Add bias keyword argument to cov - #691
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Maybe you can review my PR instead:) |
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I hadn't seen your PR :D I'll take a look |
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Closing for #690 |
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* ENH: expose correction and weights parameters in cov Resolves #688. Adds `axis`, `correction`, `frequency_weights`, and `weights` to `cov`, giving users control over the degrees-of-freedom correction and the observation-axis / weighted variants that `numpy.cov` and `torch.cov` already support. Naming follows array-api conventions (`axis`, `correction`) rather than numpy's (`rowvar`, `bias`, `ddof`); the docstring includes a one-to-one mapping. The delegation moves observations to the last axis via `xp.moveaxis`, collapsing `rowvar` out of the backend dispatch — only `ddof` vs `correction` differs between branches. Dask's native `cov` forces `.compute()` on a lazy scalar when any weights are given, so weighted dask inputs fall through to the generic implementation, which is fully lazy. * MNT: drop device= in cov weights * STY: formatter * TST: add bias tests from #691 * Update _funcs.py Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com> * Update _delegation.py Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com> * MNT: rename weights params to fweights/aweights * ENH: validate weights shape in cov * MNT: address lucascolley review * MNT: move weights validation to generic cov * Update _funcs.py Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com> * DOC: explain non-integer correction use cases in cov Addresses review feedback (kgryte, betatim) that the motivation for allowing non-integer correction was not obvious from the docstring: weighted unbiased correction and autocorrelated data both require fractional values. * TST: cover weight validation error paths in cov Adds tests for the 1-D shape and length checks in the generic cov path. Raises the diff coverage for this PR from 93.33% to 100%. * Preserve torch autograd in the batched cov path The generic `cov` implementation called `xp.asarray(m)` on its input. For torch this detaches gradients and mutates the caller's tensor in place, so `cov` on a batched tensor (ndim > 2, which routes to the generic path) with `requires_grad=True` returned a detached result and silently zeroed the input's grad. The call is unnecessary: the delegation layer already guarantees `m` is an array (it calls `array_namespace(m)` and reads `m.ndim`). Drop it, and add a torch autograd regression test. * MNT: address cov review feedback * TYP: clarify array cast in cov warning test * lint * Apply suggestions from code review Co-authored-by: Lucas Colley <lucas.colley8@gmail.com> * MNT: address cov review comments - link numpy/torch functions with intersphinx in the cov docstring (torch added to the intersphinx mapping) - add canonical examples for correction, fweights and aweights - explain the int(correction) cast: torch.cov rejects integer-valued floats such as 1.0 at runtime, so typing.cast is not enough - simplify the integer-correction check to float(correction).is_integer() - comment that the NaN shape checks account for Dask reporting unknown dimensions as NaN instead of None --------- Co-authored-by: Quentin Barthélemy <q.barthelemy@gmail.com> Co-authored-by: Lucas Colley <lucas.colley8@gmail.com>
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I'd like to use
xpx.covin scikit-learn but we need thebiaskeyword argument.That is all :D