=param.get('seed', None)
)
acqfopt = qEiAcqfOptimizer(sampler=sampler)
optimizer = AlboOptimizer(
blackbox=blackbox,
objective=objective,
acqfopt=acqfopt,
sampler=sampler,
bounds=bounds
)
run = partial(
optimizer.optimize,
niter=param.num_iter,
init_samples=param.num_init_samples,
al_iter=param.num_al_iter,
seed=param.get('seed', None)
)
return run
def main():
function = GardnerTestFunction()
for j in range(25):
x, y = generate_initial_data()
for i in range(50):
mll, model = initialize_model(x, y)
fit_gpytorch_model(mll)
augmented_objective = ClassicAugmentedLagrangianMCObjective(
objective=lambda y: y[..., 0],
constraints=[lambda y: y[..., 1]])
x_new = fit_augmented_objective(model, augmented_objective, x, y)
y_new = function(x_new)
x = torch.cat([x, x_new], dim=0)
y = torch.cat([y, y_new], dim=0)
np.save(f'results/x_{j}.bin', x)
np.save(f'results/y_{j}.bin', y)