Default normalisation function

[leanderweber]

For a lot of quantification metrics (only checked the faithfulness_metrics.py for this) the default seems to be

self.normalise = self.kwargs.get("normalise", True)
self.normalise_func` = self.kwargs.get("normalise_func", normalise_by_negative)

with normalise_by_negative being defined in normalise_func.py as

def normalise_by_negative(a: np.ndarray) -> np.ndarray:
    """Normalise relevance given a relevance matrix (r) [-1, 1]."""
    if a.min() >= 0.0:
        return a / a.max()
    if a.max() <= 0.0:
        return -a / a.min()
    return (a > 0.0) * a / a.max() - (a < 0.0) * a / a.min()

I think this type of normalisation (as default) may lead to some unexpected/unintended behavior for many metrics (RegionPerturbation, for instance), since it normalises positive and negative parts of an attribution map by different values, and then changes the sign of the negative parts (thus basically taking the abs()). I believe this changes not only the ordering of attribution values (as abs() would), but also their relative magnitudes.

For this reason, a better default may be either self.normalise = self.kwargs.get("normalise", False) or self.normalise_func = self.kwargs.get("normalise_func", normalise_by_max)` ?

[leanderweber]

nvm, missed the sign of the negatives