Black upgrade (#439)

Summary:
Pull Request resolved: #439

The parenthesis change is pretty disgusting, but I guess it is what it is.

Reviewed By: danielrjiang

Differential Revision: D21370208

fbshipit-source-id: bf2f7fe496c6bdecd50e4e3369527698aafc0c6d

Author: Balandat

botorch/acquisition/utils.py

@@ -143,7 +143,7 @@ def get_infeasible_cost(
     """
     posterior = model.posterior(X)
     lb = objective(posterior.mean - 6 * posterior.variance.clamp_min(0).sqrt()).min()
-    M = -lb.clamp_max(0.0)
+    M = -(lb.clamp_max(0.0))
     return M.item()
 
 

botorch/generation/gen.py

@@ -203,7 +203,7 @@ def gen_candidates_torch(
         i += 1
         loss = -acquisition_function(candidates).sum()
         if verbose:
-            print("Iter: {} - Value: {:.3f}".format(i, -loss.item()))
+            print("Iter: {} - Value: {:.3f}".format(i, -(loss.item())))
         loss_trajectory.append(loss.item())
         param_trajectory["candidates"].append(candidates.clone())
 

botorch/models/gpytorch.py

@@ -91,7 +91,7 @@ def _validate_tensor_args(
             )
         # Yvar may not have the same batch dimensions, but the trailing dimensions
         # of Yvar should be the same as the trailing dimensions of Y.
-        if Yvar is not None and Y.shape[-Yvar.dim() :] != Yvar.shape:
+        if Yvar is not None and Y.shape[-(Yvar.dim()) :] != Yvar.shape:
             raise BotorchTensorDimensionError(
                 "An explicit output dimension is required for observation noise."
                 f" Expected Yvar with shape: {Y.shape[-Yvar.dim() :]} (got"

botorch/models/pairwise_gp.py

@@ -445,7 +445,7 @@ def _posterior_f(self, utility: Union[Tensor, np.ndarray]) -> Tensor:
             utility: A Tensor of shape `batch_size x n`
         """
         _, _, z_logcdf, _ = self._calc_z(utility, self.D, self.std_noise)
-        loss1 = -torch.sum(z_logcdf, dim=-1)
+        loss1 = -(torch.sum(z_logcdf, dim=-1))
         inv_prod = torch.cholesky_solve(utility.unsqueeze(-1), self.covar_chol)
         loss2 = 0.5 * (utility.unsqueeze(-2) @ inv_prod).squeeze(-1).squeeze(-1)
         loss = loss1 + loss2

botorch/test_functions/multi_fidelity.py

@@ -101,7 +101,7 @@ def evaluate_true(self, X: Tensor) -> Tensor:
         )
         alpha1 = self.ALPHA[0] - 0.1 * (1 - X[..., 6])
         H = (
-            -torch.sum(self.ALPHA[1:] * torch.exp(-inner_sum)[..., 1:], dim=1)
+            -(torch.sum(self.ALPHA[1:] * torch.exp(-inner_sum)[..., 1:], dim=1))
             - alpha1 * torch.exp(-inner_sum)[..., 0]
         )
         return H
@@ -139,4 +139,4 @@ def evaluate_true(self, X: Tensor) -> Tensor:
         X_next = X[..., 1:-2]
         t1 = 100 * (X_next - X_curr ** 2 + 0.1 * (1 - X[..., -2:-1])) ** 2
         t2 = (X_curr - 1 + 0.1 * (1 - X[..., -1:]) ** 2) ** 2
-        return -(t1 + t2).sum(dim=-1)
+        return -((t1 + t2).sum(dim=-1))

botorch/test_functions/synthetic.py

@@ -75,7 +75,7 @@ def __init__(
     def evaluate_true(self, X: Tensor) -> Tensor:
         a, b, c = self.a, self.b, self.c
         part1 = -a * torch.exp(-b / math.sqrt(self.dim) * torch.norm(X, dim=-1))
-        part2 = -torch.exp(torch.mean(torch.cos(c * X), dim=-1))
+        part2 = -(torch.exp(torch.mean(torch.cos(c * X), dim=-1)))
         return part1 + part2 + a + math.e
 
 
@@ -235,8 +235,8 @@ def __init__(
     def evaluate_true(self, X: Tensor) -> Tensor:
         part1 = torch.sum(X ** 2 / 4000.0, dim=1)
         d = X.shape[1]
-        part2 = -torch.prod(
-            torch.cos(X / torch.sqrt(X.new(range(1, d + 1))).view(1, -1))
+        part2 = -(
+            torch.prod(torch.cos(X / torch.sqrt(X.new(range(1, d + 1))).view(1, -1)))
         )
         return part1 + part2 + 1.0
 
@@ -325,7 +325,7 @@ def optimizers(self) -> Tensor:
     def evaluate_true(self, X: Tensor) -> Tensor:
         self.to(device=X.device, dtype=X.dtype)
         inner_sum = torch.sum(self.A * (X.unsqueeze(1) - 0.0001 * self.P) ** 2, dim=2)
-        H = -torch.sum(self.ALPHA * torch.exp(-inner_sum), dim=1)
+        H = -(torch.sum(self.ALPHA * torch.exp(-inner_sum), dim=1))
         if self.dim == 4:
             H = (1.1 + H) / 0.839
         return H
@@ -358,7 +358,9 @@ class HolderTable(SyntheticTestFunction):
 
     def evaluate_true(self, X: Tensor) -> Tensor:
         term = torch.abs(1 - torch.norm(X, dim=1) / math.pi)
-        return -torch.abs(torch.sin(X[..., 0]) * torch.cos(X[..., 1]) * torch.exp(term))
+        return -(
+            torch.abs(torch.sin(X[..., 0]) * torch.cos(X[..., 1]) * torch.exp(term))
+        )
 
 
 class Levy(SyntheticTestFunction):
@@ -431,8 +433,10 @@ def optimizers(self) -> Tensor:
     def evaluate_true(self, X: Tensor) -> Tensor:
         self.to(device=X.device, dtype=X.dtype)
         m = 10
-        return -torch.sum(
-            torch.sin(X) * torch.sin(self.i * X ** 2 / math.pi) ** (2 * m), dim=-1
+        return -(
+            torch.sum(
+                torch.sin(X) * torch.sin(self.i * X ** 2 / math.pi) ** (2 * m), dim=-1
+            )
         )
 
 

botorch/utils/constraints.py

@@ -18,7 +18,7 @@
 
 
 def get_outcome_constraint_transforms(
-    outcome_constraints: Optional[Tuple[Tensor, Tensor]],
+    outcome_constraints: Optional[Tuple[Tensor, Tensor]]
 ) -> Optional[List[Callable[[Tensor], Tensor]]]:
     r"""Create outcome constraint callables from outcome constraint tensors.
 

botorch/utils/transforms.py

@@ -227,7 +227,7 @@ def match_batch_shape(X: Tensor, Y: Tensor) -> Tensor:
         torch.Size([2, 6, 5, 3])
 
     """
-    return X.expand(X.shape[: -Y.dim()] + Y.shape[:-2] + X.shape[-2:])
+    return X.expand(X.shape[: -(Y.dim())] + Y.shape[:-2] + X.shape[-2:])
 
 
 def convert_to_target_pre_hook(module, *args):

scripts/validate_sphinx.py

@@ -96,7 +96,7 @@ def validate_complete_sphinx(path_to_botorch: str) -> None:
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description=("Validate that Sphinx documentation is complete.")
+        description="Validate that Sphinx documentation is complete."
     )
     parser.add_argument(
         "-p",

test/acquisition/test_active_learning.py

@@ -53,7 +53,7 @@ def test_q_neg_int_post_variance(self):
                     )
                     X = torch.empty(1, 1, device=self.device, dtype=dtype)  # dummy
                     val = qNIPV(X)
-                    self.assertTrue(torch.allclose(val, -variance.mean(), atol=1e-4))
+                    self.assertTrue(torch.allclose(val, -(variance.mean()), atol=1e-4))
             # batched model
             mean = torch.zeros(2, 4, 1, device=self.device, dtype=dtype)
             variance = torch.rand(2, 4, 1, device=self.device, dtype=dtype)