Merge branch 'development' into fix/1179

CHANGELOG.md

@@ -7,6 +7,7 @@
 
 ## Bugfixes
 - Fix bug with HyperBand setup given a total budget (#1179)
+- Fix kwargs for DifferentialEvolution (#1187)
 
 # 2.2.1
 

smac/acquisition/maximizer/differential_evolution.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import inspect
+
 import numpy as np
 from ConfigSpace import Configuration, ConfigurationSpace
 from scipy.optimize._differentialevolution import DifferentialEvolutionSolver
@@ -14,6 +16,30 @@
 __license__ = "3-clause BSD"
 
 
+def check_kwarg(cls: type, kwarg_name: str) -> bool:
+    """
+    Checks if a given class accepts a specific keyword argument in its __init__ method.
+
+    Parameters
+    ----------
+        cls (type): The class to inspect.
+        kwarg_name (str): The name of the keyword argument to check.
+
+    Returns
+    -------
+        bool: True if the class's __init__ method accepts the keyword argument,
+              otherwise False.
+    """
+    # Get the signature of the class's __init__ method
+    init_signature = inspect.signature(cls.__init__)  # type: ignore[misc]
+
+    # Check if the kwarg_name is present in the signature as a parameter
+    for param in init_signature.parameters.values():
+        if param.name == kwarg_name and param.default != inspect.Parameter.empty:
+            return True  # It accepts the kwarg
+    return False  # It does not accept the kwarg
+
+
 class DifferentialEvolution(AbstractAcquisitionMaximizer):
     """Get candidate solutions via `DifferentialEvolutionSolvers` from scipy.
 
@@ -92,6 +118,11 @@ def func(x: np.ndarray) -> np.ndarray:
                 transform_continuous_designs(design=x.T, origin="Diffrential Evolution", configspace=self._configspace)
             )
 
+        accepts_seed = check_kwarg(DifferentialEvolutionSolver, "seed")
+        if accepts_seed:
+            kwargs = {"seed": self._rng.randint(1000)}
+        else:
+            kwargs = {"rng": self._rng.randint(1000)}
         ds = DifferentialEvolutionSolver(
             func,
             bounds=[[0, 1] for _ in range(len(self._configspace))],
@@ -102,13 +133,13 @@ def func(x: np.ndarray) -> np.ndarray:
             tol=0.01,
             mutation=self.mutation,
             recombination=self.recombination,
-            seed=self._rng.randint(1000),
             polish=self.polish,
             callback=None,
             disp=False,
             init="latinhypercube",
             atol=0,
             vectorized=True,
+            **kwargs,
         )
 
         _ = ds.solve()