MNT Refactor _average_weighted_percentile to avoid double sort
#31775
+207
−115
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lucyleeow
commented
Jul 17, 2025
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| result = xp.where( | ||
| is_fraction_above, | ||
| array[percentile_in_sorted, col_indices], |
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I initially thought this should be percentile_plus_one_in_sorted as from the paper, when g>0, but but searchsorted defaults to left (equals is on the right), whereas the paper defined j <= pn < j+1searchsorted effectively gives i-1 < pn <= i whereas the paper had j <= pn < j+1. This means that when pn is greater than the LHS, searchsorted's i equals j+1, from the paper.
When the quantile exactly matches an index, searchsorted's i equals j, from the paper (as the equals is on opposite sides in paper vs searchsorted).
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Reference Issues/PRs
Supercedes #30945
What does this implement/fix? Explain your changes.
Refactor
_average_weighted_percentileso we are not just performing_weighted_percentiletwice, thus avoids sorting and computing cumulative sum twice.#30945 essentially uses the sorted indicies and calculates
_weighted_percentile(-array, 100-percentile_rank)- this was verbose and required computing cumulative sum again on the negative (you could have used symmetry to avoid computing cumulative sum in cases when fraction above is greater than 0 - i.e.,g>0from Hyndman and Fan)I've followed the Hyndman and Fan computation more closely and calculate
gand just usej+1(since we already knowj). This did make handling the case wherej+1had a sample weight of 0 (or when you have sample weight of 0 at the end of the array) more complex.Any other comments?