formatting

xopt/generators/bayesian/expected_improvement.py

@@ -52,9 +52,7 @@ def _get_best_f(self, data, objective):
         """get best function value for EI based on the objective"""
         if isinstance(objective, CustomXoptObjective):
             best_f = objective(
-                torch.tensor(
-                    self.vocs.observable_data(data).to_numpy(), **self.tkwargs
-                )
+                torch.tensor(self.vocs.observable_data(data).to_numpy(), **self.tkwargs)
             ).max()
         else:
             # analytic acquisition function for single candidate generation

xopt/generators/bayesian/turbo.py

@@ -104,7 +104,8 @@ def get_trust_region(self, generator) -> Tensor:
 
             # Scale the TR to be proportional to the lengthscales of the objective model
             x_center = torch.tensor(
-                [self.center_x[ele] for ele in self.vocs.variable_names], **generator.tkwargs
+                [self.center_x[ele] for ele in self.vocs.variable_names],
+                **generator.tkwargs,
             ).unsqueeze(dim=0)
 
             # default weights are 1 (if there is no model or a model without

xopt/tests/generators/bayesian/test_turbo.py

@@ -351,8 +351,12 @@ def test_serialization(self):
         evaluator = Evaluator(function=sin_function)
         for name in ["optimize", "safety"]:
             generator = UpperConfidenceBoundGenerator(vocs=vocs, turbo_controller=name)
-            X = Xopt(evaluator=evaluator, generator=generator, vocs=vocs,
-                     dump_file="dump.yml")
+            X = Xopt(
+                evaluator=evaluator,
+                generator=generator,
+                vocs=vocs,
+                dump_file="dump.yml",
+            )
 
             yaml_str = X.yaml()
             X2 = Xopt.from_yaml(yaml_str)