enh: add normaliztion and standardization to workflow

pdf_agents/scientific_value.py

@@ -9,6 +9,7 @@
 from botorch.acquisition import UpperConfidenceBound, qUpperConfidenceBound
 from botorch.models import SingleTaskGP
 from botorch.optim import optimize_acqf  # noqa: F401
+from botorch.utils.transforms import normalize, standardize, unnormalize
 from gpytorch.mlls import ExactMarginalLogLikelihood
 from scipy.spatial import distance_matrix
 
@@ -132,7 +133,9 @@ def ask(self, batch_size: int = 1):
         train_x = torch.tensor(self.independent_cache, dtype=torch.double, device=self.device)
         if train_x.dim() == 1:
             train_x = train_x.view(-1, 1)
-        train_y = torch.tensor(value, dtype=torch.double, device=self.device)
+        norm_bounds = torch.stack([train_x.min(dim=0).values, train_x.max(dim=0).values])
+        train_x = normalize(train_x, norm_bounds)
+        train_y = standardize(torch.tensor(value, dtype=torch.double, device=self.device))
         gp = SingleTaskGP(train_x, train_y).to(self.device)
         mll = ExactMarginalLogLikelihood(gp.likelihood, gp).to(self.device)
         fit_gpytorch_mll(mll)
@@ -146,12 +149,15 @@ def ask(self, batch_size: int = 1):
         # candidates, acq_value = optimize_acqf(
         #     acq, bounds=self.bounds, q=batch_size, num_restarts=self.num_restarts, raw_samples=self.raw_samples
         # )
-        grid = torch.tensor(make_wafer_grid_list(*self.bounds.cpu().numpy().ravel(), step=self.motor_resolution))[
-            :, None, :
-        ]
+        grid = normalize(
+            torch.tensor(make_wafer_grid_list(*self.bounds.cpu().numpy().ravel(), step=self.motor_resolution))[
+                :, None, :
+            ],
+            norm_bounds,
+        )
         acq_grid = acq(grid)
         top_indicies = torch.argsort(acq_grid, descending=True, dim=0)[:batch_size]
-        candidates = grid[top_indicies].squeeze(1)
+        candidates = unnormalize(grid, norm_bounds)[top_indicies].squeeze(1)
         acq_value = acq_grid[top_indicies]
 
         if batch_size == 1: