Method suggest

bayes_opt/acquisition.py:1252–1319

Suggest a promising point to probe next. Parameters ---------- gp : GaussianProcessRegressor A fitted Gaussian Process. target_space : TargetSpace The target space to probe. n_random : int, default 10_000 Number of random

(
        self,
        gp: GaussianProcessRegressor,
        target_space: TargetSpace,
        n_random: int = 10_000,
        n_smart: int = 10,
        fit_gp: bool = True,
        random_state: int | RandomState | None = None,
    )

        self.previous_candidates = None

    def suggest(
        self,
        gp: GaussianProcessRegressor,
        target_space: TargetSpace,
        n_random: int = 10_000,
        n_smart: int = 10,
        fit_gp: bool = True,
        random_state: int | RandomState | None = None,
    ) -> NDArray[Float]:
        """Suggest a promising point to probe next.

        Parameters
        ----------
        gp : GaussianProcessRegressor
            A fitted Gaussian Process.

        target_space : TargetSpace
            The target space to probe.

        n_random : int, default 10_000
            Number of random samples to use.

        n_smart : int, default 10
            Number of starting points for the L-BFGS-B optimizer.

        fit_gp : bool, default True
            Whether to fit the Gaussian Process to the target space.
            Set to False if the GP is already fitted.

        random_state : int, RandomState, default None
            Random state to use for the optimization.

        Returns
        -------
        np.ndarray
            Suggested point to probe next.
        """
        if len(target_space) == 0:
            msg = (
                "Cannot suggest a point without previous samples. Use "
                " target_space.random_sample() to generate a point and "
                " target_space.probe(*) to evaluate it."
            )
            raise TargetSpaceEmptyError(msg)
        self.i += 1
        random_state = ensure_rng(random_state)
        if fit_gp:
            self._fit_gp(gp=gp, target_space=target_space)

        # Update the gains of the base acquisition functions
        if self.previous_candidates is not None:
            self._update_gains(gp)

        # Suggest a point using each base acquisition function
        x_max = [
            base_acq.suggest(
                gp=gp,
                target_space=target_space,