Parameter types more

README.md

@@ -5,7 +5,7 @@
 # Bayesian Optimization
 
 ![tests](https://github.com/bayesian-optimization/BayesianOptimization/actions/workflows/run_tests.yml/badge.svg)
-[![docs - stable](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fraw.githubusercontent.com%2Fbayesian-optimization%2FBayesianOptimization%2Fgh-pages%2Fversions.json&query=%24%5B%3F(%40.aliases%20%26%26%20%40.aliases.indexOf('stable')%20%3E%20-1)%5D.version&prefix=stable%20(v&suffix=)&label=docs)](https://bayesian-optimization.github.io/BayesianOptimization/)
+[![docs - stable](https://img.shields.io/badge/docs-stable-blue)](https://bayesian-optimization.github.io/BayesianOptimization/index.html)
 [![Codecov](https://codecov.io/github/bayesian-optimization/BayesianOptimization/badge.svg?branch=master&service=github)](https://codecov.io/github/bayesian-optimization/BayesianOptimization?branch=master)
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bayes_opt/acquisition.py

@@ -40,7 +40,7 @@
 from bayes_opt.target_space import TargetSpace
 
 if TYPE_CHECKING:
-    from collections.abc import Callable
+    from collections.abc import Callable, Sequence
 
     from numpy.typing import NDArray
     from scipy.optimize import OptimizeResult
@@ -906,18 +906,18 @@ class GPHedge(AcquisitionFunction):
 
     Parameters
     ----------
-    base_acquisitions : List[AcquisitionFunction]
-        List of base acquisition functions.
+    base_acquisitions : Sequence[AcquisitionFunction]
+        Sequence of base acquisition functions.
 
     random_state : int, RandomState, default None
         Set the random state for reproducibility.
     """
 
     def __init__(
-        self, base_acquisitions: list[AcquisitionFunction], random_state: int | RandomState | None = None
+        self, base_acquisitions: Sequence[AcquisitionFunction], random_state: int | RandomState | None = None
     ) -> None:
         super().__init__(random_state)
-        self.base_acquisitions = base_acquisitions
+        self.base_acquisitions = list(base_acquisitions)
         self.n_acq = len(self.base_acquisitions)
         self.gains = np.zeros(self.n_acq)
         self.previous_candidates = None

bayes_opt/bayesian_optimization.py

@@ -21,7 +21,7 @@
 from bayes_opt.util import ensure_rng
 
 if TYPE_CHECKING:
-    from collections.abc import Callable, Iterable, Mapping, Sequence
+    from collections.abc import Callable, Iterable, Mapping
 
     import numpy as np
     from numpy.random import RandomState
@@ -31,6 +31,7 @@
     from bayes_opt.acquisition import AcquisitionFunction
     from bayes_opt.constraint import ConstraintModel
     from bayes_opt.domain_reduction import DomainTransformer
+    from bayes_opt.parameter import BoundsMapping, ParamsType
 
     Float = np.floating[Any]
 
@@ -114,7 +115,7 @@ def __init__(
     ):
         self._random_state = ensure_rng(random_state)
         self._allow_duplicate_points = allow_duplicate_points
-        self._queue: deque[Mapping[str, float] | Sequence[float] | NDArray[Float]] = deque()
+        self._queue: deque[ParamsType] = deque()
 
         if acquisition_function is None:
             if constraint is None:
@@ -203,10 +204,7 @@ def res(self) -> list[dict[str, Any]]:
         return self._space.res()
 
     def register(
-        self,
-        params: Mapping[str, float] | Sequence[float] | NDArray[Float],
-        target: float,
-        constraint_value: float | NDArray[Float] | None = None,
+        self, params: ParamsType, target: float, constraint_value: float | NDArray[Float] | None = None
     ) -> None:
         """Register an observation with known target.
 
@@ -224,9 +222,7 @@ def register(
         self._space.register(params, target, constraint_value)
         self.dispatch(Events.OPTIMIZATION_STEP)
 
-    def probe(
-        self, params: Mapping[str, float] | Sequence[float] | NDArray[Float], lazy: bool = True
-    ) -> None:
+    def probe(self, params: ParamsType, lazy: bool = True) -> None:
         """Evaluate the function at the given points.
 
         Useful to guide the optimizer.
@@ -246,7 +242,7 @@ def probe(
             self._space.probe(params)
             self.dispatch(Events.OPTIMIZATION_STEP)
 
-    def suggest(self) -> dict[str, float]:
+    def suggest(self) -> dict[str, float | NDArray[Float]]:
         """Suggest a promising point to probe next."""
         if len(self._space) == 0:
             return self._space.array_to_params(self._space.random_sample(random_state=self._random_state))
@@ -321,7 +317,7 @@ def maximize(self, init_points: int = 5, n_iter: int = 25) -> None:
 
         self.dispatch(Events.OPTIMIZATION_END)
 
-    def set_bounds(self, new_bounds: Mapping[str, NDArray[Float] | Sequence[float]]) -> None:
+    def set_bounds(self, new_bounds: BoundsMapping) -> None:
         """Modify the bounds of the search space.
 
         Parameters

bayes_opt/constraint.py

@@ -57,7 +57,7 @@ def __init__(
         fun: Callable[..., float] | Callable[..., NDArray[Float]] | None,
         lb: float | NDArray[Float],
         ub: float | NDArray[Float],
-        transform=None,
+        transform: Callable[[Any], Any] | None = None,
         random_state: int | RandomState | None = None,
     ) -> None:
         self.fun = fun

bayes_opt/domain_reduction.py

@@ -8,6 +8,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from collections.abc import Iterable, Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 from warnings import warn
 
@@ -16,8 +17,6 @@
 from bayes_opt.target_space import TargetSpace
 
 if TYPE_CHECKING:
-    from collections.abc import Iterable, Mapping, Sequence
-
     from numpy.typing import NDArray
 
     Float = np.floating[Any]
@@ -67,14 +66,16 @@ def __init__(
         gamma_osc: float = 0.7,
         gamma_pan: float = 1.0,
         eta: float = 0.9,
-        minimum_window: NDArray[Float] | Sequence[float] | float | Mapping[str, float] | None = 0.0,
+        minimum_window: NDArray[Float] | Sequence[float] | Mapping[str, float] | float = 0.0,
     ) -> None:
         # TODO: Ensure that this is only applied to continuous parameters
         self.parameters = parameters
         self.gamma_osc = gamma_osc
         self.gamma_pan = gamma_pan
         self.eta = eta
-        if isinstance(minimum_window, dict):
+
+        self.minimum_window_value: NDArray[Float] | Sequence[float] | float
+        if isinstance(minimum_window, Mapping):
             self.minimum_window_value = [
                 item[1] for item in sorted(minimum_window.items(), key=lambda x: x[0])
             ]
@@ -93,8 +94,9 @@ def initialize(self, target_space: TargetSpace) -> None:
         self.original_bounds = np.copy(target_space.bounds)
         self.bounds = [self.original_bounds]
 
+        self.minimum_window: NDArray[Float] | Sequence[float]
         # Set the minimum window to an array of length bounds
-        if isinstance(self.minimum_window_value, (list, np.ndarray)):
+        if isinstance(self.minimum_window_value, (Sequence, np.ndarray)):
             if len(self.minimum_window_value) != len(target_space.bounds):
                 error_msg = "Length of minimum_window must be the same as the number of parameters"
                 raise ValueError(error_msg)