I've been trying to figure out a way to effectively get the param/attrib
descriptions as to add them to the list
but the solution is evading me. I've uploaded a more recent version of the
script. *slightly *more tidy :)
It'd be nice if one could just see if there's a badly- or un-documented
attribute/param, as opposed to sifting through it by hand .. right now I'm
not finding a nice way to extract this. perhaps I'll have more brain-power
when I get home.
I'd appreciated any input on this. It seemed simpler in my head.
Good evening,
J
2012/10/29 Jaques Grobler <[email protected]>
> Thanks for the feedback Satraijit,
>
> I'll make an optional ad-don once I've tidied the mess :)
>
> Kind Regards
>
> 2012/10/29 Satrajit Ghosh <[email protected]>
>
>> hey jaques,
>>
>> this is really great! one useful thing to add, if possible, would be to
>> scrape the description of the parameter from the docstring.
>>
>> aside: if at some point in time, sklearn starts caring about provenance
>> [1], this is a really good start at simply collecting terms.
>>
>> cheers,
>>
>> satra
>>
>> [1]
>> http://dvcs.w3.org/hg/prov/raw-file/default/mh<http://dvcs.w3.org/hg/prov/raw-file/default/model/prov-dm.html>
>>
>>
>> On Mon, Oct 29, 2012 at 9:26 AM, Jaques Grobler
>> <[email protected]>wrote:
>>
>>> Hi everyone.
>>>
>>> I've put up a gist with a list of all the parameters, how many times and
>>> where they occur, as well as for all the attributes.
>>> The script that generates it is also in the gist - just know it's not
>>> the prettiest due to borrowing data-gen code from tests and debugging
>>> things-
>>> I'm still making it prettier - but I thought I'd just put this one up
>>> for now for in case people wanted to see where
>>> the numbers come from - this version of the script is pretty dogmatic
>>> and fat but it reads easily enough and gets the job done.
>>> The more important thing is the actual list anyway.
>>> Obviously it's pretty difficult to doublecheck these numbers manually,
>>> so if you see something fishy, let me know - there's a good chance
>>> theres a mistake somewhere or a forgotten estimator or two that I didn't
>>> see.
>>>
>>> Hopefully it can be used regarding API decisions etc.
>>> Have a look:
>>>
>>> gist:https://gist.github.com/3973308
>>>
>>> Note that GIST displays the script first for some reason, so just scroll
>>> past
>>>
>>>
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