diff --git a/smt/applications/tests/test_mixed_integer.py b/smt/applications/tests/test_mixed_integer.py
index c38900e66..884a109d4 100644
--- a/smt/applications/tests/test_mixed_integer.py
+++ b/smt/applications/tests/test_mixed_integer.py
@@ -1040,9 +1040,9 @@ def test_hierarchical_design_space_example_all_categorical_decreed(self):
         for mixint_kernel in [
             MixIntKernelType.CONT_RELAX,
             MixIntKernelType.GOWER,
-    #        MixIntKernelType.COMPOUND_SYMMETRY,
-     #       MixIntKernelType.EXP_HOMO_HSPHERE,
-      #      MixIntKernelType.HOMO_HSPHERE,
+            #        MixIntKernelType.COMPOUND_SYMMETRY,
+            #       MixIntKernelType.EXP_HOMO_HSPHERE,
+            #      MixIntKernelType.HOMO_HSPHERE,
         ]:
             sm = MixedIntegerKrigingModel(
                 surrogate=KRG(
@@ -1059,7 +1059,7 @@ def test_hierarchical_design_space_example_all_categorical_decreed(self):
             sm.train()
             y_s = sm.predict_values(Xt)[:, 0]
             _pred_RMSE = np.linalg.norm(y_s - Yt) / len(Yt)
-            self.assertLess(_pred_RMSE, 1e-4)
+            self.assertLess(_pred_RMSE, 1e-6)
             y_sv = sm.predict_variances(Xt)[:, 0]
             _var_RMSE = np.linalg.norm(y_sv) / len(Yt)
             self.assertLess(_var_RMSE, 1e-6)
diff --git a/smt/surrogate_models/krg_based.py b/smt/surrogate_models/krg_based.py
index f96a0d259..cf56906bd 100644
--- a/smt/surrogate_models/krg_based.py
+++ b/smt/surrogate_models/krg_based.py
@@ -986,7 +986,7 @@ def _reduced_likelihood_function(self, theta):
                             X2 - self.X2_offset
                         ) / self.X2_scale
                         dx, _ = cross_distances(self.X2_norma[str(self._lvl)])
-              
+
             try:
                 r = self._matrix_data_corr(
                     corr=self.options["corr"],
@@ -1001,7 +1001,7 @@ def _reduced_likelihood_function(self, theta):
                     cat_kernel=self.options["categorical_kernel"],
                     kplsk_second_loop=self.kplsk_second_loop,
                 ).reshape(-1, 1)
-                
+
                 if np.isnan(r).any():
                     return reduced_likelihood_function_value, par
             except FloatingPointError:
@@ -1590,7 +1590,7 @@ def _predict_init(self, x, is_acting):
                         )
                 if np.any(self.design_space.is_conditionally_acting):
                     dx[:, np.logical_not(self.unfolded_cat)] = (
-                        dnum  / self.X2_scale[np.logical_not(self.unfolded_cat)]
+                        dnum / self.X2_scale[np.logical_not(self.unfolded_cat)]
                     )
             Lij, _ = cross_levels(
                 X=x, ij=ij, design_space=self.design_space, y=self.X_train
diff --git a/smt/utils/kriging.py b/smt/utils/kriging.py
index f4bba9892..ad2ce14db 100644
--- a/smt/utils/kriging.py
+++ b/smt/utils/kriging.py
@@ -302,8 +302,8 @@ def gower_componentwise_distances(
     Z_num = Z[:, ~cat_features]
     z_num_is_acting = z_is_acting[:, ~cat_features]
     num_is_decreed = is_decreed[~cat_features]
-    X_num_out = Z_num[x_index,]
-    Y_num_out = Z_num[y_index,]
+    Z_num[x_index,]
+    Z_num[y_index,]
     num_bounds = design_space.get_num_bounds()[~cat_features, :]
     if num_bounds.shape[0] > 0:
         Z_offset = num_bounds[:, 0]
@@ -327,7 +327,7 @@ def gower_componentwise_distances(
         y,
         hierarchical_kernel,
     )
-    D_num_out= D_num*Z_scale
+    D_num_out = D_num * Z_scale
     D = np.concatenate((D_cat, D_num), axis=1) * 0
     D[:, np.logical_not(cat_features)] = D_num
     D[:, cat_features] = D_cat