Fix documentation check errors

src/calibr/calibration.py

@@ -22,7 +22,7 @@
     GaussianProcessParameterFitter,
     fit_gaussian_process_parameters_map,
 )
-from .optimization import GlobalMinimizer, minimize_with_restarts
+from .optimization import GlobalMinimizer, GlobalMinimizerKwarg, minimize_with_restarts
 
 #: Type alias for function generating random initial values for inputs.
 InitialInputSampler: TypeAlias = Callable[[Generator, int], Array]
@@ -40,7 +40,7 @@ def get_next_inputs_batch_by_joint_optimization(
     batch_size: int,
     *,
     minimize_function: GlobalMinimizer = minimize_with_restarts,
-    **minimize_function_kwargs,
+    **minimize_function_kwargs: GlobalMinimizerKwarg,
 ) -> tuple[Array, float]:
     """
     Get next batch of inputs to evaluate by jointly optimizing acquisition function.
@@ -89,7 +89,7 @@ def get_next_inputs_batch_by_greedy_optimization(
     batch_size: int,
     *,
     minimize_function: GlobalMinimizer = minimize_with_restarts,
-    **minimize_function_kwargs,
+    **minimize_function_kwargs: GlobalMinimizerKwarg,
 ) -> tuple[Array, float]:
     """
     Get next batch of inputs to evaluate by greedily optimizing acquisition function.

src/calibr/emulation.py

@@ -17,7 +17,7 @@
 from jax.typing import ArrayLike
 from numpy.random import Generator
 
-from .optimization import GlobalMinimizer, minimize_with_restarts
+from .optimization import GlobalMinimizer, GlobalMinimizerKwarg, minimize_with_restarts
 
 try:
     import mici
@@ -76,11 +76,11 @@ def get_gaussian_process_factory(
         mean_function: Mean function for Gaussian process.
         covariance_function: Covariance function for Gaussian process.
         neg_log_prior_density: Negative logarithm of density of prior distribution on
-           vector of unconstrained parameters for Gaussian process model.
+            vector of unconstrained parameters for Gaussian process model.
         transform_parameters: Function which maps flat unconstrained parameter vector to
-           a dictionary of (potential constrained) parameters, keyed by parameter name.
+            a dictionary of (potential constrained) parameters, keyed by parameter name.
         sample_unconstrained_parameters: Function generating random values for
-           unconstrained vector of Gaussian process parameters.
+            unconstrained vector of Gaussian process parameters.
 
     Returns:
         Gaussian process factory function.
@@ -116,7 +116,7 @@ def fit_gaussian_process_parameters_map(
     gaussian_process: GaussianProcessModel,
     *,
     minimize_function: GlobalMinimizer = minimize_with_restarts,
-    **minimize_function_kwargs,
+    **minimize_function_kwargs: GlobalMinimizerKwarg,
 ) -> ParametersDict:
     """Fit parameters of Gaussian process model by maximimizing posterior density.
 

src/calibr/optimization.py

@@ -2,7 +2,7 @@
 
 from collections.abc import Callable
 from heapq import heappush
-from typing import Protocol, TypeAlias
+from typing import Any, Protocol, TypeAlias
 
 import jax
 from jax.typing import ArrayLike
@@ -22,6 +22,8 @@ class ConvergenceError(Exception):
 #: Type alias for function sampling initial optimization state given random generator
 InitialStateSampler: TypeAlias = Callable[[Generator], ndarray]
 
+GlobalMinimizerKwarg: TypeAlias = Any
+
 
 class GlobalMinimizer(Protocol):
     """Function which attempts to find global minimum of a scalar objective function."""
@@ -31,6 +33,7 @@ def __call__(
         objective_function: ObjectiveFunction,
         sample_initial_state: InitialStateSampler,
         rng: Generator,
+        **kwargs: GlobalMinimizerKwarg,
     ) -> tuple[jax.Array, float]:
         """
         Minimize a differentiable objective function.
@@ -45,6 +48,7 @@ def __call__(
                 random number generator returns a random initial state for optimization
                 of appropriate dimension.
             rng: Seeded NumPy random number generator.
+            **kwargs: Any keyword arguments to global minimizer function.
 
         Returns:
             Tuple with first entry the state corresponding to the minima point and the
@@ -71,6 +75,14 @@ def hvp(x: ArrayLike, v: ArrayLike) -> jax.Array:
     return hvp
 
 
+def _check_unknown_kwargs(unknown_kwargs: dict[str, GlobalMinimizerKwarg]) -> None:
+    if unknown_kwargs:
+        msg = ". ".join(
+            f"Unknown keyword argument {k}={v}" for k, v in unknown_kwargs.items()
+        )
+        raise ValueError(msg)
+
+
 def minimize_with_restarts(
     objective_function: ObjectiveFunction,
     sample_initial_state: InitialStateSampler,
@@ -82,6 +94,7 @@ def minimize_with_restarts(
     minimize_max_iterations: int | None = None,
     minimize_tol: float | None = None,
     logging_function: Callable[[str], None] = lambda _: None,
+    **unknown_kwargs: GlobalMinimizerKwarg,
 ) -> tuple[jax.Array, float]:
     """Minimize a differentiable objective function with random restarts.
 
@@ -102,6 +115,8 @@ def minimize_with_restarts(
             random number generator returns a random initial state for optimization of
             appropriate dimension.
         rng: Seeded NumPy random number generator.
+
+    Keyword Args:
         number_minima_to_find: Number of candidate minima of objective function to try
             to find.
         maximum_minimize_calls: Maximum number of times to try calling
@@ -120,6 +135,7 @@ def minimize_with_restarts(
         Tuple with first entry the state corresponding to the best minima candidate
         found and the second entry the corresponding objective function value.
     """
+    _check_unknown_kwargs(unknown_kwargs)
     minima_found: list[tuple[jax.Array, int, jax.Array]] = []
     minimize_calls = 0
     while (
@@ -171,6 +187,7 @@ def basin_hopping(
     minimize_method: str = "Newton-CG",
     minimize_max_iterations: int | None = None,
     minimize_tol: float | None = None,
+    **unknown_kwargs: GlobalMinimizerKwarg,
 ) -> tuple[jax.Array, float]:
     """Minimize a differentiable objective function with SciPy basin-hopping algorithm.
 
@@ -201,6 +218,7 @@ def basin_hopping(
         Tuple with first entry the state corresponding to the best minima candidate
         found and the second entry the corresponding objective function value.
     """
+    _check_unknown_kwargs(unknown_kwargs)
     results = _basin_hopping(
         jax.jit(objective_function),
         x0=sample_initial_state(rng),