For RFE there is a specific test case where the coef_ is sparse. It's
messing things up since there does not seem to be a norm function for
sparse matrix in the current stable release of scipy. Should I define a
helper function that can compute the norm of a sparse matrix or just
convert coeff_ to a dense matrix ?
2016-01-13 20:36 GMT+01:00 Joel Nothman <joel.noth...@gmail.com>:
> safe_sqr applies when its operand may be a sparse matrix. In theory this
> could be true of coef_, but I don't think this is tested as often as it
> might be.
>
> But, in general, you should not take what is done in any particular piece
> of code to be indicative of best practice. There are often multiple ways to
> do things, and while there may be a best choice, the best choice may change
> over time as different parts of the codebase are atomically created or
> modified. And we'll generally not be *that *nitpicky in reviewing, more
> often focusing on readability, functionality and, where appropriate,
> efficiency. In short, I don't think you need to worry about this much.
>
> Supporting an arbitrary norm might be appropriate in RFE too.
>
> HTH
>
> - Joel
>
> On 14 January 2016 at 03:40, WENDLINGER Antoine <
> antoinewendlin...@gmail.com> wrote:
>
>> Hi everyone,
>>
>> I'm working on issue 2121
>> <https://github.com/scikit-learn/scikit-learn/issues/2121#issuecomment-171067587>,
>> and have trouble understanding when to use safe methods like safe_sqr. What
>> I would want to do here is use np.linalg.norm on the coeff array when it
>> is of dimension 2, but it seems it is not the way to go (since it's not
>> what is done for example in the RFE feature selector, where the l2 norm of
>> the coeff array is used). Am I missing something here ?
>>
>>
>> Regards,
>>
>> Antoine
>>
>>
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