[scikit-learn] Truncated svd not working for complex matrices

[Raphael C]

Although the first priority should be correctness (in implementation
and documentation) and it makes sense to explicitly test for inputs
for which code will give the wrong answer, it would be great if we
could support complex data types, especially where it is very little
extra work.

Raphael

On 11 August 2017 at 05:41, Joel Nothman <joel.nothman at gmail.com> wrote:
> Should we be more explicitly forbidding complex data in most estimators, and
> perhaps allow it in a few where it is tested (particularly decomposition)?
>
> On 11 August 2017 at 01:08, André Melo <andre.nascimento.melo at gmail.com>
> wrote:
>>
>> Actually, it makes more sense to change
>>
>>     B = safe_sparse_dot(Q.T, M)
>>
>> To
>>     B = safe_sparse_dot(Q.T.conj(), M)
>>
>> On 10 August 2017 at 16:56, André Melo <andre.nascimento.melo at gmail.com>
>> wrote:
>> > Hi Olivier,
>> >
>> > Thank you very much for your reply. I was convinced it couldn't be a
>> > fundamental mathematical issue because the singular values were coming
>> > out exactly right, so it had to be a problem with the way complex
>> > values were being handled.
>> >
>> > I decided to look at the source code and it turns out the problem is
>> > when the following transformation is applied:
>> >
>> > U = np.dot(Q, Uhat)
>> >
>> > Replacing this by
>> >
>> > U = np.dot(Q.conj(), Uhat)
>> >
>> > solves the issue! Should I report this on github?
>> >
>> > On 10 August 2017 at 16:13, Olivier Grisel <olivier.grisel at ensta.org>
>> > wrote:
>> >> I have no idea whether the randomized SVD method is supposed to work
>> >> for
>> >> complex data or not (from a mathematical point of view). I think that
>> >> all
>> >> scikit-learn estimators assume real data (or integer data for class
>> >> labels)
>> >> and our input validation utilities will cast numeric values to float64
>> >> by
>> >> default. This might be the cause of your problem. Have a look at the
>> >> source
>> >> code to confirm. The reference to the paper can also be found in the
>> >> docstring of those functions.
>> >>
>> >> --
>> >> Olivier
>> >>
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>
>
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