RFC: add support for `nan*` reductions

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#621 opened on Apr 18, 2023 28 comments 0 reactions 0 assignees View on GitHub

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Assessment

Difficulty: 5/5
Estimated time: Over a week
Newbie friendliness: 25/100

Issue type: Feature
Clarity: Needs clarification
Activity status: Stale
Tech stack: numpy, python, pytorch
Domain: data

Research direction

Start with the Array API min() specification linked in the issue and compare the stated nanmin examples across NumPy and Torch. Clarify the desired cross-backend behavior and whether this should become a specification change or guidance; done requires an agreed approach, documented semantics, and any corresponding standard updates.

Written by the indexing model from the issue text.

Description

API extension Needs Discussion RFC

Is there advice around handling NaNs and how to translate Numpy code to using the Array API?

In particular I have code like np.nanmin(X, axis=0) that I would like to rewrite so that it works with Numpy, Torch, etc arrays.

To me the "obvious" translation seems to be xp.min(X[~xp.isnan(X)], axis=0) but this doesn't work if X has a shape like (7500, 4) (here xp is the namespace of the array X). Another option I looked for is a where= argument to min(), but that doesn't exist unfortunately.

Does anyone have advice on this topic or knows if there is work planned on this?

Dominant language: Python
Stars: 281
Forks: 52
PR merge metrics: No merged PRs in 30d

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