Hey thanks a lot.
This is more or less how I was planning to write it.
I'll try to use the same structure to make the two libraries closer.
Thanks a lot,
Luca
On Fri, Jul 10, 2015 at 1:48 PM, Raghav R V <rag...@gmail.com> wrote:
> Hi Luca!
>
> Like Michael pointed out it will be fixed by #4294
> <https://github.com/scikit-learn/scikit-learn/pull/4294> which introduces
> a new structure involving the split(X, y, labels) method...
>
> So essentially your CV class should look like -
>
> class SampleCV():
> def __init__(self, <data-independent parameters>):
> ...
> def split(X, y=None, labels=None):
> for i in range(iterations):
> yield test_split, train_split
>
> Hope this helps :)
>
> Thanks
>
> R
>
> On Fri, Jul 10, 2015 at 5:08 PM, Michael Eickenberg <
> michael.eickenb...@gmail.com> wrote:
>
>> Yes. This is an important design flaw.
>> I believe it is going to be fully addressed before 1.0 comes out.
>>
>> On Fri, Jul 10, 2015 at 1:29 PM, Luca Puggini <lucapug...@gmail.com>
>> wrote:
>>
>>> I am designing a cross validation module for the statsmodels library and
>>> I am taking inspiration from the sklearn one.
>>>
>>> The iterators in the cross validation module require n the number of
>>> samples as variable of the __init__ method.
>>>
>>> I am so wondering how can I pass for example cv=LeavePOut(p=5) to the
>>> LassoCV method.
>>>
>>> Is it required to know what will the size of the data be in the fit
>>> method?
>>>
>>>
>>> Let me know.
>>>
>>> Thanks,
>>> Luca
>>>
>>>
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