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

[Gael Varoquaux]

On Fri, Aug 11, 2017 at 12:37:12PM -0400, Andreas Mueller wrote:
> I opened https://github.com/scikit-learn/scikit-learn/issues/9528

> I suggest to first error everywhere and then fix those for which it seems
> easy and worth it, as Joel said, probably mostly in decomposition.

> Though adding support even in a few places seems like dangerous feature
> creep.

I am trying to predent that I am offline and in vacations, so I shouldn't
answer. But I do have a clear cut opinion here.

I believe that we should decide _not_ to support complex data everywhere.
The reason is that the support for complex data will always be incomplete
and risks being buggy. Indeed, complex data is very infrequent in machine
learning (unlike with signal processing). Hence, it will recieve little
usage. In addition, many machine learning algorithms cannot easily be
adapted to complex data.

To manage user expectation and to ensure quality of the codebase, let us
error on complex data.

Should we move this discussion on the issue opened by Andy?

Gaël


> On 08/11/2017 03:16 AM, Raphael C wrote:
> >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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-- 
    Gael Varoquaux
    Researcher, INRIA Parietal
    NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
    Phone:  ++ 33-1-69-08-79-68
    http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux