def update()

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

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

    def update(self, Y: Tensor) -> None:
        r"""Update non-dominated front and decomposition.

        Args:
            Y: A `(batch_shape) x n x m`-dim tensor of new, incremental outcomes.
        """
        if self._update_neg_Y(Y=Y):
            self.reset()
        else:
            if self.num_outcomes == 2 or self._neg_pareto_Y.shape[-2] == 0:
                # If there are two objective, recompute the box decomposition
                # because the partitions can be computed analytically.
                # If the current pareto set has no points, recompute the box
                # decomposition.
                self.reset()
            else:
                # only include points that are better than the reference point
                better_than_ref = (Y > self.ref_point).all(dim=-1)
                Y = Y[better_than_ref]
                Y_all = torch.cat([self._neg_pareto_Y, -Y], dim=-2)
                pareto_mask = is_non_dominated(-Y_all)
                # determine the number of points in Y that are Pareto optimal
                num_new_pareto = pareto_mask[-Y.shape[-2] :].sum()
                self._neg_pareto_Y = Y_all[pareto_mask]
                if num_new_pareto > 0:
                    # update local upper bounds for the minimization problem
                    self._U, self._Z = update_local_upper_bounds_incremental(
                        # this assumes minimization
                        new_pareto_Y=self._neg_pareto_Y[-num_new_pareto:],
                        U=self._U,
                        Z=self._Z,
                    )
                    # use the negative local upper bounds as the new pareto
                    # frontier for the minimization problem and perform
                    # box decomposition on dominated space.
                    self._get_partitioning()