[Scikit-learn-general] feature selection algo

hm... interesting -- and there is no comparison against "minimizing
independence"? e.g. dCov measure
http://en.wikipedia.org/wiki/Distance_correlation which is really simple
to estimate and as intuitive as a correlation coefficient

Post by Satrajit Ghosh
fyi
---------- Forwarded message ----------
Date: Fri, Jun 15, 2012 at 12:35 AM
[2]http://jmlr.csail.mit.edu/papers/volume13/song12a/song12a.pdf
these guys define a nice nonlinear/nonparametric measure of correlation
that might be of interest to you.

Satrajit Ghosh

2012-06-15 14:10:37 UTC

hi yarik,

hm... interesting -- and there is no comparison against "minimizing

Post by Yaroslav Halchenko
independence"? e.g. dCov measure
http://en.wikipedia.org/wiki/Distance_correlation which is really simple
to estimate and as intuitive as a correlation coefficient

thanks for bringing up dCov. have you had a chance to play with it (their R
package) [1] /try to implement it from their paper [2]. it seems like dCov,
from the paper doesn't apply to 2 vectors, but it can compare the
covariance of two datasets, where n_samples>1. their code also allows you
to enter two distance matrices into dCor and dCov and dCor(x,x) != dCor(x).

i would love if somebody can explain to me how dCor, dCov can be used for 2
random vectors.

all that said, i started implementing dCorr/dCov for sklearn and stopped as
i couldn't match my output with the R code (i didn't actually look at the
source there) and it seemed it wasn't going to be related to comparing two
samples.

cheers,

satra

[1] http://cran.r-project.org/web/packages/energy/index.html
[2] http://arxiv.org/pdf/0803.4101.pdf

correlation

Post by Satrajit Ghosh
that might be of interest to you.

--
Yaroslav O. Halchenko
Postdoctoral Fellow, Department of Psychological and Brain Sciences
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419
WWW: http://www.linkedin.com/in/yarik
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Yaroslav Halchenko

2012-06-15 14:19:28 UTC

I haven't had a chance to play with it extensively but I have a basic
implementation:
https://github.com/PyMVPA/PyMVPA/blob/master/mvpa2/misc/dcov.py
which still lacks statistical assessment, but provides dCov, dCor values

and yes -- it is "inherently multivariate", but since also could be
useful if applied in a univariate fashion, you can see those uv bool
arguments. Implementation as is is very memory hungry (even for
multivariate case... which shouldn't be the case)

Post by Satrajit Ghosh
hi yarik,
hm... interesting -- and there is no comparison against "minimizing
independence"? e.g. dCov measure
[1]http://en.wikipedia.org/wiki/Distance_correlation which is really
simple
to estimate and as intuitive as a correlation coefficient
thanks for bringing up dCov. have you had a chance to play with it (their
R package) [1] /try to implement it from their paper [2]. it seems like
dCov, from the paper doesn't apply to 2 vectors, but it can compare the
covariance of two datasets, where n_samples>1. their code also allows you
to enter two distance matrices into dCor and dCov and dCor(x,x) != dCor(x).
i would love if somebody can explain to me how dCor, dCov can be used for
2 random vectors.
all that said, i started implementing dCorr/dCov for sklearn and stopped
as i couldn't match my output with the R code (i didn't actually look at
the source there) and it seemed it wasn't going to be related to comparing
two samples.
cheers,
satra
[1]�[2]http://cran.r-project.org/web/packages/energy/index.html
[2]�[3]http://arxiv.org/pdf/0803.4101.pdf
�

Satrajit Ghosh

2012-06-15 14:30:04 UTC

hi yarik,

here is my attempt:

https://github.com/satra/scikit-learn/blob/enh/covariance/sklearn/covariance/distance_covariance.py

i'll look at your code in detail later today to understand the uv=True case.

cheers,

satra

Post by Yaroslav Halchenko
I haven't had a chance to play with it extensively but I have a basic
https://github.com/PyMVPA/PyMVPA/blob/master/mvpa2/misc/dcov.py
which still lacks statistical assessment, but provides dCov, dCor values
and yes -- it is "inherently multivariate", but since also could be
useful if applied in a univariate fashion, you can see those uv bool
arguments. Implementation as is is very memory hungry (even for
multivariate case... which shouldn't be the case)

Post by Satrajit Ghosh
hi yarik,
hm... interesting -- and there is no comparison against "minimizing
independence"? e.g. dCov measure
[1]http://en.wikipedia.org/wiki/Distance_correlation which is

really

Post by Satrajit Ghosh
simple
to estimate and as intuitive as a correlation coefficient
thanks for bringing up dCov. have you had a chance to play with it

(their

Post by Satrajit Ghosh
R package) [1] /try to implement it from their paper [2]. it seems

Post by Satrajit Ghosh
dCov, from the paper doesn't apply to 2 vectors, but it can compare

the

Post by Satrajit Ghosh
covariance of two datasets, where n_samples>1. their code also allows

you

Post by Satrajit Ghosh
to enter two distance matrices into dCor and dCov and dCor(x,x) != dCor(x).
i would love if somebody can explain to me how dCor, dCov can be used

for

Post by Satrajit Ghosh
2 random vectors.
all that said, i started implementing dCorr/dCov for sklearn and

stopped

Post by Satrajit Ghosh
as i couldn't match my output with the R code (i didn't actually look

Post by Satrajit Ghosh
the source there) and it seemed it wasn't going to be related to

comparing

Post by Satrajit Ghosh
two samples.
cheers,
satra
[1]ï¿œ[2]http://cran.r-project.org/web/packages/energy/index.html
[2]ï¿œ[3]http://arxiv.org/pdf/0803.4101.pdf
ï¿œ

Yaroslav Halchenko

2012-06-15 14:45:17 UTC

Post by Satrajit Ghosh
hi yarik,
[1]https://github.com/satra/scikit-learn/blob/enh/covariance/sklearn/covariance/distance_covariance.py
i'll look at your code in detail later today to understand the uv=True

it is just to compute dCo[vr] among each pair of variables (so
similar to what corrcoef does) instead of taking them all as a
multivariate pattern.

j***@gmail.com

2012-06-15 19:50:46 UTC

hi yarik,
[1]https://github.com/satra/scikit-learn/blob/enh/covariance/sklearn/covariance/distance_covariance.py
i'll look at your code in detail later today to understand the uv=True

it is just to compute dCo[vr] among each pair of variables (so
similar to what corrcoef does) instead of taking them all as a
multivariate pattern.

trying to see how well this works, here's my gist
https://gist.github.com/2938402

bretzel and cosine have essentially zero distance correlation ??

Josef

Post by Yaroslav Halchenko
--
Yaroslav O. Halchenko
Postdoctoral Fellow, Department of Psychological and Brain Sciences
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419
WWW: http://www.linkedin.com/in/yarik
------------------------------------------------------------------------------
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threat landscape has changed and how IT managers can respond. Discussions
will include endpoint security, mobile security and the latest in malware
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
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j***@gmail.com

2012-06-15 19:52:20 UTC

hi yarik,
[1]https://github.com/satra/scikit-learn/blob/enh/covariance/sklearn/covariance/distance_covariance.py
i'll look at your code in detail later today to understand the uv=True

it is just to compute dCo[vr] among each pair of variables (so
similar to what corrcoef does) instead of taking them all as a
multivariate pattern.

trying to see how well this works, here's my gist
https://gist.github.com/2938402

(univariate only)

Post by j***@gmail.com
bretzel and cosine have essentially zero distance correlation ??
Josef

Yaroslav Halchenko

2012-06-15 20:20:19 UTC

Here is a comparison to output of my code (marked with >):

0.00458652660079 0.788017364828 0.00700027844478 0.00483928213727

0.145564526722 0.480124905375 0.422482399359 0.217567496918

6.50616752373e-07 7.99461373461e-05 0.00700027844478 0.0094610687282

0.120884106118 0.249205123601 0.422482399359 0.556949542822

0.00760091429285 0.819325410184 0.00909659010031 0.0094610687282

0.108943679969 0.195731527474 0.556245690994 0.556949542822

9.30536435524e-08 9.96844926893e-06 0.00921024880111 0.0094610687282

0.0362155612112 0.0648617517611 0.559754044038 0.556949542822

;)

[1]https://github.com/satra/scikit-learn/blob/enh/covariance/sklearn/covariance/distance_covariance.py
i'll look at your code in detail later today to understand the uv=True

trying to see how well this works, here's my gist
https://gist.github.com/2938402

(univariate only)

Post by j***@gmail.com
bretzel and cosine have essentially zero distance correlation ??

j***@gmail.com

2012-06-15 20:42:00 UTC

0.00458652660079 0.788017364828 0.00700027844478 0.00483928213727

0.145564526722 0.480124905375 0.422482399359 0.217567496918

6.50616752373e-07 7.99461373461e-05 0.00700027844478 0.0094610687282

0.120884106118 0.249205123601 0.422482399359 0.556949542822

0.00760091429285 0.819325410184 0.00909659010031 0.0094610687282

0.108943679969 0.195731527474 0.556245690994 0.556949542822

9.30536435524e-08 9.96844926893e-06 0.00921024880111 0.0094610687282

0.0362155612112 0.0648617517611 0.559754044038 0.556949542822

I was only looking at wikipedia, (I dowloaded the papers a while ago
but never read them)

https://github.com/PyMVPA/PyMVPA/blob/master/mvpa2/misc/dcov.py#L160
looks like a double sum, but wikipedia only has one sum, elementwise product.

When you have a nice implementation, I will (try to) borrow it :)

Josef

;)

[1]https://github.com/satra/scikit-learn/blob/enh/covariance/sklearn/covariance/distance_covariance.py
i'll look at your code in detail later today to understand the uv=True

trying to see how well this works, here's my gist
https://gist.github.com/2938402

(univariate only)

Post by j***@gmail.com
bretzel and cosine have essentially zero distance correlation ??

Yaroslav Halchenko

2012-06-15 20:50:09 UTC

Post by j***@gmail.com
https://github.com/PyMVPA/PyMVPA/blob/master/mvpa2/misc/dcov.py#L160
looks like a double sum, but wikipedia only has one sum, elementwise product.

sorry -- I might be slow -- what sum? there is only an outer product in

160: Axy = Ax[:, None] * Ay[None, :]

Post by j***@gmail.com
When you have a nice implementation, I will (try to) borrow it :)

j***@gmail.com

2012-06-15 22:19:36 UTC

Post by j***@gmail.com
https://github.com/PyMVPA/PyMVPA/blob/master/mvpa2/misc/dcov.py#L160
looks like a double sum, but wikipedia only has one sum, elementwise product.

sorry -- I might be slow -- what sum? there is only an outer product in
160: Axy = Ax[:, None] * Ay[None, :]

I shouldn't read to fast, I didn't see that you vectorized uv.

(
And I found my silly mistake in the distance calculation while copying
from script to file.
compared to R which returns sqrt,

np.sqrt(dr) - 0.995387854787786

1.1102230246251565e-16
)

Josef

Post by j***@gmail.com
When you have a nice implementation, I will (try to) borrow it :)

do you mean I should remove all embedded comments? ;)
yeah -- could be improved -- I just tried to stay descriptive and
generic so it could be used for both mass-univariate stats (like
corrcoef) and multivariate. and pretty much it is just only a
difference in how distances computed ;) -- otherwise 100% the same
code.
--
Yaroslav O. Halchenko
Postdoctoral Fellow, Department of Psychological and Brain Sciences
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419
WWW: http://www.linkedin.com/in/yarik
------------------------------------------------------------------------------
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Exclusive live event will cover all the ways today's security and
threat landscape has changed and how IT managers can respond. Discussions
will include endpoint security, mobile security and the latest in malware
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
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xinfan meng

2012-06-15 13:46:16 UTC