On 2018/11/10 12:39 PM, Stephan Hoyer wrote:
On Sat, Nov 10, 2018 at 2:22 PM Hameer Abbasi <einstein.edison@gmail.com <mailto:einstein.edison@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.
I don't know how people who currently use MaskedArray would feel about that. I would love to hear their thoughts.
Thank you. I am a user of masked arrays, and have been since pre-numpy days. I introduced their extensive use in matplotlib long ago. I have been a bit concerned, indeed, that all of the discussion of modifying masked arrays seems to be by people who don't actually use them explicitly (though they might be using them without knowing it via internal operations in matplotlib, or they might be quickly getting rid of them after they are yielded by netCDF4.Dataset()). I think that those of us who do use masked arrays recognize that they are not perfect; they have some quirks and gotchas, and one has to be careful to use numpy.ma functions instead of numpy functions in most cases. But we use them because they have real advantages over the alternatives, which are using nans and/or manually tracking independent masks throughout calculations. These advantages are largely because masked values *don't* behave like nan, *don't* propagate. This is fundamental to the design, and motivated by real-life use cases. The proposal to add a skipna kwarg to MaskedArray looks to me like it is giving purity priority over practicality. It will force ma users to insert skipna kwargs all over the place--because the default will be contrary to the primary purposes of using masked arrays, in most cases. How many people will it actually benefit? How many people are being bitten, and how badly, by masked array behavior? If there were a prospect of truly integrating missing/masked value handling into numpy, simplifying or phasing out numpy.ma, I would be delighted--I think it is the biggest single fundamental improvement that could be made, from the user's standpoint. I was sad to see Mark Wiebe's work in that direction come to grief. If there are ways of gradually improving numpy.ma and its interoperability with the rest of numpy and with the proliferation of duck arrays, I'm all in favor--so long as they don't effectively wreck numpy.ma for its present intended purposes. Eric
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