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

2017-08-12 Thread Alexandre Gramfort 
I agree with Gaël on this. If you want to support complex values just
copy the estimators / functions you want and maintain them in a
separate package. +1 to error when complex are passed.
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Re: [scikit-learn] Truncated svd not working for complex matrices

2017-08-11 Thread 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  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 
> >>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 
> >>>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 
> 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.infohttp://twitter.com/GaelVaroquaux
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Re: [scikit-learn] Truncated svd not working for complex matrices

2017-08-11 Thread Andreas Mueller 

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.


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

2017-08-11 Thread 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  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 
> 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 
>> 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 
>> > 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
>> >>
>> >> ___
>> >> scikit-learn mailing list
>> >> scikit-learn@python.org
>> >> https://mail.python.org/mailman/listinfo/scikit-learn
>> >>
>> ___
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>> https://mail.python.org/mailman/listinfo/scikit-learn
>
>
>
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Re: [scikit-learn] Truncated svd not working for complex matrices

2017-08-10 Thread Joel Nothman 
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 
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 
> 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 
> 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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> >> scikit-learn@python.org
> >> https://mail.python.org/mailman/listinfo/scikit-learn
> >>
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Re: [scikit-learn] Truncated svd not working for complex matrices

2017-08-10 Thread Olivier Grisel 
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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