Vectorize diff evo

smac/acquisition/maximizer/differential_evolution.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 
 import numpy as np
-from ConfigSpace import Configuration
+from ConfigSpace import Configuration, ConfigurationSpace
 from scipy.optimize._differentialevolution import DifferentialEvolutionSolver
 
+from smac.acquisition.function.abstract_acquisition_function import AbstractAcquisitionFunction
 from smac.acquisition.maximizer import AbstractAcquisitionMaximizer
 from smac.utils.configspace import transform_continuous_designs
 
@@ -24,7 +25,42 @@ class DifferentialEvolution(AbstractAcquisitionMaximizer):
 
     [1] Storn, R and Price, K, Differential Evolution - a Simple and Efficient Heuristic for Global
      Optimization over Continuous Spaces, Journal of Global Optimization, 1997, 11, 341 - 359.
+
+     Parameters
+    ----------
+    configspace : ConfigurationSpace
+    acquisition_function : AbstractAcquisitionFunction
+    challengers : int, defaults to 50000
+        Number of challengers.
+    max_iter: int | None, defaults to None
+        Maximum number of iterations that the DE will perform.
+    strategy: str, defaults to "best1bin"
+        The strategy to use for the DE.
+    polish: bool, defaults to True
+        Whether to polish the final solution using L-BFGS-B.
+    mutation: tuple[float, float], defaults to (0.5, 1.0)
+        The mutation constant.
+    recombination: float, defaults to 0.7
+        The recombination constant.
+    seed : int, defaults to 0
     """
+    def __init__(self, 
+                 configspace: ConfigurationSpace, 
+                 acquisition_function: AbstractAcquisitionFunction | None = None, 
+                 max_iter: int = 1000,
+                 challengers: int = 50000, 
+                 strategy: str = "best1bin",
+                 polish: bool = True,
+                 mutation: tuple[float, float] = (0.5, 1.0),
+                 recombination: float =0.7,
+                 seed: int = 0):
+        super().__init__(configspace, acquisition_function, challengers, seed)
+        # raise NotImplementedError("DifferentialEvolution is not yet implemented.")
+        self.max_iter = max_iter
+        self.strategy = strategy
+        self.polish = polish
+        self.mutation = mutation
+        self.recombination = recombination
 
     def _maximize(
         self,
@@ -37,26 +73,31 @@ def _maximize(
 
         def func(x: np.ndarray) -> np.ndarray:
             assert self._acquisition_function is not None
-            return -self._acquisition_function([transform_continuous_designs(
-                design=np.expand_dims(x, axis=0), origin="Diffrential Evolution", configspace=self._configspace
-                )[0]])
+            if len(x.shape) == 1:
+                return -self._acquisition_function([transform_continuous_designs(
+                 design=np.expand_dims(x, axis=0), origin="Diffrential Evolution", configspace=self._configspace
+                 )[0]])
+            return -self._acquisition_function(transform_continuous_designs(
+                design=x.T, origin="Diffrential Evolution", configspace=self._configspace
+                ))
 
         ds = DifferentialEvolutionSolver(
             func,
             bounds=[[0, 1] for _ in range(len(self._configspace))],
             args=(),
-            strategy="best1bin",
-            maxiter=1000,
-            popsize=50,
+            strategy=self.strategy,
+            maxiter=self.max_iter,
+            popsize=self._challengers // self.max_iter,
             tol=0.01,
-            mutation=(0.5, 1),
-            recombination=0.7,
+            mutation=self.mutation,
+            recombination=self.recombination,
             seed=self._rng.randint(1000),
-            polish=True,
+            polish=self.polish,
             callback=None,
             disp=False,
             init="latinhypercube",
             atol=0,
+            vectorized=True
         )
 
         _ = ds.solve()