fea: apply flake8 fixes, update test to account for f"{xyz=}" != f"{x…

…yz = }"

botorch/acquisition/analytic.py

@@ -982,7 +982,7 @@ def _log_ei_helper(u: Tensor) -> Tensor:
     if not (u.dtype == torch.float32 or u.dtype == torch.float64):
         raise TypeError(
             f"LogExpectedImprovement only supports torch.float32 and torch.float64 "
-            f"dtypes, but received {u.dtype = }."
+            f"dtypes, but received {u.dtype=}."
         )
     # The function has two branching decisions. The first is u < bound, and in this
     # case, just taking the logarithm of the naive _ei_helper implementation works.

botorch/acquisition/logei.py

@@ -540,7 +540,7 @@ def check_tau(tau: FloatOrTensor, name: str) -> FloatOrTensor:
     """Checks the validity of the tau arguments of the functions below, and returns
     `tau` if it is valid."""
     if isinstance(tau, Tensor) and tau.numel() != 1:
-        raise ValueError(name + f" is not a scalar: {tau.numel() = }.")
+        raise ValueError(f"{name} is not a scalar: {tau.numel()=}.")
     if not (tau > 0):
-        raise ValueError(name + f" is non-positive: {tau = }.")
+        raise ValueError(f"{name} is non-positive: {tau=}.")
     return tau

botorch/acquisition/multi_objective/hypervolume_knowledge_gradient.py

@@ -557,7 +557,7 @@ def _split_hvkg_fantasy_points(
     """
     if n_f * num_pareto > X.size(-2):
         raise ValueError(
-            f"`n_f*num_pareto` ({n_f*num_pareto}) must be less than"
+            f"`n_f*num_pareto` ({n_f * num_pareto}) must be less than"
             f" the `q`-batch dimension of `X` ({X.size(-2)})."
         )
     split_sizes = [X.size(-2) - n_f * num_pareto, n_f * num_pareto]

botorch/optim/optimize.py

@@ -248,7 +248,7 @@ def _optimize_acqf_sequential_q(
             if base_X_pending is not None
             else candidates
         )
-        logger.info(f"Generated sequential candidate {i+1} of {opt_inputs.q}")
+        logger.info(f"Generated sequential candidate {i + 1} of {opt_inputs.q}")
     opt_inputs.acq_function.set_X_pending(base_X_pending)
     return candidates, torch.stack(acq_value_list)
 
@@ -325,7 +325,7 @@ def _optimize_batch_candidates() -> tuple[Tensor, Tensor, list[Warning]]:
             opt_warnings += ws
             batch_candidates_list.append(batch_candidates_curr)
             batch_acq_values_list.append(batch_acq_values_curr)
-            logger.info(f"Generated candidate batch {i+1} of {len(batched_ics)}.")
+            logger.info(f"Generated candidate batch {i + 1} of {len(batched_ics)}.")
 
         batch_candidates = torch.cat(batch_candidates_list)
         has_scalars = batch_acq_values_list[0].ndim == 0

botorch/utils/probability/utils.py

@@ -161,7 +161,7 @@ def log_ndtr(x: Tensor) -> Tensor:
     if not (x.dtype == torch.float32 or x.dtype == torch.float64):
         raise TypeError(
             f"log_Phi only supports torch.float32 and torch.float64 "
-            f"dtypes, but received {x.dtype = }."
+            f"dtypes, but received {x.dtype=}."
         )
     neg_inv_sqrt_2, log_2 = get_constants_like((_neg_inv_sqrt_2, _log_2), x)
     return log_erfc(neg_inv_sqrt_2 * x) - log_2
@@ -181,7 +181,7 @@ def log_erfc(x: Tensor) -> Tensor:
     if not (x.dtype == torch.float32 or x.dtype == torch.float64):
         raise TypeError(
             f"log_erfc only supports torch.float32 and torch.float64 "
-            f"dtypes, but received {x.dtype = }."
+            f"dtypes, but received {x.dtype=}."
         )
     is_pos = x > 0
     x_pos = x.masked_fill(~is_pos, 0)

test/acquisition/multi_objective/test_hypervolume_knowledge_gradient.py

@@ -436,7 +436,7 @@ def test_split_hvkg_fantasy_points(self):
         n_f = 100
         num_pareto = 3
         msg = (
-            rf".*\({n_f*num_pareto}\) must be less than"
+            rf".*\({n_f * num_pareto}\) must be less than"
             rf" the `q`-batch dimension of `X` \({X.size(-2)}\)\."
         )
         with self.assertRaisesRegex(ValueError, msg):

test/utils/probability/test_utils.py

@@ -320,7 +320,7 @@ def test_gaussian_probabilities(self) -> None:
 
         float16_msg = (
             "only supports torch.float32 and torch.float64 dtypes, but received "
-            "x.dtype = torch.float16."
+            "x.dtype=torch.float16."
         )
         with self.assertRaisesRegex(TypeError, expected_regex=float16_msg):
             log_erfc(torch.tensor(1.0, dtype=torch.float16, device=self.device))