def _get_partitioning()

in botorch/utils/multi_objective/box_decompositions/non_dominated.py [0:0]

• 23 lines of code
• 8 McCabe index (conditional complexity)

    def _get_partitioning(self) -> None:
        r"""Compute non-dominated partitioning.

        Given local upper bounds for the minimization problem (self._U), this computes
        the non-dominated partitioning for the maximization problem. Note that
        -self.U contains the local lower bounds for the maximization problem. Following
        [Yang2019]_, this treats -self.U as a *new* pareto frontier for a minimization
        problem with a reference point of [infinity]^m and computes a dominated
        partitioning for this minimization problem.
        """
        new_ref_point = torch.full(
            torch.Size([1]) + self._neg_ref_point.shape,
            float("inf"),
            dtype=self._neg_ref_point.dtype,
            device=self._neg_ref_point.device,
        )
        # initialize local upper bounds for the second minimization problem
        self.register_buffer("_U2", new_ref_point)
        # initialize defining points for the second minimization problem
        # use ref point for maximization as the ideal point for minimization.
        self._Z2 = self.ref_point.expand(
            1, self.num_outcomes, self.num_outcomes
        ).clone()
        for j in range(self._neg_ref_point.shape[-1]):
            self._Z2[0, j, j] = self._U2[0, j]
        # incrementally update local upper bounds and defining points
        # for each new Pareto point
        self._U2, self._Z2 = update_local_upper_bounds_incremental(
            new_pareto_Y=-self._U,
            U=self._U2,
            Z=self._Z2,
        )
        cell_bounds = get_partition_bounds(
            Z=self._Z2, U=self._U2, ref_point=new_ref_point.view(-1)
        )
        self.register_buffer("hypercell_bounds", cell_bounds)