def compute_loss(self, *args, **kwargs):
if self.num_data == 0:
loss = TensorType([0.0])
loss.requires_grad_(True)
return loss
else:
return super().loss(*args, **kwargs)
def func_and_grad(x):
x = TensorType(np.atleast_2d(x))
x.requires_grad_(True)
m, v = self.predict_function(x)
s = v.sqrt()
if not self.y: # No current data: use mean ("everything is an improvement")
f = m
else:
f = _expected_improvement(m, s, min(self.y), mode="min")
if f.requires_grad:
f.backward()
g = x.grad.detach().cpu().numpy().flatten()
else:
g = 0.0 * x.detach().cpu().numpy().flatten()
f = f.detach().cpu().item()
return f, g
