[Numpy-discussion] New numpy functions: filled, filled_like

[josef.pktd at gmail.com]

On Mon, Jan 14, 2013 at 11:15 AM, Olivier Delalleau <shish at keba.be> wrote:
> 2013/1/14 Matthew Brett <matthew.brett at gmail.com>:
>> Hi,
>>
>> On Mon, Jan 14, 2013 at 9:02 AM, Dave Hirschfeld
>> <dave.hirschfeld at gmail.com> wrote:
>>> Robert Kern <robert.kern <at> gmail.com> writes:
>>>
>>>>
>>>> >>> >
>>>> >>> > One alternative that does not expand the API with two-liners is to let
>>>> >>> > the ndarray.fill() method return self:
>>>> >>> >
>>>> >>> >   a = np.empty(...).fill(20.0)
>>>> >>>
>>>> >>> This violates the convention that in-place operations never return
>>>> >>> self, to avoid confusion with out-of-place operations. E.g.
>>>> >>> ndarray.resize() versus ndarray.reshape(), ndarray.sort() versus
>>>> >>> np.sort(), and in the broader Python world, list.sort() versus
>>>> >>> sorted(), list.reverse() versus reversed(). (This was an explicit
>>>> >>> reason given for list.sort to not return self, even.)
>>>> >>>
>>>> >>> Maybe enabling this idiom is a good enough reason to break the
>>>> >>> convention ("Special cases aren't special enough to break the rules. /
>>>> >>> Although practicality beats purity"), but it at least makes me -0 on
>>>> >>> this...
>>>> >>>
>>>> >>
>>>> >> I tend to agree with the notion that inplace operations shouldn't return
>>>> >> self, but I don't know if it's just because I've been conditioned this way.
>>>> >> Not returning self breaks the fluid interface pattern [1], as noted in a
>>>> >> similar discussion on pandas [2], FWIW, though there's likely some way to
>>>> >> have both worlds.
>>>> >
>>>> > Ah-hah, here's the email where Guide officially proclaims that there
>>>> > shall be no "fluent interface" nonsense applied to in-place operators
>>>> > in Python, because it hurts readability (at least for Dutch people
>>>> > ):
>>>> >   http://mail.python.org/pipermail/python-dev/2003-October/038855.html
>>>>
>>>> That's a statement about the policy for the stdlib, and just one
>>>> person's opinion. You, and numpy, are permitted to have a different
>>>> opinion.
>>>>
>>>> In any case, I'm not strongly advocating for it. It's violation of
>>>> principle ("no fluent interfaces") is roughly in the same ballpark as
>>>> np.filled() ("not every two-liner needs its own function"), so I
>>>> thought I would toss it out there for consideration.
>>>>
>>>> --
>>>> Robert Kern
>>>>
>>>
>>> FWIW I'm +1 on the idea. Perhaps because I just don't see many practical
>>> downsides to breaking the convention but I regularly see a big issue with there
>>> being no way to instantiate an array with a particular value.
>>>
>>> The one obvious way to do it is use ones and multiply by the value you want. I
>>> work with a lot of inexperienced programmers and I see this idiom all the time.
>>> It takes a fair amount of numpy knowledge to know that you should do it in two
>>> lines by using empty and setting a slice.
>>>
>>> In [1]: %timeit NaN*ones(10000)
>>> 1000 loops, best of 3: 1.74 ms per loop
>>>
>>> In [2]: %%timeit
>>>    ...: x = empty(10000, dtype=float)
>>>    ...: x[:] = NaN
>>>    ...:
>>> 10000 loops, best of 3: 28 us per loop
>>>
>>> In [3]: 1.74e-3/28e-6
>>> Out[3]: 62.142857142857146
>>>
>>>
>>> Even when not in the mythical "tight loop" setting an array to one and then
>>> multiplying uses up a lot of cycles - it's nearly 2 orders of magnitude slower
>>> than what we know they *should* be doing.
>>>
>>> I'm agnostic as to whether fill should be modified or new functions provided but
>>> I think numpy is currently missing this functionality and that providing it
>>> would save a lot of new users from shooting themselves in the foot performance-
>>> wise.
>>
>> Is this a fair summary?
>>
>> => fill(shape, val), fill_like(arr, val) - new functions, as proposed
>> For: readable, seems to fit a pattern often used, presence in
>> namespace may clue people into using the 'fill' rather than * val or +
>> val
>> Con: a very simple alias for a = ones(shape) ; a.fill(val), maybe
>> cluttering already full namespace.
>>
>> => empty(shape).fill(val) - by allowing return value from arr.fill(val)
>> For: readable
>> Con: breaks guideline not to return anything from in-place operations,
>> no presence in namespace means users may not find this pattern.
>>
>> => no new API
>> For : easy maintenance
>> Con : harder for users to discover fill pattern, filling a new array
>> requires two lines instead of one.
>>
>> So maybe the decision rests on:
>>
>> How important is it that users see these function names in the
>> namespace in order to discover the pattern "a = ones(shape) ;
>> a.fill(val)"?
>>
>> How important is it to obey guidelines for no-return-from-in-place?
>>
>> How important is it to avoid expanding the namespace?
>>
>> How common is this pattern?
>>
>> On the last, I'd say that the only common use I have for this pattern
>> is to fill an array with NaN.
>
> My 2 cts from a user perspective:
>
> - +1 to have such a function. I usually use numpy.ones * scalar
> because honestly, spending two lines of code for such a basic
> operations seems like a waste. Even if it's slower and potentially
> dangerous due to casting rules.
> - I think having a noun rather than a verb makes more sense since we
> have numpy.ones and numpy.zeros (and I always read "numpy.empty" as
> "give me an empty array", not "empty an array").
> - I agree the name collision with np.ma.filled is a problem. I have no
> better suggestion though at this point.

np.array_filled(shape, value, dtype)  ?
maybe more verbose, but unambiguous AFAICS

BTW
GAUSS http://en.wikipedia.org/wiki/GAUSS_(software)
also has zeros and ones. 1st release 1984

np.array_filled((100, 2), -999, int) ?

Josef


>
> -=- Olivier
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