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

[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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> >> https://mail.python.org/mailman/listinfo/scikit-learn
> >>
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Re: [scikit-learn] Truncated svd not working for complex matrices

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

[André Melo]

Hello all,

I'm trying to use the randomized version of scikit-learn's
TruncatedSVD (although I'm actually calling the internal function
randomized_svd to get the actual u, s, v matrices). While it is
working fine for real matrices, for complex matrices I can't get back
the original matrix even though the singular values are exactly
correct:

>>> import numpy as np
>>> from sklearn.utils.extmath import randomized_svd
>>> N = 3
>>> a = np.random.rand(N, N)*(1 + 1j)
>>> u1, s1, v1 = np.linalg.svd(a)
>>> u2, s2, v2 = randomized_svd(a, n_components=N, n_iter=7)
>>> np.allclose(s1, s2)
True
>>> np.allclose(a, u1.dot(np.diag(s1)).dot(v1))
True
>>> np.allclose(a, u2.dot(np.diag(s2)).dot(v2))
False

Any idea what could be wrong?

Thank you!

Best regards,
Andre Melo
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