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Author: LukasFehring

smac/initial_design/random_design.py

@@ -4,6 +4,7 @@
 
 from smac.initial_design.abstract_initial_design import AbstractInitialDesign
 from smac.utils.configspace import create_uniform_configspace_copy
+
 __copyright__ = "Copyright 2025, Leibniz University Hanover, Institute of AI"
 __license__ = "3-clause BSD"
 

smac/utils/configspace.py

@@ -30,7 +30,9 @@
 __license__ = "3-clause BSD"
 
 
-get_one_exchange_neighbourhood = partial(get_one_exchange_neighbourhood, stdev=0.05, num_neighbors=8)
+get_one_exchange_neighbourhood = partial(
+    get_one_exchange_neighbourhood, stdev=0.05, num_neighbors=8
+)
 
 
 def convert_configurations_to_array(configs: list[Configuration]) -> np.ndarray:
@@ -106,22 +108,30 @@ def get_types(
                 bounds[i] = (0, 1.0)
         elif isinstance(param, NormalFloatHyperparameter):
             if can_be_inactive:
-                raise ValueError("Inactive parameters not supported for Beta and Normal Hyperparameters")
+                raise ValueError(
+                    "Inactive parameters not supported for Beta and Normal Hyperparameters"
+                )
 
             bounds[i] = (param.lower_vectorized, param.upper_vectorized)
         elif isinstance(param, NormalIntegerHyperparameter):
             if can_be_inactive:
-                raise ValueError("Inactive parameters not supported for Beta and Normal Hyperparameters")
+                raise ValueError(
+                    "Inactive parameters not supported for Beta and Normal Hyperparameters"
+                )
 
             bounds[i] = (param.lower_vectorized, param.upper_vectorized)
         elif isinstance(param, BetaFloatHyperparameter):
             if can_be_inactive:
-                raise ValueError("Inactive parameters not supported for Beta and Normal Hyperparameters")
+                raise ValueError(
+                    "Inactive parameters not supported for Beta and Normal Hyperparameters"
+                )
 
             bounds[i] = (param.lower_vectorized, param.upper_vectorized)
         elif isinstance(param, BetaIntegerHyperparameter):
             if can_be_inactive:
-                raise ValueError("Inactive parameters not supported for Beta and Normal Hyperparameters")
+                raise ValueError(
+                    "Inactive parameters not supported for Beta and Normal Hyperparameters"
+                )
 
             bounds[i] = (param.lower_vectorized, param.upper_vectorized)
         elif not isinstance(
@@ -146,7 +156,9 @@ def get_types(
     return types, bounds
 
 
-def get_conditional_hyperparameters(X: np.ndarray, Y: np.ndarray | None = None) -> np.ndarray:
+def get_conditional_hyperparameters(
+    X: np.ndarray, Y: np.ndarray | None = None
+) -> np.ndarray:
     """Returns conditional hyperparameters if values with -1 or smaller are observed. X is used
     if Y is not specified.
     """
@@ -183,7 +195,9 @@ def print_config_changes(
     for k in sorted(all_keys):
         inc_k = incumbent.get(k, "-inactive-")
         cha_k = challenger.get(k, "-inactive-")
-        lines.append(f"--- {k}: {inc_k} -> {cha_k}" + " (unchanged)" if inc_k == cha_k else "")
+        lines.append(
+            f"--- {k}: {inc_k} -> {cha_k}" + " (unchanged)" if inc_k == cha_k else ""
+        )
 
     msg = "\n".join(lines)
     logger.debug(msg)
@@ -212,7 +226,9 @@ def transform_continuous_designs(
         if isinstance(param, NumericalHyperparameter):
             if isinstance(param, IntegerHyperparameter):
                 design[:, idx] = param.to_vector(param.to_value(design[:, idx]))
-            elif isinstance(param, NumericalHyperparameter) and not isinstance(param, IntegerHyperparameter):
+            elif isinstance(param, NumericalHyperparameter) and not isinstance(
+                param, IntegerHyperparameter
+            ):
                 continue
         elif isinstance(param, Constant):
             design_ = np.zeros(np.array(design.shape) + np.array((0, 1)))
@@ -228,7 +244,9 @@ def transform_continuous_designs(
             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.")
+            raise ValueError(
+                "Hyperparameter not supported when transforming a continuous design."
+            )
 
     configs = []
     for vector in design:
@@ -244,14 +262,17 @@ def transform_continuous_designs(
 
     return configs
 
-def create_uniform_configspace_copy(configspace:ConfigurationSpace) -> ConfigurationSpace:
+
+def create_uniform_configspace_copy(
+    configspace: ConfigurationSpace,
+) -> ConfigurationSpace:
     """Creates a copy of the given configuration space with uniform transformed hyperparameters.
-    
+
     Parameters
     ----------
     configspace : ConfigurationSpace
         The configuration space to be transformed.
-        
+
     Returns
     -------
     ConfigurationSpace
@@ -260,7 +281,9 @@ def create_uniform_configspace_copy(configspace:ConfigurationSpace) -> Configura
     configspace_name = configspace.name
     configspace_meta = configspace.meta
     random_state = configspace.random.get_state()
-    new_configuration_space = ConfigurationSpace(name = configspace_name, meta = configspace_meta)
+    new_configuration_space = ConfigurationSpace(
+        name=configspace_name, meta=configspace_meta
+    )
     new_configuration_space.random.set_state(random_state)
 
     for hyperparameter in configspace.values():