[SciPy-User] return "full_output" or how to stop throwing away already calculated results

josef.pktd@gmai... josef.pktd@gmai...
Tue Mar 9 23:32:48 CST 2010


On Tue, Mar 9, 2010 at 11:54 PM,  <josef.pktd@gmail.com> wrote:
> On Tue, Mar 9, 2010 at 10:37 PM, Rob Falck <robfalck@gmail.com> wrote:
>> Returning an object would be my preference as well, it seems more
>> pythonic.  Most optimizers should be able to return at a minimum
>>
>> result.x
>> result.objfun
>> result.iterations
>> result.exit_status
>>
>> Some optimizers have other useful data to return, such as the Lagrange
>> multiplers from slsqp.  Going down that path means probably breaking
>> current implementations of the optimizers, but doing it right would be
>> worth it, in my opinion.  We should also agree upon names for the
>> common attributes.
>
> If we create new functions, as David argued, then the old signature
> could still be kept.
>
> I was curious what the increase in call overhead is, and for an
> essentially empty function it would be up to 60%. However, since this
> is mainly for functions that do heavier work, this will not be really
> relevant. (It might add a little bit to my Monte Carlos or bootstrap,
> but this should be negligible), and we will gain if we don't have to
> redo some calculations.
>
> The main reason I liked the full output option instead of always
> returning a result instance, is that, if full_output is true, then
> additional results can be calculated, that I don't want when I just
> need a fast minimal result.

something like this might work, without any undesired calculations and
any break in current API, and return type is independent of
"full_output" flag. (short of going to a class with lazy evaluation)

def function2a():
    '''current function'''
    return np.arange(5)

new:

def function5a(full_output=True):
    '''current function rewritten to return Store'''
    result = Store()
    result.a = np.arange(5)
    if full_output:
        #results that require additional memory or additional calculations
        result.b = function0()
    return result

def function5():
    '''replacement for current function'''
    result = function5a(full_output=False)
    return result.a


The extra calculations might not apply for many functions, but I have
some in statsmodels for which I could also switch to this kind of
pattern instead of making the return type conditional on a keyword.

Josef

>
>>
>> On Tue, Mar 9, 2010 at 8:56 PM, David Warde-Farley <dwf@cs.toronto.edu> wrote:
>>>
>>> On 9-Mar-10, at 4:01 PM, josef.pktd@gmail.com wrote:
>>>
>>>> in statsmodels, I am switching more to the pattern when
>>>> full_output=True (or similar keyword) for a function, then all
>>>> intermediate results are returned, attached to a generic class
>>>> instance ( or could be a struct in matlab or a bunch for Gael,...)
>>>
>>> David Cournapeau pointed out (in the thread "anyone to look at #1402?"
>>> on the numpy-discussion list) that the wider Python community frowns
>>> on the type of the returned value depending on a boolean flag
>>> argument, preferring instead different function names that call some
>>> common (private) helper function. It provided some validation for my
>>> uncomfortable feelings about scipy.optimize methods that do this.
>>>
>>> I do think it's worth thinking about returning some sort of proxy
>>> object that can have attributes set to either 'None' or the
>>> appropriate values. It would certainly make code more readable (except
>>> in the situation where you use argument unpacking, but even then it
>>> isn't obvious to an outsider how crucial that full_output thing is).
>>>
>>> David
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User@scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>
>>
>>
>> --
>> - Rob Falck
>> _______________________________________________
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>> SciPy-User@scipy.org
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>>
>


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