[issue34227] Weighted random.sample() (weighted sampling without replacement)

[Raymond Hettinger]

Raymond Hettinger  added the comment:

Thank for the suggestion and patch, but I will decline.  To me, the reseviour 
style algorithm doesn't fit in well (with the slow sorting step and the 
frequent calls to the random in the key function). For large population sizes 
(which sample() was designed for), the API and algorithm perform badly.  For 
typical student "urn problems", the better approach is to pre-weight the 
population by the exact counts specified in the problem.  I recommend that you 
post a recipe for what you want to do, but I don't think the standard library 
is the place for it.

--
resolution:  -> rejected
stage: patch review -> resolved
status: open -> closed

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[issue34227] Weighted random.sample() (weighted sampling without replacement)

[Serhiy Storchaka]

Change by Serhiy Storchaka :


--
assignee:  -> rhettinger
nosy: +mark.dickinson, rhettinger
versions: +Python 3.8

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[issue34227] Weighted random.sample() (weighted sampling without replacement)

[Piotr Jurkiewicz]

Change by Piotr Jurkiewicz :


--
keywords: +patch
pull_requests: +7988
stage:  -> patch review

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[issue34227] Weighted random.sample() (weighted sampling without replacement)

[Piotr Jurkiewicz]

New submission from Piotr Jurkiewicz :

Function random.choices(), which appeared in Python 3.6, allows to perform 
weighted random sampling with replacement. Function random.sample() performs 
random sampling without replacement, but cannot do it weighted.

I propose to enhance random.sample() to perform weighted sampling. That way all 
four possibilities will be supported:

- non-weighted sampling with replacement: random.choices(..., weights=None) 
(exists)

- weighted sampling with replacement: random.choices(..., weights=weights) 
(exists)

- non-weighted sampling without replacement: random.sample(..., weights=None) 
(exists)

- weighted sampling without replacement: random.sample(..., weights=weights) 
(NEW)

Rationale:

Weighted sampling without replacement is a popular problem. There are lot of 
questions on StackOverflow and similar sites how to implement it. 
Unfortunately, many proposed solutions are wrong, for example:

https://stackoverflow.com/a/353510/2178047
https://softwareengineering.stackexchange.com/a/233552/161807

or have excessive computational complexity (e.g. quadratic). There are lot of 
suggestions to use numpy.random.choice() to do that, which supports all four 
possibilities with a single function:

numpy.random.choice(a, size=None, replace=True, p=None)

But of course this is an overkill to install numpy just to do that.

I think that this should be possible with stdlib, without the need to implement 
it by yourself or to install numpy. Especially, that it can be implemented in 2 
lines (plus 4 lines of error checking), as you can see in the PR.

--
components: Library (Lib)
messages: 322367
nosy: piotrjurkiewicz
priority: normal
severity: normal
status: open
title: Weighted random.sample() (weighted sampling without replacement)
type: enhancement

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[issue34227] Weighted random.sample() (weighted sampling without replacement)

[Pablo Galindo Salgado]

Change by Pablo Galindo Salgado :


--
nosy: +pablogsal

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