fix seeding

smt/applications/ego.py

@@ -14,7 +14,10 @@
 
 from smt.surrogate_models import KPLS, KRG, KPLSK, MGP, GEKPLS
 from smt.applications.application import SurrogateBasedApplication
-from smt.applications.mixed_integer import MixedIntegerContext
+from smt.applications.mixed_integer import (
+    MixedIntegerContext,
+    MixedIntegerSamplingMethod,
+)
 from smt.utils.design_space import (
     BaseDesignSpace,
     DesignSpace,
@@ -271,10 +274,14 @@ def _setup_optimizer(self, fun):
 
         else:
             self.mixint = None
-            self._sampling = lambda n: self.design_space.sample_valid_x(
-                n,
+            sampling = MixedIntegerSamplingMethod(
+                LHS,
+                self.design_space,
+                criterion="ese",
                 random_state=self.options["random_state"],
-            )[0]
+            )
+            self._sampling = lambda n: sampling(n)
+
             self.categorical_kernel = None
 
         # Build DOE

smt/applications/mixed_integer.py

@@ -44,9 +44,9 @@ def __init__(self, sampling_method_class, design_space, **kwargs):
         )
         self._design_space = design_space
         if "random_state" in kwargs:
-            self._design_space.seed = kwargs["random_state"]
-        elif self._design_space.seed is None:
-            self._design_space.seed = 42
+            self._design_space.random_state = kwargs["random_state"]
+        elif self._design_space.random_state is None:
+            self._design_space.random_state = 42
         self._unfolded_xlimits = design_space.get_unfolded_num_bounds()
         self._output_in_folded_space = kwargs.get("output_in_folded_space", True)
         kwargs.pop("output_in_folded_space", None)
@@ -60,7 +60,7 @@ def _compute(self, nt, return_is_acting=False):
         x_doe, is_acting = self._design_space.sample_valid_x(
             nt,
             unfolded=not self._output_in_folded_space,
-            random_state=self._design_space.seed,
+            random_state=self._design_space.random_state,
         )
         if return_is_acting:
             return x_doe, is_acting

smt/applications/tests/test_ego.py

@@ -121,7 +121,7 @@ def test_rosenbrock_2D(self):
         xlimits = fun.xlimits
         criterion = "LCB"  #'EI' or 'SBO' or 'LCB'
         random_state = 42
-        design_space = DesignSpace(xlimits, seed=random_state)
+        design_space = DesignSpace(xlimits, random_state=random_state)
         xdoe = FullFactorial(xlimits=xlimits)(10)
         ego = EGO(
             n_start=30,
@@ -164,7 +164,7 @@ def test_rosenbrock_2D_parallel(self):
         xlimits = fun.xlimits
         criterion = "LCB"  #'EI' or 'SBO' or 'LCB'
         random_state = 42
-        design_space = DesignSpace(xlimits, seed=random_state)
+        design_space = DesignSpace(xlimits, random_state=random_state)
 
         xdoe = FullFactorial(xlimits=xlimits)(10)
         qEI = "KB"
@@ -250,7 +250,7 @@ def test_branin_2D_mixed_parallel(self):
                 IntegerVariable(*xlimits[0]),
                 FloatVariable(*xlimits[1]),
             ],
-            seed=random_state,
+            random_state=random_state,
         )
         sm = KRG(design_space=design_space, print_global=False, n_start=25)
         mixint = MixedIntegerContext(design_space)
@@ -289,7 +289,7 @@ def test_branin_2D_mixed(self):
                 IntegerVariable(*xlimits[0]),
                 FloatVariable(*xlimits[1]),
             ],
-            seed=random_state,
+            random_state=random_state,
         )
         criterion = "EI"  #'EI' or 'SBO' or 'LCB'
 
@@ -326,7 +326,7 @@ def test_branin_2D_mixed_tunnel(self):
                 IntegerVariable(*xlimits[0]),
                 FloatVariable(*xlimits[1]),
             ],
-            seed=random_state,
+            random_state=random_state,
         )
         criterion = "EI"  #'EI' or 'SBO' or 'LCB'
 
@@ -388,10 +388,10 @@ def test_ego_mixed_integer(self):
                 CategoricalVariable(["large", "small"]),
                 OrdinalVariable([0, 2, 3]),
             ],
-            seed=42,
+            random_state=42,
         )
         samp = MixedIntegerSamplingMethod(
-            LHS, design_space, criterion="ese", random_state=design_space.seed
+            LHS, design_space, criterion="ese", random_state=design_space.random_state
         )
         xdoe = samp(n_doe)
 
@@ -402,7 +402,7 @@ def test_ego_mixed_integer(self):
             xdoe=xdoe,
             surrogate=KRG(design_space=design_space, print_global=False),
             enable_tunneling=False,
-            random_state=design_space.seed,
+            random_state=design_space.random_state,
         )
         _, y_opt, _, _, _ = ego.optimize(fun=TestEGO.function_test_mixed_integer)
 
@@ -420,10 +420,10 @@ def test_ego_mixed_integer_gower_distance(self):
                 CategoricalVariable(["large", "small"]),
                 IntegerVariable(0, 2),
             ],
-            seed=random_state,
+            random_state=random_state,
         )
         samp = MixedIntegerSamplingMethod(
-            LHS, design_space, criterion="ese", random_state=design_space.seed
+            LHS, design_space, criterion="ese", random_state=design_space.random_state
         )
         xdoe = samp(n_doe)
 
@@ -440,7 +440,7 @@ def test_ego_mixed_integer_gower_distance(self):
                 print_global=False,
             ),
             enable_tunneling=False,
-            random_state=design_space.seed,
+            random_state=design_space.random_state,
         )
         _, y_opt, _, _, _ = ego.optimize(fun=TestEGO.function_test_mixed_integer)
 
@@ -495,7 +495,7 @@ def f_hv(X):
                 IntegerVariable(0, 5),  # x6
                 IntegerVariable(0, 5),  # x7
             ],
-            seed=random_state,
+            random_state=random_state,
         )
 
         # x6 is active when x0 >= 2
@@ -686,7 +686,7 @@ def f_hv(X):
                 IntegerVariable(0, 2),
                 IntegerVariable(0, 2),
             ],
-            seed=random_state,
+            random_state=random_state,
         )
 
         # x4 is acting if meta == 1, 3
@@ -700,7 +700,7 @@ def f_hv(X):
 
         n_doe = 25
         samp = MixedIntegerSamplingMethod(
-            LHS, ds, criterion="ese", random_state=ds.seed
+            LHS, ds, criterion="ese", random_state=ds.random_state
         )
         Xt, x_is_active = samp(n_doe, return_is_acting=True)
 
@@ -742,7 +742,7 @@ def test_ego_mixed_integer_homo_gaussian(self):
                 CategoricalVariable(["large", "small"]),
                 IntegerVariable(0, 2),
             ],
-            seed=random_state,
+            random_state=random_state,
         )
         n_doe = 5
         sampling = MixedIntegerSamplingMethod(
@@ -782,7 +782,7 @@ def test_ego_mixed_integer_homo_gaussian_pls(self):
                 CategoricalVariable(["large", "small"]),
                 IntegerVariable(0, 2),
             ],
-            seed=random_state,
+            random_state=random_state,
         )
         sampling = MixedIntegerSamplingMethod(
             LHS,
@@ -819,7 +819,7 @@ def test_ydoe_option(self):
         xlimits = fun.xlimits
         criterion = "LCB"  #'EI' or 'SBO' or 'LCB'
         random_state = 42
-        design_space = DesignSpace(xlimits, seed=random_state)
+        design_space = DesignSpace(xlimits, random_state=random_state)
         xdoe = FullFactorial(xlimits=xlimits)(10)
         ydoe = fun(xdoe)
         ego = EGO(
@@ -838,7 +838,7 @@ def test_find_best_point(self):
         fun = TestEGO.function_test_1d
         xlimits = np.array([[0.0, 25.0]])
         random_state = 42
-        design_space = DesignSpace(xlimits, seed=random_state)
+        design_space = DesignSpace(xlimits, random_state=random_state)
         xdoe = FullFactorial(xlimits=xlimits)(3)
         ydoe = fun(xdoe)
         ego = EGO(
@@ -874,7 +874,7 @@ def _evaluate(self, x, kx):
 
         fun = TensorProductIndirect(ndim=2, func=func)
         random_state = 42
-        design_space = DesignSpace(fun.xlimits, seed=42)
+        design_space = DesignSpace(fun.xlimits, random_state=42)
 
         # Construction of the DOE
         sampling = LHS(xlimits=fun.xlimits, criterion="m", random_state=random_state)
@@ -906,7 +906,7 @@ def _evaluate(self, x, kx):
 
         return ego, fun
 
-    def test_ego_seeding(self):
+    def test_ego_random_stateing(self):
         def f_obj(X):
             """
             s01 objective
@@ -983,7 +983,7 @@ def f_obj(X):
         )
 
         # To define the initial DOE
-        random_state = 42  # seed value for the sampling
+        random_state = 42  # random_state value for the sampling
         n_doe = 5  # initial doe size
         sampling = MixedIntegerSamplingMethod(
             LHS, design_space, criterion="ese", random_state=random_state
@@ -1039,7 +1039,7 @@ def test_qei_criterion_default(self):
         fun = TestEGO.function_test_1d
         xlimits = np.array([[0.0, 25.0]])
         random_state = 42
-        design_space = DesignSpace(xlimits, seed=random_state)
+        design_space = DesignSpace(xlimits, random_state=random_state)
         xdoe = FullFactorial(xlimits=xlimits)(3)
         ydoe = fun(xdoe)
         ego = EGO(
@@ -1094,7 +1094,7 @@ def function_test_1d(x):
         xlimits = np.array([[0.0, 25.0]])
 
         random_state = 42  # for reproducibility
-        design_space = DesignSpace(xlimits, seed=random_state)
+        design_space = DesignSpace(xlimits, random_state=random_state)
         xdoe = np.atleast_2d([0, 7, 25]).T
         n_doe = xdoe.size
 
@@ -1213,7 +1213,7 @@ def function_test_mixed_integer(X):
                 CategoricalVariable(["square", "circle"]),
                 IntegerVariable(0, 2),
             ],
-            seed=random_state,
+            random_state=random_state,
         )
 
         criterion = "EI"  #'EI' or 'SBO' or 'LCB'
@@ -1284,7 +1284,7 @@ def function_test_1d(x):
         xlimits = np.array([[0.0, 25.0]])
 
         random_state = 42
-        design_space = DesignSpace(xlimits, seed=random_state)
+        design_space = DesignSpace(xlimits, random_state=random_state)
         xdoe = np.atleast_2d([0, 7, 25]).T
         n_doe = xdoe.size
 

smt/utils/design_space.py

@@ -696,7 +696,9 @@ class DesignSpace(BaseDesignSpace):
     """
 
     def __init__(
-        self, design_variables: Union[List[DesignVariable], list, np.ndarray], seed=None
+        self,
+        design_variables: Union[List[DesignVariable], list, np.ndarray],
+        random_state=None,
     ):
         self.sampler = None
 
@@ -721,7 +723,7 @@ def _is_num(val):
                 converted_dvs.append(FloatVariable(bounds[0], bounds[1]))
             design_variables = converted_dvs
 
-        self.seed = seed  # For testing
+        self.random_state = random_state  # For testing
 
         self._cs = None
         if HAS_CONFIG_SPACE:
@@ -742,6 +744,11 @@ def _is_num(val):
                     cs_vars[name] = CategoricalHyperparameter(name, choices=dv.values)
                 else:
                     raise ValueError(f"Unknown variable type: {dv!r}")
+            seed = None
+            if isinstance(random_state, int):
+                seed = random_state
+            elif isinstance(random_state, np.random.RandomState):
+                seed = random_state.get_state()[1][0]
 
             self._cs = NoDefaultConfigurationSpace(space=cs_vars, seed=seed)
 
@@ -927,11 +934,18 @@ def _sample_valid_x(
         """Sample design vectors"""
         # Simplified implementation: sample design vectors in unfolded space
         x_limits_unfolded = self.get_unfolded_num_bounds()
-        if self.seed is None:
-            self.seed = random_state
+        if self.random_state is None:
+            self.random_state = random_state
 
         if self._cs is not None:
             # Sample Configuration objects
+            if not (hasattr(self, "seed")):
+                seed = None
+                if isinstance(random_state, int):
+                    seed = random_state
+                elif isinstance(random_state, np.random.RandomState):
+                    seed = random_state.get_state()[1][0]
+                self.seed = seed
             self._cs.seed(self.seed)
             if self.seed is not None:
                 self.seed += 1
@@ -984,6 +998,14 @@ def _get_correct_config(self, vector: np.ndarray) -> Configuration:
                 # At this point, the parameter active statuses are set correctly, so we only need to correct the
                 # configuration to one that does not violate the forbidden clauses
                 elif isinstance(e, ForbiddenValueError):
+                    if not (hasattr(self, "seed")):
+                        seed = None
+                        if isinstance(self.random_state, int):
+                            seed = self.random_state
+                        elif isinstance(random_state, np.random.RandomState):
+                            seed = self.random_state.get_state()[1][0]
+                        self.seed = seed
+
                     return get_random_neighbor(config, seed=self.seed)
 
                 else:

smt/utils/test/test_design_space.py

@@ -198,7 +198,7 @@ def test_design_space(self):
                 IntegerVariable(-1, 2),
                 FloatVariable(0.5, 1.5),
             ],
-            seed=42,
+            random_state=42,
         )
         self.assertEqual(len(ds.design_variables), 4)
         if HAS_CONFIG_SPACE:
@@ -340,7 +340,7 @@ def test_design_space_hierarchical(self):
                 IntegerVariable(0, 1),  # x2
                 FloatVariable(0, 1),  # x3
             ],
-            seed=42,
+            random_state=42,
         )
         ds.declare_decreed_var(
             decreed_var=3, meta_var=0, meta_value="A"
@@ -434,7 +434,7 @@ def test_design_space_hierarchical_config_space(self):
                 IntegerVariable(0, 1),  # x2
                 FloatVariable(0, 1),  # x3
             ],
-            seed=42,
+            random_state=42,
         )
         ds.declare_decreed_var(
             decreed_var=3, meta_var=0, meta_value="A"
@@ -530,7 +530,7 @@ def _is_conditionally_acting(self) -> np.ndarray:
                         IntegerVariable(0, 1),  # x2
                         FloatVariable(0, 1),  # x3
                     ],
-                    seed=42,
+                    random_state=42,
                 )
                 ds.declare_decreed_var(
                     decreed_var=3, meta_var=0, meta_value="A"
@@ -550,7 +550,7 @@ def test_check_conditionally_acting_2(self):
                         IntegerVariable(0, 1),  # x2
                         FloatVariable(0, 1),  # x3
                     ],
-                    seed=42,
+                    random_state=42,
                 )
                 ds.declare_decreed_var(
                     decreed_var=0, meta_var=1, meta_value="E"