linting, whitespace removal, import structuring

Author: adrianmolzon

bayes_opt/acquisition.py

@@ -74,11 +74,11 @@ def _serialize_random_state(self) -> dict | None:
         if self.random_state is not None:
             state = self.random_state.get_state()
             return {
-                'bit_generator': state[0],
-                'state': state[1].tolist(),  # Convert numpy array to list
-                'pos': state[2],
-                'has_gauss': state[3],
-                'cached_gaussian': state[4]
+                "bit_generator": state[0],
+                "state": state[1].tolist(),  # Convert numpy array to list
+                "pos": state[2],
+                "has_gauss": state[3],
+                "cached_gaussian": state[4],
             }
         return None
 
@@ -88,11 +88,11 @@ def _deserialize_random_state(self, state_dict: dict | None) -> None:
             if self.random_state is None:
                 self.random_state = RandomState()
             state = (
-                state_dict['bit_generator'],
-                np.array(state_dict['state'], dtype=np.uint32),
-                state_dict['pos'],
-                state_dict['has_gauss'],
-                state_dict['cached_gaussian']
+                state_dict["bit_generator"],
+                np.array(state_dict["state"], dtype=np.uint32),
+                state_dict["pos"],
+                state_dict["has_gauss"],
+                state_dict["cached_gaussian"],
             )
             self.random_state.set_state(state)
 
@@ -102,24 +102,24 @@ def base_acq(self, *args: Any, **kwargs: Any) -> NDArray[Float]:
 
     def get_acquisition_params(self) -> dict[str, Any]:
         """Get the acquisition function parameters.
-        
+
         Returns
         -------
         dict
             Dictionary containing the acquisition function parameters.
             All values must be JSON serializable.
         """
         return {}
-        
-    def set_acquisition_params(self, params: dict[str, Any]) -> None:
+
+    def set_acquisition_params(self, params: dict) -> None:
         """Set the acquisition function parameters.
-        
+
         Parameters
         ----------
         params : dict
             Dictionary containing the acquisition function parameters.
         """
-        pass
+        return {}
 
     def suggest(
         self,
@@ -167,7 +167,7 @@ def suggest(
 
         acq = self._get_acq(gp=gp, constraint=target_space.constraint)
         return self._acq_min(acq, target_space, n_random=n_random, n_l_bfgs_b=n_l_bfgs_b)
-    
+
     def _fit_gp(self, gp: GaussianProcessRegressor, target_space: TargetSpace) -> None:
         # Sklearn's GP throws a large number of warnings at times, but
         # we don't really need to see them here.
@@ -501,16 +501,30 @@ def decay_exploration(self) -> None:
             self.exploration_decay_delay is None or self.exploration_decay_delay <= self.i
         ):
             self.kappa = self.kappa * self.exploration_decay
-    
+
     def get_acquisition_params(self) -> dict:
+        """Get the current acquisition function parameters.
+
+        Returns
+        -------
+        dict
+            Dictionary containing the current acquisition function parameters.
+        """
         return {
             "kappa": self.kappa,
             "exploration_decay": self.exploration_decay,
             "exploration_decay_delay": self.exploration_decay_delay,
-            "random_state": self._serialize_random_state()
+            "random_state": self._serialize_random_state(),
         }
 
     def set_acquisition_params(self, params: dict) -> None:
+        """Set the acquisition function parameters.
+
+        Parameters
+        ----------
+        params : dict
+            Dictionary containing the acquisition function parameters.
+        """
         self.kappa = params["kappa"]
         self.exploration_decay = params["exploration_decay"]
         self.exploration_decay_delay = params["exploration_decay_delay"]
@@ -648,17 +662,30 @@ def decay_exploration(self) -> None:
             self.exploration_decay_delay is None or self.exploration_decay_delay <= self.i
         ):
             self.xi = self.xi * self.exploration_decay
-            
+
     def get_acquisition_params(self) -> dict:
-        """Get the acquisition function parameters."""
+        """Get the current acquisition function parameters.
+
+        Returns
+        -------
+        dict
+            Dictionary containing the current acquisition function parameters.
+        """
         return {
             "xi": self.xi,
             "exploration_decay": self.exploration_decay,
             "exploration_decay_delay": self.exploration_decay_delay,
-            "random_state": self._serialize_random_state()
+            "random_state": self._serialize_random_state(),
         }
-        
+
     def set_acquisition_params(self, params: dict) -> None:
+        """Set the acquisition function parameters.
+
+        Parameters
+        ----------
+        params : dict
+            Dictionary containing the acquisition function parameters.
+        """
         self.xi = params["xi"]
         self.exploration_decay = params["exploration_decay"]
         self.exploration_decay_delay = params["exploration_decay_delay"]
@@ -804,16 +831,30 @@ def decay_exploration(self) -> None:
             self.exploration_decay_delay is None or self.exploration_decay_delay <= self.i
         ):
             self.xi = self.xi * self.exploration_decay
-            
+
     def get_acquisition_params(self) -> dict:
+        """Get the current acquisition function parameters.
+
+        Returns
+        -------
+        dict
+            Dictionary containing the current acquisition function parameters.
+        """
         return {
             "xi": self.xi,
             "exploration_decay": self.exploration_decay,
             "exploration_decay_delay": self.exploration_decay_delay,
-            "random_state": self._serialize_random_state()
+            "random_state": self._serialize_random_state(),
         }
-        
+
     def set_acquisition_params(self, params: dict) -> None:
+        """Set the acquisition function parameters.
+
+        Parameters
+        ----------
+        params : dict
+            Dictionary containing the acquisition function parameters.
+        """
         self.xi = params["xi"]
         self.exploration_decay = params["exploration_decay"]
         self.exploration_decay_delay = params["exploration_decay_delay"]
@@ -1008,18 +1049,32 @@ def suggest(
         self.dummies.append(x_max)
 
         return x_max
-    
+
     def get_acquisition_params(self) -> dict:
+        """Get the current acquisition function parameters.
+
+        Returns
+        -------
+        dict
+            Dictionary containing the current acquisition function parameters.
+        """
         return {
             "dummies": [dummy.tolist() for dummy in self.dummies],
             "base_acquisition_params": self.base_acquisition.get_acquisition_params(),
             "strategy": self.strategy,
             "atol": self.atol,
             "rtol": self.rtol,
-            "random_state": self._serialize_random_state()
+            "random_state": self._serialize_random_state(),
         }
-        
+
     def set_acquisition_params(self, params: dict) -> None:
+        """Set the acquisition function parameters.
+
+        Parameters
+        ----------
+        params : dict
+            Dictionary containing the acquisition function parameters.
+        """
         self.dummies = [np.array(dummy) for dummy in params["dummies"]]
         self.base_acquisition.set_acquisition_params(params["base_acquisition_params"])
         self.strategy = params["strategy"]
@@ -1144,28 +1199,42 @@ def suggest(
         self.previous_candidates = np.array(x_max)
         idx = self._sample_idx_from_softmax_gains()
         return x_max[idx]
-    
+
     def get_acquisition_params(self) -> dict:
+        """Get the current acquisition function parameters.
+
+        Returns
+        -------
+        dict
+            Dictionary containing the current acquisition function parameters.
+        """
         return {
             "base_acquisitions_params": [acq.get_acquisition_params() for acq in self.base_acquisitions],
             "gains": self.gains.tolist(),
-            "previous_candidates": self.previous_candidates.tolist() if self.previous_candidates is not None else None,
-            "random_states": [acq._serialize_random_state() for acq in self.base_acquisitions] + [self._serialize_random_state()]
+            "previous_candidates": self.previous_candidates.tolist()
+            if self.previous_candidates is not None
+            else None,
+            "random_states": [acq._serialize_random_state() for acq in self.base_acquisitions]
+            + [self._serialize_random_state()],
         }
-        
+
     def set_acquisition_params(self, params: dict) -> None:
+        """Set the acquisition function parameters.
+
+        Parameters
+        ----------
+        params : dict
+            Dictionary containing the acquisition function parameters.
+        """
         for acq, acq_params, random_state in zip(
-            self.base_acquisitions, 
-            params["base_acquisitions_params"],
-            params["random_states"][:-1]
+            self.base_acquisitions, params["base_acquisitions_params"], params["random_states"][:-1]
         ):
             acq.set_acquisition_params(acq_params)
             acq._deserialize_random_state(random_state)
-            
+
         self.gains = np.array(params["gains"])
-        self.previous_candidates = (np.array(params["previous_candidates"]) 
-                                  if params["previous_candidates"] is not None 
-                                  else None)
-        
-        self._deserialize_random_state(params["random_states"][-1])
+        self.previous_candidates = (
+            np.array(params["previous_candidates"]) if params["previous_candidates"] is not None else None
+        )
 
+        self._deserialize_random_state(params["random_states"][-1])