refactor(issue-607): extract _state_to_dict and _load_state_dict methods (#609)

- Add _state_to_dict() to serialize optimizer state to dictionary
- Add _load_state_dict() to load optimizer state from dictionary
- Update save_state() to use _state_to_dict()
- Update load_state() to use _load_state_dict()
- Enables JSON-based state manipulation without file I/O

Co-authored-by: i.a.grebenkin <i.a.grebenkin@tbank.ru>

Author: quuger

bayes_opt/bayesian_optimization.py

@@ -406,13 +406,13 @@ def set_gp_params(self, **params: Any) -> None:
             params["kernel"] = wrap_kernel(kernel=params["kernel"], transform=self._space.kernel_transform)
         self._gp.set_params(**params)
 
-    def save_state(self, path: str | PathLike[str]) -> None:
-        """Save complete state for reconstruction of the optimizer.
+    def _state_to_dict(self) -> dict[str, Any]:
+        """Convert optimizer state to a dictionary.
 
-        Parameters
-        ----------
-        path : str or PathLike
-            Path to save the optimization state
+        Returns
+        -------
+        dict
+            Dictionary containing the complete optimizer state.
         """
         random_state = None
         if self._random_state is not None:
@@ -428,7 +428,7 @@ def save_state(self, path: str | PathLike[str]) -> None:
         # Get constraint values if they exist
         constraint_values = self._space._constraint_values.tolist() if self.is_constrained else None
         acquisition_params = self._acquisition_function.get_acquisition_params()
-        state = {
+        return {
             "pbounds": {key: self._space._bounds[i].tolist() for i, key in enumerate(self._space.keys)},
             # Add current transformed bounds if using bounds transformer
             "transformed_bounds": (self._space.bounds.tolist() if self._bounds_transformer else None),
@@ -448,20 +448,14 @@ def save_state(self, path: str | PathLike[str]) -> None:
             "acquisition_params": acquisition_params,
         }
 
-        with Path(path).open("w") as f:
-            json.dump(state, f, indent=2)
-
-    def load_state(self, path: str | PathLike[str]) -> None:
-        """Load optimizer state from a JSON file.
+    def _load_state_dict(self, state: dict[str, Any]) -> None:
+        """Load optimizer state from a dictionary.
 
         Parameters
         ----------
-        path : str or PathLike
-            Path to the JSON file containing the optimizer state.
+        state : dict
+            Dictionary containing the optimizer state.
         """
-        with Path(path).open("r") as file:
-            state = json.load(file)
-
         params_array = np.asarray(state["params"], dtype=np.float64)
         target_array = np.asarray(state["target"], dtype=np.float64)
         constraint_array = (
@@ -504,3 +498,27 @@ def load_state(self, path: str | PathLike[str]) -> None:
                 state["random_state"]["cached_gaussian"],
             )
             self._random_state.set_state(random_state_tuple)
+
+    def save_state(self, path: str | PathLike[str]) -> None:
+        """Save complete state for reconstruction of the optimizer.
+
+        Parameters
+        ----------
+        path : str or PathLike
+            Path to save the optimization state
+        """
+        state = self._state_to_dict()
+        with Path(path).open("w") as f:
+            json.dump(state, f, indent=2)
+
+    def load_state(self, path: str | PathLike[str]) -> None:
+        """Load optimizer state from a JSON file.
+
+        Parameters
+        ----------
+        path : str or PathLike
+            Path to the JSON file containing the optimizer state.
+        """
+        with Path(path).open("r") as file:
+            state = json.load(file)
+        self._load_state_dict(state)