fix for gekpls

smt/applications/mfkpls.py

@@ -34,6 +34,13 @@ def _initialize(self):
             desc="Correlation function type",
             types=(str),
         )
+        declare(
+            "hyper_opt",
+            "Cobyla",
+            values=("Cobyla"),
+            desc="Optimiser for hyperparameters optimisation",
+            types=str,
+        )
         declare("n_comp", 1, types=int, desc="Number of principal components")
         self.name = "MFKPLS"
 

smt/applications/mfkplsk.py

@@ -26,6 +26,13 @@ def _initialize(self):
             desc="Correlation function type",
             types=(str),
         )
+        declare(
+            "hyper_opt",
+            "Cobyla",
+            values=("Cobyla"),
+            desc="Optimiser for hyperparameters optimisation",
+            types=str,
+        )
         self.name = "MFKPLSK"
 
     def _componentwise_distance(self, dx, opt=0):

smt/applications/mixed_integer.py

@@ -97,7 +97,6 @@ def __init__(
         """
         super().__init__()
         self._surrogate = surrogate
-        self._surrogate.options["hyper_opt"] = "Cobyla"
         if isinstance(self._surrogate, KrgBased):
             raise ValueError(
                 "Using MixedIntegerSurrogateModel integer model with "
@@ -109,7 +108,6 @@ def __init__(
         self._input_in_folded_space = input_in_folded_space
         self.supports = self._surrogate.supports
         self.options["print_global"] = False
-        self.options["hyper_opt"] = "Cobyla"
         if "poly" in self._surrogate.options:
             if self._surrogate.options["poly"] != "constant":
                 raise ValueError("constant regression must be used with mixed integer")
@@ -198,6 +196,7 @@ def __init__(
                 + " is not supported. Please use MixedIntegerSurrogateModel instead."
             )
         self.options["design_space"] = self._surrogate.design_space
+        self._surrogate.options["hyper_opt"] = "Cobyla"
 
         self._input_in_folded_space = input_in_folded_space
         self.supports = self._surrogate.supports

smt/surrogate_models/gekpls.py

@@ -32,6 +32,13 @@ def _initialize(self):
             types=int,
             desc="Number of extra points per training point",
         )
+        declare(
+            "hyper_opt",
+            "Cobyla",
+            values=("Cobyla"),
+            desc="Optimiser for hyperparameters optimisation",
+            types=str,
+        )
         self.supports["training_derivatives"] = True
 
     def _check_param(self):

smt/surrogate_models/krg_based.py

@@ -1908,6 +1908,10 @@ def grad_minus_reduced_likelihood_function(log10t):
                                 optimal_theta_res = optimal_theta_res_loop
 
                     elif self.options["hyper_opt"] == "TNC":
+                        if self.options["use_het_noise"]:
+                            raise ValueError(
+                                "For heteroscedastic noise, please use Cobyla"
+                            )
                         theta_all_loops = 10**theta_all_loops
                         for theta0_loop in theta_all_loops:
                             optimal_theta_res_loop = optimize.minimize(
@@ -1916,7 +1920,7 @@ def grad_minus_reduced_likelihood_function(log10t):
                                 method="TNC",
                                 jac=grad_minus_reduced_likelihood_function,
                                 bounds=bounds_hyp,
-                                options={"maxiter": 100},
+                                options={"maxfun": 2 * limit},
                             )
                             if optimal_theta_res_loop["fun"] < optimal_theta_res["fun"]:
                                 optimal_theta_res = optimal_theta_res_loop