On Sat, Nov 10, 2018 at 5:39 PM Stephan Hoyer <sho...@gmail.com> wrote:
> On Sat, Nov 10, 2018 at 2:22 PM Hameer Abbasi <einstein.edi...@gmail.com> > wrote: > >> To summarize, I think these are our options: >> >> 1. Change the behavior of np.anyarray() to check for an __anyarray__() >> protocol. Change np.matrix.__anyarray__() to return a base numpy array >> (this is a minor backwards compatibility break, but probably for the best). >> Start issuing a FutureWarning for any MaskedArray operations that violate >> Liskov and add a skipna argument that in the future will default to >> skipna=False. >> >> 2. Introduce a new coercion function, e.g., np.duckarray(). This is the >> easiest option because we don't need to cleanup NumPy's existing ndarray >> subclasses. >> >> >> My vote is still for 1. I don’t have an issue for PyData/Sparse depending >> on recent-ish NumPy versions — It’ll need a lot of the recent protocols >> anyway, although I could be convinced otherwise if major package devs >> (scikits, SciPy, Dask) were to weigh in and say they’ll jump on it (which >> seems unlikely given SciPy’s policy to support old NumPy versions). >> > > I agree that option (1) is fine for PyData/sparse. The bigger issue is > that this change should be conditional on making breaking changes (at least > raising FutureWarning for now) to np.ma.MaskedArray. > Might be good to try before worrying too much - MaskedArray already overrides *a lot*; it is not at all obvious to me that things wouldn't "just work" if we bulk-replaced `asarray` with `asanyarray`. And with `__array_function__` we now have the option to fix code paths that do not work immediately. -- Marten
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