On Sat, Jun 22, 2019 at 11:51 AM Marten van Kerkwijk < m.h.vankerkwijk@gmail.com> wrote:
Hi Allan,
I'm not sure I would go too much by what the old MaskedArray class did. It indeed made an effort not to overwrite masked values with a new result, even to the extend of copying back masked input data elements to the output data array after an operation. But the fact that this is non-sensical if the dtype changes (or the units in an operation on quantities) suggests that this mental model simply does not work.
I think a sensible alternative mental model for the MaskedArray class is that all it does is forward any operations to the data it holds and separately propagate a mask,
I'm generally on-board with that mental picture, and agree that the use-case described by Ben (different layers of satellite imagery) is important. Same thing happens in astronomy data, e.g. you have a CCD image of the sky and there are cosmic rays that contaminate the image. Those are not garbage data, just pixels that one wants to ignore in some, but not all, contexts. However, it's worth noting that one cannot blindly forward any operations to the data it holds since the operation may be illegal on that data. The simplest example is dividing `a / b` where `b` has data values of 0 but they are masked. That operation should succeed with no exception, and here the resultant value under the mask is genuinely garbage. The current MaskedArray seems a bit inconsistent in dealing with invalid calcuations. Dividing by 0 (if masked) is no problem and returns the numerator. Taking the log of a masked 0 gives the usual divide by zero RuntimeWarning and puts a 1.0 under the mask of the output. Perhaps the expression should not even be evaluated on elements where the output mask is True, and all the masked output data values should be set to a predictable value (e.g. zero for numerical, zero-length string for string, or maybe a default fill value). That at least provides consistent and predictable behavior that is simple to explain. Otherwise the story is that the data under the mask *might* be OK, unless for a particular element the computation was invalid in which case it is filled with some arbitrary value. I think that is actually an error-prone behavior that should be avoided. - Tom
ORing elements together for binary operations, etc., and explicitly skipping masked elements in reductions (ideally using `where` to be as agnostic as possible about the underlying data, for which, e.g., setting masked values to `0` for `np.reduce.add` may or may not be the right thing to do - what if they are string?).
With this mental picture, the underlying data are always have well-defined meaning: they have been operated on as if the mask did not exist. There then is also less reason to try to avoid getting it back to the user.
As a concrete example (maybe Ben has others): in astropy we have a sigma-clipping average routine, which uses a `MaskedArray` to iteratively mask items that are too far off from the mean; here, the mask varies each iteration (an initially masked element can come back into play), but the data do not.
All the best,
Marten
On Sat, Jun 22, 2019 at 10:54 AM Allan Haldane <allanhaldane@gmail.com> wrote:
On 6/21/19 2:37 PM, Benjamin Root wrote:
Just to note, data that is masked isn't always garbage. There are plenty of use-cases where one may want to temporarily apply a mask for a set of computation, or possibly want to apply a series of different masks to the data. I haven't read through this discussion deeply enough, but is this new class going to destroy underlying masked data? and will it be possible to swap out masks?
Cheers! Ben Root
Indeed my implementation currently feels free to clobber the data at masked positions and makes no guarantees not to.
I'd like to try to support reasonable use-cases like yours though. A few thoughts:
First, the old np.ma.MaskedArray explicitly does not promise to preserve masked values, with a big warning in the docs. I can't recall the examples, but I remember coming across cases where clobbering happens. So arguably your behavior was never supported, and perhaps this means that no-clobber behavior is difficult to reasonably support.
Second, the old np.ma.MaskedArray avoids frequent clobbering by making lots of copies. Therefore, in most cases you will not lose any performance in my new MaskedArray relative to the old one by making an explicit copy yourself. I.e, is it problematic to have to do
>>> result = MaskedArray(data.copy(), trial_mask).sum()
instead of
>>> marr.mask = trial_mask >>> result = marr.sum()
since they have similar performance?
Third, in the old np.ma.MaskedArray masked positions are very often "effectively" clobbered, in the sense that they are not computed. For example, if you do "c = a+b", and then change the mask of c, the values at masked position of the result of (a+b) do not correspond to the sum of the masked values in a and b. Thus, by "unmasking" c you are exposing nonsense values, which to me seems likely to cause heisenbugs.
In summary, by not making no-clobber guarantees and by strictly preventing exposure of nonsense values, I suspect that: 1. my new code is simpler and faster by avoiding lots of copies, and forces copies to be explicit in user code. 2. disallowing direct modification of the mask lowers the "API surface area" making people's MaskedArray code less buggy and easier to read: Exposure of nonsense values by "unmasking" is one less possibility to keep in mind.
Best, Allan
On Thu, Jun 20, 2019 at 12:44 PM Allan Haldane <allanhaldane@gmail.com <mailto:allanhaldane@gmail.com>> wrote:
On 6/19/19 10:19 PM, Marten van Kerkwijk wrote: > Hi Allan, > > This is very impressive! I could get the tests that I wrote for my class > pass with yours using Quantity with what I would consider very minimal > changes. I only could not find a good way to unmask data (I like the > idea of setting the mask on some elements via `ma[item] = X`); is this > on purpose?
Yes, I want to make it difficult for the user to access the garbage values under the mask, which are often clobbered values. The only way to "remove" a masked value is by replacing it with a new non-masked value.
> Anyway, it would seem easily at the point where I should comment on your > repository rather than in the mailing list!
To make further progress on this encapsulation idea I need a more complete ducktype to pass into MaskedArray to test, so that's what I'll work on next, when I have time. I'll either try to finish my ArrayCollection type, or try making a simple NDunit ducktype piggybacking on astropy's Unit.
Best, Allan
> > All the best, > > Marten > > > On Wed, Jun 19, 2019 at 5:45 PM Allan Haldane <allanhaldane@gmail.com <mailto:allanhaldane@gmail.com> > <mailto:allanhaldane@gmail.com <mailto:allanhaldane@gmail.com>>> wrote: > > On 6/18/19 2:04 PM, Marten van Kerkwijk wrote: > > > > > > On Tue, Jun 18, 2019 at 12:55 PM Allan Haldane > <allanhaldane@gmail.com <mailto:allanhaldane@gmail.com> <mailto:allanhaldane@gmail.com <mailto:allanhaldane@gmail.com>> > > <mailto:allanhaldane@gmail.com <mailto:allanhaldane@gmail.com> <mailto:allanhaldane@gmail.com <mailto:allanhaldane@gmail.com>>>> > wrote: > > <snip> > > > > > This may be too much to ask from the initializer, but, if > so, it still > > > seems most useful if it is made as easy as possible to do, > say, `class > > > MaskedQuantity(Masked, Quantity): <very few overrides>`. > > > > Currently MaskedArray does not accept ducktypes as underlying > arrays, > > but I think it shouldn't be too hard to modify it to do so. > Good idea! > > > > > > Looking back at my trial, I see that I also never got to duck arrays - > > only ndarray subclasses - though I tried to make the code as > agnostic as > > possible. > > > > (Trial at > > >
https://github.com/astropy/astropy/compare/master...mhvk:utils-masked-class?... )
> > > > I already partly navigated this mixin-issue in the > > "MaskedArrayCollection" class, which essentially does > > ArrayCollection(MaskedArray(array)), and only takes
about 30
> lines of > > boilerplate. That's the backwards encapsulation order
from
> what you want > > though. > > > > > > Yes, indeed, from a quick trial `MaskedArray(np.arange(3.) * u.m, > > mask=[True, False, False])` does indeed not have a `.unit` attribute > > (and cannot represent itself...); I'm not at all sure that
my
> method of > > just creating a mixed class is anything but a recipe for disaster, > though! > > Based on your suggestion I worked on this a little today, and now my > MaskedArray more easily encapsulates both ducktypes and
ndarray
> subclasses (pushed to repo). Here's an example I got working with masked > units using unyt: > > [1]: from MaskedArray import X, MaskedArray, MaskedScalar > > [2]: from unyt import m, km > > [3]: import numpy as np > > [4]: uarr = MaskedArray([1., 2., 3.]*km, mask=[0,1,0]) > > [5]: uarr > > MaskedArray([1., X , 3.]) > [6]: uarr + 1*m > > MaskedArray([1.001, X , 3.001]) > [7]: uarr.filled() > > unyt_array([1., 0., 3.], 'km') > [8]: np.concatenate([uarr, 2*uarr]).filled() > unyt_array([1., 0., 3., 2., 0., 6.], '(dimensionless)') > > The catch is the ducktype/subclass has to rigorously follow numpy's > indexing rules, including distinguishing 0d arrays from scalars. For now > only I used unyt in the example above since it happens to be less strict > about dimensionless operations than astropy.units which trips up my > repr code. (see below for example with astropy.units). Note in the last > line I lost the dimensions, but that is because unyt does not handle > np.concatenate. To get that to work we need a true ducktype for units. > > The example above doesn't expose the ".units" attribute outside the > MaskedArray, and it doesn't print the units in the repr. But you can > access them using "filled". > > While I could make MaskedArray forward unknown attribute accesses to the > encapsulated array, that seems a bit dangerous/bug-prone at
first
> glance, so probably I want to require the user to make a MaskedArray > subclass to do so. I've just started playing with that (probably buggy), > and Ive attached subclass examples for astropy.unit and unyt, with some > example output below. > > Cheers, > Allan > > > > Example using the attached astropy unit subclass: > > >>> from astropy.units import m, km, s > >>> uarr = MaskedQ(np.ones(3), units=km, mask=[0,1,0]) > >>> uarr > MaskedQ([1., X , 1.], units=km) > >>> uarr.units > km > >>> uarr + (1*m) > MaskedQ([1.001, X , 1.001], units=km) > >>> uarr/(1*s) > MaskedQ([1., X , 1.], units=km / s) > >>> (uarr*(1*m))[1:] > MaskedQ([X , 1.], units=km m) > >>> np.add.outer(uarr, uarr) > MaskedQ([[2., X , 2.], > [X , X , X ], > [2., X , 2.]], units=km) > >>> print(uarr) > [1. X 1.] km m > > Cheers, > Allan > > > > > Even if this impossible, I think it is conceptually
useful
> to think > > > about what the masking class should do. My sense is
that,
> e.g., it > > > should not attempt to decide when an operation succeeds or not, > > but just > > > "or together" input masks for regular, multiple-input functions, > > and let > > > the underlying arrays skip elements for reductions by using > `where` > > > (hey, I did implement that for a reason... ;-). In > particular, it > > > suggests one should not have things like domains and
all
> that (I never > > > understood why `MaskedArray` did that). If one wants
more,
> the class > > > should provide a method that updates the mask (a
sensible
> default > > might > > > be `mask |= ~np.isfinite(result)` - here, the class being masked > > should > > > logically support ufuncs and functions, so it can decide what > > "isfinite" > > > means). > > > > I agree it would be nice to remove domains. It would make life > easier, > > and I could remove a lot of twiddly code! I kept it in for now to > > minimize the behavior changes from the old MaskedArray. > > > > > > That makes sense. Could be separated out to a backwards-compatibility > > class later. > > > > > > > In any case, I would think that a basic truth should be that > > everything > > > has a mask with a shape consistent with the data, so > > > 1. Each complex numbers has just one mask, and setting > `a.imag` with a > > > masked array should definitely propagate the mask. > > > 2. For a masked array with structured dtype, I'd similarly say > > that the > > > default is for a mask to have the same shape as the
array.
> But that > > > something like your collection makes sense for the
case
> where one > > wants > > > to mask items in a structure. > > > > Agreed that we should have a single bool per complex or structured > > element, and the mask shape is the same as the array
shape.
> That's how I > > implemented it. But there is still a problem with complex.imag > > assignment: > > > > >>> a = MaskedArray([1j, 2, X]) > > >>> i = a.imag > > >>> i[:] = MaskedArray([1, X, 1]) > > > > If we make the last line copy the mask to the original array, what > > should the real part of a[2] be? Conversely, if we don't copy > the mask, > > what should the imag part of a[1] be? It seems like we
might
> "want" the > > masks to be OR'd instead, but then should i[2] be masked after > we just > > set it to 1? > > > > Ah, I see the issue now... Easiest to implement and closest in analogy > > to a regular view would be to just let it unmask a[2] (with > whatever is > > in real; user beware!). > > > > Perhaps better would be to special-case such that `imag` returns a > > read-only view of the mask. Making `imag` itself read-only
would
> prevent > > possibly reasonable things like `i[np.isclose(i, 0)] = 0` -
but
> there is > > no reason this should update the mask. > > > > Still, neither is really satisfactory... > > > > > > > > > p.s. I started trying to implement the above "Mixin" class; will > > try to > > > clean that up a bit so that at least it uses `where`
and
> push it up. > > > > I played with "where", but didn't include it since 1.17 is not > released. > > To avoid duplication of effort, I've attached a diff of what I > tried. I > > actually get a slight slowdown of about 10% by using where... > > > > > > Your implementation is indeed quite similar to what I got in > > __array_ufunc__ (though one should "&" the where with
~mask).
> > > > I think the main benefit is not to presume that whatever is underneath > > understands 0 or 1, i.e., avoid filling. > > > > > > If you make progress with the mixin, a push is welcome.
I
> imagine a > > problem is going to be that np.isscalar doesn't work to detect > duck > > scalars. > > > > I fear that in my attempts I've simply decided that only array scalars > > exist... > > > > -- Marten > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org <mailto:NumPy-Discussion@python.org> <mailto:NumPy-Discussion@python.org <mailto:NumPy-Discussion@python.org>> > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org <mailto:NumPy-Discussion@python.org> <mailto:NumPy-Discussion@python.org <mailto:NumPy-Discussion@python.org>> > https://mail.python.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org <mailto:NumPy-Discussion@python.org> > https://mail.python.org/mailman/listinfo/numpy-discussion >
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