Fix differential evolution

smac/acquisition/maximizer/differential_evolution.py

@@ -5,6 +5,7 @@
 from scipy.optimize._differentialevolution import DifferentialEvolutionSolver
 
 from smac.acquisition.maximizer import AbstractAcquisitionMaximizer
+from smac.utils.configspace import transform_continuous_designs
 
 __copyright__ = "Copyright 2022, automl.org"
 __license__ = "3-clause BSD"
@@ -36,7 +37,9 @@ def _maximize(
 
         def func(x: np.ndarray) -> np.ndarray:
             assert self._acquisition_function is not None
-            return -self._acquisition_function([Configuration(self._configspace, vector=x)])
+            return -self._acquisition_function([transform_continuous_designs(
+                design=np.expand_dims(x, axis=0), origin="Diffrential Evolution", configspace=self._configspace
+                )[0]])
 
         ds = DifferentialEvolutionSolver(
             func,
@@ -58,8 +61,9 @@ def func(x: np.ndarray) -> np.ndarray:
 
         _ = ds.solve()
         for pop, val in zip(ds.population, ds.population_energies):
-            rc = Configuration(self._configspace, vector=pop)
-            rc.origin = "Acquisition Function Maximizer: Differential Evolution"
+            rc = transform_continuous_designs(
+                design=np.expand_dims(pop, axis=0), origin="Acquisition Function Maximizer: Differential Evolution", configspace=self._configspace
+                )[0]
             configs.append((-val, rc))
 
         configs.sort(key=lambda t: t[0])

smac/initial_design/abstract_initial_design.py

@@ -155,57 +155,3 @@ def select_configurations(self) -> list[Configuration]:
     def _select_configurations(self) -> list[Configuration]:
         """Selects the initial configurations, depending on the implementation of the initial design."""
         raise NotImplementedError
-
-    def _transform_continuous_designs(
-        self, design: np.ndarray, origin: str, configspace: ConfigurationSpace
-    ) -> list[Configuration]:
-        """Transforms the continuous designs into a discrete list of configurations.
-
-        Parameters
-        ----------
-        design : np.ndarray
-            Array of hyperparameters originating from the initial design strategy.
-        origin : str | None, defaults to None
-            Label for a configuration where it originated from.
-        configspace : ConfigurationSpace
-
-        Returns
-        -------
-        configs : list[Configuration]
-            Continuous transformed configs.
-        """
-        params = list(configspace.values())
-        for idx, param in enumerate(params):
-            if isinstance(param, IntegerHyperparameter):
-                design[:, idx] = param.to_vector(param.to_value(design[:, idx]))
-            elif isinstance(param, NumericalHyperparameter):
-                continue
-            elif isinstance(param, Constant):
-                design_ = np.zeros(np.array(design.shape) + np.array((0, 1)))
-                design_[:, :idx] = design[:, :idx]
-                design_[:, idx + 1 :] = design[:, idx:]
-                design = design_
-            elif isinstance(param, CategoricalHyperparameter):
-                v_design = design[:, idx]
-                v_design[v_design == 1] = 1 - 10**-10
-                design[:, idx] = np.array(v_design * len(param.choices), dtype=int)
-            elif isinstance(param, OrdinalHyperparameter):
-                v_design = design[:, idx]
-                v_design[v_design == 1] = 1 - 10**-10
-                design[:, idx] = np.array(v_design * len(param.sequence), dtype=int)
-            else:
-                raise ValueError("Hyperparameter not supported when transforming a continuous design.")
-
-        configs = []
-        for vector in design:
-            try:
-                conf = deactivate_inactive_hyperparameters(
-                    configuration=None, configuration_space=configspace, vector=vector
-                )
-            except ForbiddenValueError:
-                continue
-
-            conf.origin = origin
-            configs.append(conf)
-
-        return configs

smac/initial_design/latin_hypercube_design.py

@@ -5,6 +5,7 @@
 from scipy.stats.qmc import LatinHypercube
 
 from smac.initial_design.abstract_initial_design import AbstractInitialDesign
+from smac.utils.configspace import transform_continuous_designs
 
 __copyright__ = "Copyright 2022, automl.org"
 __license__ = "3-clause BSD"
@@ -25,6 +26,6 @@ def _select_configurations(self) -> list[Configuration]:
 
         lhd = LatinHypercube(d=len(params) - constants, seed=self._rng.randint(0, 1000000)).random(n=self._n_configs)
 
-        return self._transform_continuous_designs(
+        return transform_continuous_designs(
             design=lhd, origin="Initial Design: Latin Hypercube", configspace=self._configspace
         )

smac/initial_design/sobol_design.py

@@ -9,6 +9,8 @@
 from scipy.stats.qmc import Sobol
 
 from smac.initial_design.abstract_initial_design import AbstractInitialDesign
+from smac.utils.configspace import transform_continuous_designs
+
 
 __copyright__ = "Copyright 2022, automl.org"
 __license__ = "3-clause BSD"
@@ -43,6 +45,6 @@ def _select_configurations(self) -> list[Configuration]:
             warnings.simplefilter("ignore")
             sobol = sobol_gen.random(self._n_configs)
 
-        return self._transform_continuous_designs(
+        return transform_continuous_designs(
             design=sobol, origin="Initial Design: Sobol", configspace=self._configspace
         )

smac/utils/configspace.py

@@ -16,7 +16,10 @@
     OrdinalHyperparameter,
     UniformFloatHyperparameter,
     UniformIntegerHyperparameter,
+    IntegerHyperparameter,
+    NumericalHyperparameter,
 )
+from ConfigSpace.util import ForbiddenValueError, deactivate_inactive_hyperparameters
 from ConfigSpace.util import get_one_exchange_neighbourhood
 
 __copyright__ = "Copyright 2022, automl.org"
@@ -182,6 +185,61 @@ def print_config_changes(
     logger.debug(msg)
 
 
+def transform_continuous_designs(
+        design: np.ndarray, origin: str, configspace: ConfigurationSpace
+    ) -> list[Configuration]:
+        """Transforms the continuous designs into a discrete list of configurations.
+
+        Parameters
+        ----------
+        design : np.ndarray
+            Array of hyperparameters originating from the initial design strategy.
+        origin : str | None, defaults to None
+            Label for a configuration where it originated from.
+        configspace : ConfigurationSpace
+
+        Returns
+        -------
+        configs : list[Configuration]
+            Continuous transformed configs.
+        """
+        params = configspace.get_hyperparameters()
+        for idx, param in enumerate(params):
+            if isinstance(param, IntegerHyperparameter):
+                design[:, idx] = param._inverse_transform(param._transform(design[:, idx]))
+            elif isinstance(param, NumericalHyperparameter):
+                continue
+            elif isinstance(param, Constant):
+                design_ = np.zeros(np.array(design.shape) + np.array((0, 1)))
+                design_[:, :idx] = design[:, :idx]
+                design_[:, idx + 1 :] = design[:, idx:]
+                design = design_
+            elif isinstance(param, CategoricalHyperparameter):
+                v_design = design[:, idx]
+                v_design[v_design == 1] = 1 - 10**-10
+                design[:, idx] = np.array(v_design * len(param.choices), dtype=int)
+            elif isinstance(param, OrdinalHyperparameter):
+                v_design = design[:, idx]
+                v_design[v_design == 1] = 1 - 10**-10
+                design[:, idx] = np.array(v_design * len(param.sequence), dtype=int)
+            else:
+                raise ValueError("Hyperparameter not supported when transforming a continuous design.")
+
+        configs = []
+        for vector in design:
+            try:
+                conf = deactivate_inactive_hyperparameters(
+                    configuration=None, configuration_space=configspace, vector=vector
+                )
+            except ForbiddenValueError:
+                continue
+
+            conf.origin = origin
+            configs.append(conf)
+
+        return configs
+
+
 # def check_subspace_points(
 #     X: np.ndarray,
 #     cont_dims: np.ndarray | list = [],