[WIP] chore: support 3.14t

[phi-friday]

Adds Python 3.14t support and updates compatibility checks for the free-threaded build.

Some tests are currently failing, once #613 is merged or rejected, we can decide how to resolve them.

❯ uv run pytest -n 4
========================================== test session starts ==========================================
platform linux -- Python 3.14.5, pytest-9.0.2, pluggy-1.6.0
rootdir: /home/phi/git/python/BayesianOptimization
configfile: pyproject.toml
plugins: anyio-4.12.1, cov-7.0.0, xdist-3.8.0
4 workers [174 items]   
................................................................................................. [ 55%]
....................F........................................................                                                                    [100%]
======================================================================= FAILURES =======================================================================
_______________________________________________________________ test_gphedge_integration _______________________________________________________________
[gw1] linux -- Python 3.14.5 /home/phi/git/python/BayesianOptimization/.venv/bin/python

gp = GaussianProcessRegressor(random_state=RandomState(MT19937) at 0x5AB34C05200)
target_space = <bayes_opt.target_space.TargetSpace object at 0x5ab32837510>, random_state = RandomState(MT19937) at 0x5AB34C05200

    def test_gphedge_integration(gp, target_space, random_state):
        base_acq1 = acquisition.UpperConfidenceBound()
        base_acq2 = acquisition.ProbabilityOfImprovement(xi=0.01)
        base_acquisitions = [base_acq1, base_acq2]
    
        acq = acquisition.GPHedge(base_acquisitions=base_acquisitions)
        assert acq.base_acquisitions == base_acquisitions
        with pytest.raises(exception.TargetSpaceEmptyError):
            acq.suggest(gp=gp, target_space=target_space, random_state=random_state)
        target_space.register(params={"x": 2.5, "y": 0.5}, target=3.0)
    
        for _ in range(5):
            p = acq.suggest(gp=gp, target_space=target_space, random_state=random_state)
>           target_space.register(p, sum(p))

tests/test_acquisition.py:371: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <bayes_opt.target_space.TargetSpace object at 0x5ab32837510>, params = array([4., 3.]), target = np.float64(7.0), constraint_value = None

    def register(
        self, params: ParamsType, target: float, constraint_value: float | NDArray[Float] | None = None
    ) -> None:
        """Append a point and its target value to the known data.
    
        Parameters
        ----------
        params : np.ndarray
            a single point, with len(x) == self.dim.
    
        target : float
            target function value
    
        constraint_value : float or np.ndarray or None
            Constraint function value
    
        Raises
        ------
        NotUniqueError:
            if the point is not unique
    
        Notes
        -----
        runs in amortized constant time
    
        Examples
        --------
        >>> target_func = lambda p1, p2: p1 + p2
        >>> pbounds = {"p1": (0, 1), "p2": (1, 100)}
        >>> space = TargetSpace(target_func, pbounds)
        >>> len(space)
        0
        >>> x = np.array([0, 0])
        >>> y = 1
        >>> space.register(x, y)
        >>> len(space)
        1
        """
        x = self._as_array(params)
    
        if x in self:
            if self._allow_duplicate_points:
                self.n_duplicate_points = self.n_duplicate_points + 1
    
                print(
                    Fore.RED + f"Data point {x} is not unique. {self.n_duplicate_points}"
                    " duplicates registered. Continuing ..." + Fore.RESET
                )
            else:
                error_msg = (
                    f"Data point {x} is not unique. You can set"
                    ' "allow_duplicate_points=True" to avoid this error'
                )
>               raise NotUniqueError(error_msg)
E               bayes_opt.exception.NotUniqueError: Data point [4. 3.] is not unique. You can set "allow_duplicate_points=True" to avoid this error

bayes_opt/target_space.py:477: NotUniqueError
=================================================================== warnings summary ===================================================================
tests/test_bayesian_optimization.py::test_save_load_w_categorical_params
  /home/phi/git/python/BayesianOptimization/tests/test_bayesian_optimization.py:381: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    optimizer = BayesianOptimization(f=str_target_func, pbounds=str_pbounds, random_state=1, verbose=0)

tests/test_bayesian_optimization.py::test_save_load_w_categorical_params
  /home/phi/git/python/BayesianOptimization/tests/test_bayesian_optimization.py:388: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    new_optimizer = BayesianOptimization(f=str_target_func, pbounds=str_pbounds, random_state=1, verbose=0)

tests/test_bayesian_optimization.py::test_predict_integer_params
  /home/phi/git/python/BayesianOptimization/tests/test_bayesian_optimization.py:711: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    optimizer = BayesianOptimization(f=target_func, pbounds=int_pbounds, random_state=1, verbose=0)

tests/test_bayesian_optimization.py::test_predict_categorical_params
  /home/phi/git/python/BayesianOptimization/tests/test_bayesian_optimization.py:754: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    optimizer = BayesianOptimization(f=cat_target_func, pbounds=cat_pbounds, random_state=1, verbose=0)

tests/test_bayesian_optimization.py::test_predict_custom_parameter
  /home/phi/git/python/BayesianOptimization/tests/test_bayesian_optimization.py:803: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    optimizer = BayesianOptimization(f=area_of_triangle, pbounds=pbounds, random_state=1, verbose=0)

tests/test_bayesian_optimization.py::test_save_load_w_custom_parameter
  /home/phi/git/python/BayesianOptimization/tests/test_bayesian_optimization.py:574: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    optimizer = BayesianOptimization(f=area_of_triangle, pbounds=pbounds, random_state=1, verbose=0)

tests/test_bayesian_optimization.py::test_save_load_w_custom_parameter
  /home/phi/git/python/BayesianOptimization/tests/test_bayesian_optimization.py:587: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    new_optimizer = BayesianOptimization(f=area_of_triangle, pbounds=pbounds, random_state=1, verbose=0)

tests/test_parameter.py::test_preconstructed_parameter
  /home/phi/git/python/BayesianOptimization/tests/test_parameter.py:179: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    optimizer1 = BayesianOptimization(target_func, pbounds)

tests/test_parameter.py::test_preconstructed_parameter
  /home/phi/git/python/BayesianOptimization/tests/test_parameter.py:182: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    optimizer2 = BayesianOptimization(target_func, pbounds)

tests/test_parameter.py::test_integration_mixed_optimization
  /home/phi/git/python/BayesianOptimization/tests/test_parameter.py:202: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    optimizer = BayesianOptimization(target_func, pbounds)

tests/test_parameter.py::test_integration_mixed_optimization_with_constraints
  /home/phi/git/python/BayesianOptimization/tests/test_parameter.py:224: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    optimizer = BayesianOptimization(target_func, pbounds, constraint=constraint)

tests/test_parameter.py: 5 warnings
tests/test_seq_domain_red.py: 1 warning
tests/test_target_space.py: 4 warnings
  /home/phi/git/python/BayesianOptimization/.venv/lib/python3.14t/site-packages/_pytest/python.py:166: UserWarning: Non-float parameters are experimental and may not work as expected. Exercise caution when using them and please report any issues you encounter.
    result = testfunction(**testargs)

tests/test_parameter.py::test_combined_wrapped_kernel_fit
  /home/phi/git/python/BayesianOptimization/.venv/lib/python3.14t/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.
    warnings.warn(

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=============================================================== short test summary info ================================================================
FAILED tests/test_acquisition.py::test_gphedge_integration - bayes_opt.exception.NotUniqueError: Data point [4. 3.] is not unique. You can set "allow_duplicate_points=True" to avoid this error
================================================ 1 failed, 173 passed, 22 warnings in 142.63s (0:02:22) ================================================

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[t-muser]

Hey @phi-friday, thanks for the contributions as usual! And apologies for the delay, feel free to ping me if things take too long.