[Numpy-discussion] Asking for opinions: Priops

Friedrich Romstedt friedrichromstedt@gmail....
Mon Sep 27 09:40:37 CDT 2010


2010/9/23 Dag Sverre Seljebotn <dagss@student.matnat.uio.no>:
> Essentially, perhaps what you have sketched up + an ability to extend
> the graph with object conversion routes would be perfect for my own
> uses. So you can define a function with overloads (A, B) and (A, C), but
> also that objects of type D can be converted to C (and how). For
> instance, consider:
>
> np.array([1,2,3]) + [1,2,3]
>
> Here, list-> array could be handled through a defined coercion to array,
> rather than having to add an overload for list for every method taking
> an array.

This seems to be a good thing.  Let's reason about this for some
moment.  Atm, the relation is *not* transitive.  (I.e., (A, B) and (B,
C) does imply nothing for (A, C).  A, B, C classes.)  But such kind of
transitivity is what you mean, if (A, B) is defined and there is a
graph egde in the "conversion graph" (B, C), then (A, C) can choose
the (A, B) route via C -> B conversion; here the notation (B, C) in
the conversion graph means "conversion from C to B".

I don't see a clear solution at the end satisfying me.  It seems that
one really has to conduct a search in the additional conversion graph.
 Since this is expensive, I believe it would probably be good to
derive a "ConversionPriop" from priop.Priop.

What are your thoughts now?

I feel it useful to add this conversion graph, since it creates many
egdes in the resulting effective coercion graph, which do not have all
to be specified explicitly, but which exist.

Before, this conversion graph exists already in the sense of
subclassing, i.e., if your second object is a subclass of `list`, in
your example, it will be sufficient to define the edge with `list`,
and it will find the edge.  Would this suffice for your needs?  It
could be that this is even safer than magic conversion pathes.  Maybe
it's even better, more straightforward.

I'm really unsure about this.  Please give me your feedback.

> np.array([1,2,3]) + [1,2,3]

For this,

a) numpy.ndarray could be subclassing list, if it doesn't do already,
but then it would look like list concatenation.

b) It would be sufficient to add the edge with `list` as outlined before.

So, now I tend to *not* adding the functionality ... :-/

Maybe the strongest argument in favour of adding it nevertheless is,
that, in our example, ndarray can be seen at a "view" of a list.  So
operations with objects that can be seen as something else should be
supported, especially if the class it is seen as is more deep in the
class hierarchy.  This means, edges (ndarray, ndarray) in fact should
match on (ndarray, list), although `ndarray` is more deep than `list`.
 (Opposed to the case one defines (ndarray, list), and this matches
(ndarrary, list_subclass), seeing only the `list` functionality in
`list_subclass`.)

Would it, having this in mind, suffice to add a transitivity not only
for subclasses, but also for superclasses?  If (A, B) shall be coerced
and C is a subclass of B, i.e. B a superclass of C, then (A, B) should
translate to (A, C), using C(b) for the operand?  This means, C
*extends* the functionality of B only.  I'm feeling quite good with
this, because it's less arbitrary than allowing for *all* conversion
routes.  Actually it's nothing more than giving the class C as the
conversion function, since classes are callable.  And since not all
subclasses maybe need more than a superclass instance for construction
..., the graph still has to created manually.

Looking further into this, it seems to suffice classes which can be
constructed from their superclass instances only.  E.g. class C just
needs to be registered as an "extending class", and if in (A, B) it
holds that ``isinstance(b, C)`` while (A, B) is not defined but (A,
C), then the (A, C) edge will be called via the C(b) constructor.
Conversion would just happen using a *take* argument to the
registration, optionally being a string defining the kwarg used in the
C constructor for construction from base class instances.  E.g.:

priop.extender(numpy.ndarray, constructor=numpy.asarray, extends=list)
priop.extender(my_class, take='universal')
    # *universal* may be some late kwarg in the constructor.
    # Automatically applies to all classes `my_class` derives from.

Unfortunately, ndarray isn't a subclass of list.  Maybe this would be
worth a NEP, if it is technically possible.  Otherwise, the first form
would suffice it.

?

It got a bit longish,
Friedrich

P.S.: I like the fact that I understood that "conversion a->b" means
"B extends A".  As opposed to the fact "B functionality is a subset of
A functionality".


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