def is_non_dominated()

in botorch/utils/multi_objective/pareto.py [0:0]

• 18 lines of code
• 6 McCabe index (conditional complexity)

def is_non_dominated(Y: Tensor, deduplicate: bool = True) -> Tensor:
    r"""Computes the non-dominated front.

    Note: this assumes maximization.

    For small `n`, this method uses a highly parallel methodology
    that compares all pairs of points in Y. However, this is memory
    intensive and slow for large `n`. For large `n` (or if Y is larger
    than 5MB), this method will dispatch to a loop-based approach
    that is faster and has a lower memory footprint.

    Args:
        Y: A `(batch_shape) x n x m`-dim tensor of outcomes.
        deduplicate: A boolean indicating whether to only return
            unique points on the pareto frontier.

    Returns:
        A `(batch_shape) x n`-dim boolean tensor indicating whether
        each point is non-dominated.
    """
    n = Y.shape[-2]
    if n == 0:
        return torch.zeros(Y.shape[:-1], dtype=torch.bool, device=Y.device)
    el_size = 64 if Y.dtype == torch.double else 32
    if n > 1000 or n ** 2 * Y.shape[:-2].numel() * el_size / 8 > MAX_BYTES:
        return _is_non_dominated_loop(Y)

    Y1 = Y.unsqueeze(-3)
    Y2 = Y.unsqueeze(-2)
    dominates = (Y1 >= Y2).all(dim=-1) & (Y1 > Y2).any(dim=-1)
    nd_mask = ~(dominates.any(dim=-1))
    if deduplicate:
        # remove duplicates
        # find index of first occurrence  of each unique element
        indices = (Y1 == Y2).all(dim=-1).long().argmax(dim=-1)
        keep = torch.zeros_like(nd_mask)
        keep.scatter_(dim=-1, index=indices, value=1.0)
        return nd_mask & keep
    return nd_mask