ruff updated on Python3.9

SMTorg · Apr 17, 2024 · a60a6cf · a60a6cf
1 parent a344037
commit a60a6cf
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Showing 5 changed files with 22 additions and 22 deletions.
diff --git a/smt/applications/mixed_integer.py b/smt/applications/mixed_integer.py
@@ -224,9 +224,9 @@ def __init__(
             )
             and self._surrogate.options["categorical_kernel"] is None
         ):
-            self._surrogate.options[
-                "categorical_kernel"
-            ] = MixIntKernelType.HOMO_HSPHERE
+            self._surrogate.options["categorical_kernel"] = (
+                MixIntKernelType.HOMO_HSPHERE
+            )
             warnings.warn(
                 "Using MixedIntegerSurrogateModel integer model with Continuous Relaxation is not supported. \
                     Switched to homoscedastic hypersphere kernel instead."

diff --git a/smt/surrogate_models/krg_based.py b/smt/surrogate_models/krg_based.py
@@ -720,16 +720,16 @@ def _matrix_data_corr(
                 d_cont = d[:, np.logical_not(cat_features)]
         if self.options["corr"] == "squar_sin_exp":
             if self.options["categorical_kernel"] != MixIntKernelType.GOWER:
-                theta_cont_features[
-                    -len([self.design_space.is_cat_mask]) :
-                ] = np.atleast_2d(
-                    np.array([True] * len([self.design_space.is_cat_mask]))
-                ).T
-                theta_cat_features[1][
-                    -len([self.design_space.is_cat_mask]) :
-                ] = np.atleast_2d(
-                    np.array([False] * len([self.design_space.is_cat_mask]))
-                ).T
+                theta_cont_features[-len([self.design_space.is_cat_mask]) :] = (
+                    np.atleast_2d(
+                        np.array([True] * len([self.design_space.is_cat_mask]))
+                    ).T
+                )
+                theta_cat_features[1][-len([self.design_space.is_cat_mask]) :] = (
+                    np.atleast_2d(
+                        np.array([False] * len([self.design_space.is_cat_mask]))
+                    ).T
+                )
 
         theta_cont = theta[theta_cont_features[:, 0]]
         r_cont = _correlation_types[corr](theta_cont, d_cont)

diff --git a/smt/surrogate_models/tests/test_surrogate_model_examples.py b/smt/surrogate_models/tests/test_surrogate_model_examples.py
@@ -818,9 +818,9 @@ def df_dx(x):
         genn.options["hidden_layer_sizes"] = [6, 6]
         genn.options["alpha"] = 0.1
         genn.options["lambd"] = 0.1
-        genn.options[
-            "gamma"
-        ] = 1.0  # 1 = gradient-enhanced on, 0 = gradient-enhanced off
+        genn.options["gamma"] = (
+            1.0  # 1 = gradient-enhanced on, 0 = gradient-enhanced off
+        )
         genn.options["num_iterations"] = 1000
         genn.options["is_backtracking"] = True
         genn.options["is_normalize"] = False

diff --git a/smt/utils/design_space.py b/smt/utils/design_space.py
@@ -526,9 +526,9 @@ def unfold_x(
 
                 # The is_acting matrix is simply repeated column-wise
                 if is_acting is not None:
-                    is_acting_unfolded[
-                        :, i_x_unfold : i_x_unfold + n_dim_cat
-                    ] = np.tile(is_acting[:, [i]], (1, n_dim_cat))
+                    is_acting_unfolded[:, i_x_unfold : i_x_unfold + n_dim_cat] = (
+                        np.tile(is_acting[:, [i]], (1, n_dim_cat))
+                    )
 
                 i_x_unfold += n_dim_cat
 

diff --git a/smt/utils/neural_net/model.py b/smt/utils/neural_net/model.py
@@ -221,9 +221,9 @@ def train(
 
                 # Compute average cost and print output
                 avg_cost = np.mean(optimizer.cost_history).squeeze()
-                self._training_history["epoch_" + str(e)][
-                    "batch_" + str(b)
-                ] = optimizer.cost_history
+                self._training_history["epoch_" + str(e)]["batch_" + str(b)] = (
+                    optimizer.cost_history
+                )
 
                 if not silent:
                     print(