ENH: medcouple n log n (see #9570) #9571
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- added fast algorithm - added legacy multiplexing option - revised tests - suggested draft release note
also STY: more style fixes
also STY: multi line warning again
bashtage
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May 27, 2025
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bashtage
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Overall looking pretty good.
statsmodels/stats/stattools.py
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| ----- | ||
| This is a helper function for the O(N log N) medcouple algorithm. | ||
| """ | ||
| AW = sorted(zip(A, W), key=lambda x: x[0]) |
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Should these be ndarray at this point, in which case we could avoid the python zip and sorted, which would normally be much slower for even modest sized data?
statsmodels/stats/stattools.py
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| mid = (beg + end) // 2 | ||
| trial = AW[mid][0] | ||
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| wleft = sum(w for a, w in AW if a < trial) |
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These might also be slow since using Python objects.
statsmodels/stats/stattools.py
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| wleft = sum(w for a, w in AW if a < trial) | ||
| wright = sum(w for a, w in AW if a >= trial) | ||
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| if 2 * wleft > wtot: |
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Do we need some nan protection somwhere? Usually need to ensure that all values are non-nan which using boolean comp.
ENH: other numpy library usage. STY: docstring improvements.
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Thank you for your code critique. I made the following main changes:
Please let me know what else may be needed. |
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I am following up on issue #9570 (link to issue) by creating this pull request, which implements Medcouple in O(N log N) time.
Legacy functionality with O(N**2) time is preserved with the use of a flag
use_fast. This flag defaults to the new behavior.Please let me know if you have any questions, or if you need anything else.
Overview:
added fast algorithm
added legacy multiplexing option
revised tests (passed locally)
suggested draft release note
closes ENH: Medcouple in O(N Log N) time #9570
tests added / passed.
code/documentation is well formatted.
properly formatted commit message. See
NumPy's guide.