Merge 4966d75 into 8fc434b

smt/applications/mfk.py

@@ -296,6 +296,9 @@ def _new_train_init(self):
             _, self.cat_features = compute_X_cont(
                 np.concatenate(xt, axis=0), self.design_space
             )
+            self.X_offset[self.cat_features] *= 0
+            self.X_scale[self.cat_features] *= 0
+            self.X_scale[self.cat_features] += 1
 
         nlevel = self.nlvl
 
@@ -761,7 +764,9 @@ def predict_variances_all_levels(self, X, is_acting=None):
         sigma2 = self.optimal_par[0]["sigma2"] / self.y_std**2
         MSE[:, 0] = sigma2 * (
             # 1 + self.optimal_noise_all[0] - (r_t ** 2).sum(axis=0) + (u_ ** 2).sum(axis=0)
-            1 - (r_t**2).sum(axis=0) + (u_**2).sum(axis=0)
+            1
+            - (r_t**2).sum(axis=0)
+            + (u_**2).sum(axis=0)
         )
 
         # Calculate recursively kriging variance at level i
@@ -845,16 +850,17 @@ def predict_variances_all_levels(self, X, is_acting=None):
                 Q_ = (np.dot((yt - np.dot(Ft, beta)).T, yt - np.dot(Ft, beta)))[0, 0]
                 MSE[:, i] = (
                     # sigma2_rho * MSE[:, i - 1]
-                    +Q_
-                    / (2 * (self.nt_all[i] - p - q))
+                    +Q_ / (2 * (self.nt_all[i] - p - q))
                     # * (1 + self.optimal_noise_all[i] - (r_t ** 2).sum(axis=0))
                     * (1 - (r_t**2).sum(axis=0))
                     + sigma2 * (u_**2).sum(axis=0)
                 )
             else:
                 MSE[:, i] = sigma2 * (
                     # 1 + self.optimal_noise_all[i] - (r_t ** 2).sum(axis=0) + (u_ ** 2).sum(axis=0)
-                    1 - (r_t**2).sum(axis=0) + (u_**2).sum(axis=0)
+                    1
+                    - (r_t**2).sum(axis=0)
+                    + (u_**2).sum(axis=0)
                 )  # + sigma2_rho * MSE[:, i - 1]
             if self.options["propagate_uncertainty"]:
                 MSE[:, i] = MSE[:, i] + sigma2_rho * MSE[:, i - 1]

smt/applications/mixed_integer.py

@@ -222,9 +222,9 @@ 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."

smt/applications/tests/test_mixed_integer.py

@@ -1316,26 +1316,26 @@ def test_hierarchical_variables_NN(self):
             sm.predict_values(
                 np.array(
                     [
-                        [0, -1, -2, 8, 0, 2, 0, 0],
-                        [1, -1, -2, 16, 1, 2, 1, 0],
-                        [2, -1, -2, 32, 2, 2, 1, -2],
+                        [0, -1, -2, 0, 0, 2, 0, 0],
+                        [1, -1, -2, 1, 1, 2, 1, 0],
+                        [2, -1, -2, 2, 2, 2, 1, -2],
                     ]
                 )
             )[:, 0],
             sm.predict_values(
                 np.array(
                     [
-                        [0, -1, -2, 8, 0, 2, 10, 10],
-                        [1, -1, -2, 16, 1, 2, 1, 10],
-                        [2, -1, -2, 32, 2, 2, 1, -2],
+                        [0, -1, -2, 0, 0, 2, 10, 10],
+                        [1, -1, -2, 1, 1, 2, 1, 10],
+                        [2, -1, -2, 2, 2, 2, 1, -2],
                     ]
                 )
             )[:, 0],
         )
         self.assertTrue(
             np.linalg.norm(
-                sm.predict_values(np.array([[0, -1, -2, 8, 0, 2, 0, 0]]))
-                - sm.predict_values(np.array([[0, -1, -2, 8, 0, 12, 10, 10]]))
+                sm.predict_values(np.array([[0, -1, -2, 0, 0, 2, 0, 0]]))
+                - sm.predict_values(np.array([[0, -1, -2, 0, 0, 12, 10, 10]]))
             )
             > 1e-8
         )
@@ -1789,7 +1789,7 @@ def test_compound_hetero_noise_auto(self):
         )
 
     def test_mixed_homo_gaussian_3D_ord_cate(self):
-        xt = np.array([[0.5, 0, 5], [2, 3, 4], [5, 2, -1], [-2, 4, 0.5]])
+        xt = np.array([[0, 5, 0], [2, 4, 3], [4, -1, 2], [2, 0.5, 1]])
         yt = np.array([[0.0], [3], [1.0], [1.5]])
         design_space = DesignSpace(
             [

smt/surrogate_models/genn.py

@@ -200,9 +200,11 @@ def _train(self):
             beta1=self.options["beta1"],
             beta2=self.options["beta2"],
             epochs=self.options["num_epochs"],
-            batch_size=None
-            if self.options["mini_batch_size"] < 0
-            else self.options["mini_batch_size"],
+            batch_size=(
+                None
+                if self.options["mini_batch_size"] < 0
+                else self.options["mini_batch_size"]
+            ),
             max_iter=self.options["num_iterations"],
             is_backtracking=self.options["is_backtracking"],
             is_verbose=self.options["is_print"],

smt/surrogate_models/krg_based.py

@@ -718,16 +718,16 @@ 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)

smt/surrogate_models/tests/test_surrogate_model_examples.py

@@ -322,7 +322,7 @@ def test_mixed_gower_krg(self):
         sm.train()
 
         # DOE for validation
-        x = np.linspace(0, 5, 5)
+        x = np.linspace(0, 4, 5)
         y = sm.predict_values(x)
 
         plt.plot(xt, yt, "o", label="data")
@@ -725,9 +725,9 @@ 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