Method register

bayes_opt/target_space.py:424–518

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

(
        self, params: ParamsType, target: float, constraint_value: float | NDArray[Float] | None = None
    )

        return x

    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)

        # if x is not within the bounds of the parameter space, warn the user
        if self._bounds is not None and not np.all((self._bounds[:, 0] <= x) & (x <= self._bounds[:, 1])):
            for key in self.keys: