Discussion:
ANN: scikit-learn 0.12
(too old to reply)
Andreas Mueller
2012-09-04 22:38:37 UTC
Permalink
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.

There has also been a lot of progress in documentation
and ease of use.

Details can be found on the what's new
<http://scikit-learn.org/stable/whats_new.html>page.

Sources and windows binaries are available on sourceforge,
through pypi (http://pypi.python.org/pypi/scikit-learn/0.12) or
can be installed directly using pip:

pip install -U scikit-learn


I want to thank all of the developers who made this release possible
and welcome our new contributors.

Keep on learning,
Andy
Zach Bastick
2012-09-04 22:50:13 UTC
Permalink
Congratulations to all the developers!

This should save a lot of time for people trying to find the new
features that were in the dev version but weren't in 0.11, like me :)

Cheers,

Zach
Post by Andreas Mueller
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the what's new
<http://scikit-learn.org/stable/whats_new.html>page.
Sources and windows binaries are available on sourceforge,
through pypi (http://pypi.python.org/pypi/scikit-learn/0.12) or
pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Andreas Mueller
2012-09-05 06:46:00 UTC
Permalink
Post by Zach Bastick
Congratulations to all the developers!
This should save a lot of time for people trying to find the new
features that were in the dev version but weren't in 0.11, like me :)
Cheers,
Zach
One particular improvement (not) just for you:
If you use the r2 score on single examples, an error
is raised instead of producing garbage output ;)
Lars Buitinck
2012-09-04 23:04:31 UTC
Permalink
Post by Andreas Mueller
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
Thanks for all the work, Andy!
--
Lars Buitinck
Scientific programmer, ILPS
University of Amsterdam
Olivier Grisel
2012-09-04 23:08:09 UTC
Permalink
Post by Lars Buitinck
Post by Andreas Mueller
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
Thanks for all the work, Andy!
+1
--
Olivier
http://twitter.com/ogrisel - http://github.com/ogrisel
Robert Layton
2012-09-04 23:18:11 UTC
Permalink
Post by Olivier Grisel
Post by Lars Buitinck
Post by Andreas Mueller
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
Thanks for all the work, Andy!
+1
--
Olivier
http://twitter.com/ogrisel - http://github.com/ogrisel
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Congratulations to all involved!
--
Public key at: http://pgp.mit.edu/ Search for this email address and select
the key from "2011-08-19" (key id: 54BA8735)
Satrajit Ghosh
2012-09-05 00:47:28 UTC
Permalink
Post by Lars Buitinck
Thanks for all the work, Andy!
+1

cheers,

satra
Andreas Mueller
2012-09-05 07:01:16 UTC
Permalink
Hey guys!
Thanks for all the acknowledgement.
See you on github ;)

Andy
Peter Prettenhofer
2012-09-05 07:48:02 UTC
Permalink
Post by Lars Buitinck
Post by Andreas Mueller
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
Thanks for all the work, Andy!
Indeed - thanks for all your effort, Andy- we owe you some beers!

best,
Peter
Post by Lars Buitinck
--
Lars Buitinck
Scientific programmer, ILPS
University of Amsterdam
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Peter Prettenhofer
Andreas Mueller
2012-09-05 07:54:23 UTC
Permalink
Post by Peter Prettenhofer
Post by Lars Buitinck
Post by Andreas Mueller
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
Thanks for all the work, Andy!
Indeed - thanks for all your effort, Andy- we owe you some beers!
I'll remind you next time I make it to Vienna. Or at the next sprint in
Paris?
Peter Prettenhofer
2012-09-05 07:58:27 UTC
Permalink
Post by Andreas Mueller
Post by Peter Prettenhofer
Post by Lars Buitinck
Post by Andreas Mueller
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
Thanks for all the work, Andy!
Indeed - thanks for all your effort, Andy- we owe you some beers!
I'll remind you next time I make it to Vienna. Or at the next sprint in
Paris?
Looking forward to that! I actually thought about joining the next
sprint - let me check the dates.
Post by Andreas Mueller
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Peter Prettenhofer
Peter Prettenhofer
2012-09-05 08:15:09 UTC
Permalink
Post by Peter Prettenhofer
Post by Andreas Mueller
Post by Peter Prettenhofer
Post by Lars Buitinck
Post by Andreas Mueller
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
Thanks for all the work, Andy!
Indeed - thanks for all your effort, Andy- we owe you some beers!
I'll remind you next time I make it to Vienna. Or at the next sprint in
Paris?
Looking forward to that! I actually thought about joining the next
sprint - let me check the dates.
I just realized that PyconFr starts next week - I don't think that I
can make it on such a short notice. What about PyconDe? I just saw
that you have a talk there! Should also be a great opportunity to
improve ones Cython kung-fu - Stefan Behnel gives a number of talks on
the topic.
Post by Peter Prettenhofer
Post by Andreas Mueller
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Peter Prettenhofer
--
Peter Prettenhofer
Andreas Mueller
2012-09-05 08:25:50 UTC
Permalink
Post by Peter Prettenhofer
Post by Peter Prettenhofer
Post by Andreas Mueller
Post by Peter Prettenhofer
Post by Lars Buitinck
Post by Andreas Mueller
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
Thanks for all the work, Andy!
Indeed - thanks for all your effort, Andy- we owe you some beers!
I'll remind you next time I make it to Vienna. Or at the next sprint in
Paris?
Looking forward to that! I actually thought about joining the next
sprint - let me check the dates.
I just realized that PyconFr starts next week - I don't think that I
can make it on such a short notice. What about PyconDe? I just saw
that you have a talk there! Should also be a great opportunity to
improve ones Cython kung-fu - Stefan Behnel gives a number of talks on
the topic.
As I give a talk, I guess I'll better make it ;)
It's just 20 minutes, though, I think.

I'll be there and if I find some people, I'd love to sprint.
Or we can win some kaggle competitions and drink some beers ;)
Peter Prettenhofer
2012-09-05 08:33:52 UTC
Permalink
Post by Andreas Mueller
Post by Peter Prettenhofer
Post by Peter Prettenhofer
Post by Andreas Mueller
Post by Peter Prettenhofer
Post by Lars Buitinck
Post by Andreas Mueller
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
Thanks for all the work, Andy!
Indeed - thanks for all your effort, Andy- we owe you some beers!
I'll remind you next time I make it to Vienna. Or at the next sprint in
Paris?
Looking forward to that! I actually thought about joining the next
sprint - let me check the dates.
I just realized that PyconFr starts next week - I don't think that I
can make it on such a short notice. What about PyconDe? I just saw
that you have a talk there! Should also be a great opportunity to
improve ones Cython kung-fu - Stefan Behnel gives a number of talks on
the topic.
As I give a talk, I guess I'll better make it ;)
It's just 20 minutes, though, I think.
I'll be there and if I find some people, I'd love to sprint.
Or we can win some kaggle competitions and drink some beers ;)
sounds good - count me in!
Post by Andreas Mueller
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Peter Prettenhofer
Andreas Mueller
2012-09-05 08:16:04 UTC
Permalink
Post by Peter Prettenhofer
Post by Andreas Mueller
Post by Peter Prettenhofer
Post by Lars Buitinck
Post by Andreas Mueller
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
Thanks for all the work, Andy!
Indeed - thanks for all your effort, Andy- we owe you some beers!
I'll remind you next time I make it to Vienna. Or at the next sprint in
Paris?
Looking forward to that! I actually thought about joining the next
sprint - let me check the dates.
:) Well I can't make it to the one in err one or two weeks as I'm still
in Cambridge. But I'm sure I can make it to the next.
bthirion
2012-09-05 05:50:20 UTC
Permalink
Congrats ! And again, thanks, Andy,

B
Post by Andreas Mueller
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the what's new
<http://scikit-learn.org/stable/whats_new.html>page.
Sources and windows binaries are available on sourceforge,
through pypi (http://pypi.python.org/pypi/scikit-learn/0.12) or
pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
Alexandre Gramfort
2012-09-05 07:14:26 UTC
Permalink
congrats sklearners and Andy for pulling this off !

Alex
Post by bthirion
Congrats ! And again, thanks, Andy,
B
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the what's new page.
Sources and windows binaries are available on sourceforge,
through pypi (http://pypi.python.org/pypi/scikit-learn/0.12) or
pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Gilles Louppe
2012-09-05 09:40:51 UTC
Permalink
Congratulations! Thanks everyone for the good work!
Post by bthirion
Congrats ! And again, thanks, Andy,
B
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the what's new page.
Sources and windows binaries are available on sourceforge,
through pypi (http://pypi.python.org/pypi/scikit-learn/0.12) or
pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
Matthieu Brucher
2012-09-05 06:08:15 UTC
Permalink
Excellent work!
I have a question on MDS. Is it the classic MDS or something else? (Asking
the question as PCA is the classic MDS). It seems to be when the distance
matrix is Euclidean?

Cheers,

Matthieu
Post by Andreas Mueller
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the what's new
<http://scikit-learn.org/stable/whats_new.html>page.
Sources and windows binaries are available on sourceforge,
through pypi (http://pypi.python.org/pypi/scikit-learn/0.12) or
pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Information System Engineer, Ph.D.
Blog: http://matt.eifelle.com
LinkedIn: http://www.linkedin.com/in/matthieubrucher
Music band: http://liliejay.com/
Andreas Mueller
2012-09-05 06:49:53 UTC
Permalink
Hi Matthieu.
The release contains metric and non-metric MDS.
Details can be found here:
http://scikit-learn.org/stable/modules/manifold.html#multidimensional-scaling
I don't think the implementation includes classical MDS,
as that would be redundant, like you said.

Hope that helps,
Andy
Post by Matthieu Brucher
Excellent work!
I have a question on MDS. Is it the classic MDS or something else?
(Asking the question as PCA is the classic MDS). It seems to be when
the distance matrix is Euclidean?
Cheers,
Matthieu
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the what's new
<http://scikit-learn.org/stable/whats_new.html>page.
Sources and windows binaries are available on sourceforge,
through pypi (http://pypi.python.org/pypi/scikit-learn/0.12) or
pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Information System Engineer, Ph.D.
Blog: http://matt.eifelle.com
LinkedIn: http://www.linkedin.com/in/matthieubrucher
Music band: http://liliejay.com/
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Matthieu Brucher
2012-09-05 07:02:08 UTC
Permalink
After checking the code, the metric MDS used here is the classic MDS (the
stress function is squared-sum of the discrepancies between original
distances and computed distances). I didn't check the speed yet (currently
on road), but the implementation may benefit from using directly PCA (just
like Isomap did, as Isomap is just an classic MDS with a special input
distance matrix).

I'll try to see if I have the time to do a comparison. Perhaps the current
implementation is faster! In that case, Isomap may be enhanced by MDS.

Cheers,

Matthieu
Post by Andreas Mueller
Hi Matthieu.
The release contains metric and non-metric MDS.
http://scikit-learn.org/stable/modules/manifold.html#multidimensional-scaling
I don't think the implementation includes classical MDS,
as that would be redundant, like you said.
Hope that helps,
Andy
Excellent work!
I have a question on MDS. Is it the classic MDS or something else? (Asking
the question as PCA is the classic MDS). It seems to be when the distance
matrix is Euclidean?
Cheers,
Matthieu
Post by Andreas Mueller
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the what's new
<http://scikit-learn.org/stable/whats_new.html>page.
Sources and windows binaries are available on sourceforge,
through pypi (http://pypi.python.org/pypi/scikit-learn/0.12) or
pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Information System Engineer, Ph.D.
Blog: http://matt.eifelle.com
LinkedIn: http://www.linkedin.com/in/matthieubrucher
Music band: http://liliejay.com/
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Information System Engineer, Ph.D.
Blog: http://matt.eifelle.com
LinkedIn: http://www.linkedin.com/in/matthieubrucher
Music band: http://liliejay.com/
Nelle Varoquaux
2012-09-05 07:04:25 UTC
Permalink
Post by Matthieu Brucher
Excellent work!
I have a question on MDS. Is it the classic MDS or something else? (Asking
the question as PCA is the classic MDS). It seems to be when the distance
matrix is Euclidean?
It is indeed the classical MDS. When the whole similarity matrix, indeed,
the classical MDS is equivalent to a PCA. But when there a missing values,
PCA don't work.
An improvement to do to the current implementation is to use the PCA when
the whole matrix is provided. That should speed it up a lot.

Cheers,
N
Post by Matthieu Brucher
Cheers,
Matthieu
Post by Andreas Mueller
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the what's new
<http://scikit-learn.org/stable/whats_new.html>page.
Sources and windows binaries are available on sourceforge,
through pypi (http://pypi.python.org/pypi/scikit-learn/0.12) or
pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Information System Engineer, Ph.D.
Blog: http://matt.eifelle.com
LinkedIn: http://www.linkedin.com/in/matthieubrucher
Music band: http://liliejay.com/
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Matthieu Brucher
2012-09-05 07:28:29 UTC
Permalink
Post by Nelle Varoquaux
Post by Matthieu Brucher
Excellent work!
I have a question on MDS. Is it the classic MDS or something else?
(Asking the question as PCA is the classic MDS). It seems to be when the
distance matrix is Euclidean?
It is indeed the classical MDS. When the whole similarity matrix, indeed,
the classical MDS is equivalent to a PCA. But when there a missing values,
PCA don't work.
An improvement to do to the current implementation is to use the PCA when
the whole matrix is provided. That should speed it up a lot.
Cheers,
N
Hi Nelle,

Thanks for the intel, I forgot it could handle sparse matrices.
One additional question: is it possible to have details on the minimized
cost function? As there are thousands of different possibilities, sometimes
dealing with outiliers (ans so shortcuts), small values...

Cheers,

Matthieu
--
Information System Engineer, Ph.D.
Blog: http://matt.eifelle.com
LinkedIn: http://www.linkedin.com/in/matthieubrucher
Music band: http://liliejay.com/
Nelle Varoquaux
2012-09-05 07:31:38 UTC
Permalink
Post by Matthieu Brucher
Post by Nelle Varoquaux
Post by Matthieu Brucher
Excellent work!
I have a question on MDS. Is it the classic MDS or something else?
(Asking the question as PCA is the classic MDS). It seems to be when the
distance matrix is Euclidean?
It is indeed the classical MDS. When the whole similarity matrix, indeed,
the classical MDS is equivalent to a PCA. But when there a missing values,
PCA don't work.
An improvement to do to the current implementation is to use the PCA when
the whole matrix is provided. That should speed it up a lot.
Cheers,
N
Hi Nelle,
Thanks for the intel, I forgot it could handle sparse matrices.
One additional question: is it possible to have details on the minimized
cost function? As there are thousands of different possibilities, sometimes
dealing with outiliers (ans so shortcuts), small values...
Sure. I'll add that to the documentation during the code sprint. If I
remember correctly, I just use the raw stress, but I need to check that.

Cheers,
N
Post by Matthieu Brucher
Cheers,
Matthieu
--
Information System Engineer, Ph.D.
Blog: http://matt.eifelle.com
LinkedIn: http://www.linkedin.com/in/matthieubrucher
Music band: http://liliejay.com/
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Matthieu Brucher
2012-09-05 07:36:17 UTC
Permalink
Post by Nelle Varoquaux
Post by Matthieu Brucher
Post by Nelle Varoquaux
Post by Matthieu Brucher
Excellent work!
I have a question on MDS. Is it the classic MDS or something else?
(Asking the question as PCA is the classic MDS). It seems to be when the
distance matrix is Euclidean?
It is indeed the classical MDS. When the whole similarity matrix,
indeed, the classical MDS is equivalent to a PCA. But when there a missing
values, PCA don't work.
An improvement to do to the current implementation is to use the PCA
when the whole matrix is provided. That should speed it up a lot.
Cheers,
N
Hi Nelle,
Thanks for the intel, I forgot it could handle sparse matrices.
One additional question: is it possible to have details on the minimized
cost function? As there are thousands of different possibilities, sometimes
dealing with outiliers (ans so shortcuts), small values...
Sure. I'll add that to the documentation during the code sprint. If I
remember correctly, I just use the raw stress, but I need to check that.
Cheers,
N
Thanks ;)

Matthieu
--
Information System Engineer, Ph.D.
Blog: http://matt.eifelle.com
LinkedIn: http://www.linkedin.com/in/matthieubrucher
Music band: http://liliejay.com/
0.12 release of scikit-learn
2012-09-05 17:10:03 UTC
Permalink
tag me ! push me !
--
scikit-learn
Andreas Müller
2012-09-05 17:36:03 UTC
Permalink
Sorry, forgot to tag the final version. Will be fixed in a minute.

----- Ursprüngliche Mail -----
Von: "0.12 release of scikit-learn" <***@onerussian.com>
An: scikit-learn-***@lists.sourceforge.net
Gesendet: Mittwoch, 5. September 2012 18:10:03
Betreff: Re: [Scikit-learn-general] ANN: scikit-learn 0.12

tag me ! push me !
--
scikit-learn
Yaroslav Halchenko
2012-09-05 19:53:28 UTC
Permalink
have anyone ran into similar a situation that documentation fails to
build since running examples while doing sphinx requires too much RAM (or how
many GB should be normally present? ;))? in my case I got:

[ 2489.091989] Out of memory: Kill process 17211 (sphinx-build) score 792 or sacrifice child
[ 2489.091994] Killed process 17211 (sphinx-build) total-vm:6721740kB, anon-rss:6397432kB, file-rss:0kB
[ 2489.092768] sphinx-build: page allocation failure: order:0, mode:0x20058

while doing

$> PYTHONPATH=/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.7/dist-packages MPLCONFIGDIR=/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/build HOME=/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/build sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.1.3
...
plotting plot_cluster_comparison.py

=========================================================
Comparing different clustering algorithms on toy datasets
=========================================================

This example aims at showing characteristics of different
clustering algorithms on datasets that are "interesting"
but still in 2D. The last dataset is an example of a 'null'
situation for clustering: the data is homogeneous, and
there is no good clustering.

While these examples give some intuition about the algorithms,
this intuition might not apply to very high dimensional data.

The results could be improved by tweaking the parameters for
each clustering strategy, for instance setting the number of
clusters for the methods that needs this parameter
specified. Note that affinity propagation has a tendency to
create many clusters. Thus in this example its two parameters
(damping and per-point preference) were set to to mitigate this
behavior.

/usr/lib/python2.7/dist-packages/scipy/stats/distributions.py:30: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
import vonmises_cython
/usr/lib/python2.7/dist-packages/scipy/stats/distributions.py:30: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility
import vonmises_cython
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio4.py:15: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
from mio_utils import squeeze_element, chars_to_strings
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio4.py:15: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility
from mio_utils import squeeze_element, chars_to_strings
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio5.py:96: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
from mio5_utils import VarReader5
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio5.py:96: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility
from mio5_utils import VarReader5
/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.7/dist-packages/sklearn/cluster/hierarchical.py:111: UserWarning: the number of connected components of the connectivity matrix is 2 > 1. Completing it to avoid stopping the tree early.
% n_components)
[2] 17621 killed PYTHONPATH= MPLCONFIGDIR= HOME= sphinx-build -b html -d _build/doctrees .
PYTHONPATH= MPLCONFIGDIR= HOME= sphinx-build -b html -d _build/doctrees . 34,94s user 5,33s system 78% cpu 51,201 total

that happened with numpy 1.7.0b1 ... now I will try with the stable numpy
1.6.2.

NB. yet to check -- there was a failed test of 0.11 with this pre-release of numpy:
http://www.onerussian.com/Linux/deb/logs/python-numpy_1.7.0~b1-1_amd64.testrdepends.debian-sid/scikit-learn_0.11.0-2_amd64.build
look by the end

Cheers,
Post by Andreas Mueller
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the [1]what's new page.
Sources and windows binaries are available on sourceforge,
through pypi ([2]http://pypi.python.org/pypi/scikit-learn/0.12) or
  pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
References
Visible links
1. http://scikit-learn.org/stable/whats_new.html
2. http://pypi.python.org/pypi/scikit-learn/0.12
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
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
Yaroslav Halchenko
2012-09-05 20:22:49 UTC
Permalink
indeed seems to build fine with numpy 1.6.2... so watchout -- if someone
has time to look into it, now would be a good time to raise concerns
if any exist regarding upcoming numpy 1.7 release.

Cheers
Post by Yaroslav Halchenko
have anyone ran into similar a situation that documentation fails to
build since running examples while doing sphinx requires too much RAM (or how
[ 2489.091989] Out of memory: Kill process 17211 (sphinx-build) score 792 or sacrifice child
[ 2489.091994] Killed process 17211 (sphinx-build) total-vm:6721740kB, anon-rss:6397432kB, file-rss:0kB
[ 2489.092768] sphinx-build: page allocation failure: order:0, mode:0x20058
while doing
$> PYTHONPATH=/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.7/dist-packages MPLCONFIGDIR=/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/build HOME=/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/build sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.1.3
...
plotting plot_cluster_comparison.py
=========================================================
Comparing different clustering algorithms on toy datasets
=========================================================
This example aims at showing characteristics of different
clustering algorithms on datasets that are "interesting"
but still in 2D. The last dataset is an example of a 'null'
situation for clustering: the data is homogeneous, and
there is no good clustering.
While these examples give some intuition about the algorithms,
this intuition might not apply to very high dimensional data.
The results could be improved by tweaking the parameters for
each clustering strategy, for instance setting the number of
clusters for the methods that needs this parameter
specified. Note that affinity propagation has a tendency to
create many clusters. Thus in this example its two parameters
(damping and per-point preference) were set to to mitigate this
behavior.
/usr/lib/python2.7/dist-packages/scipy/stats/distributions.py:30: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
import vonmises_cython
/usr/lib/python2.7/dist-packages/scipy/stats/distributions.py:30: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility
import vonmises_cython
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio4.py:15: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
from mio_utils import squeeze_element, chars_to_strings
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio4.py:15: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility
from mio_utils import squeeze_element, chars_to_strings
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio5.py:96: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
from mio5_utils import VarReader5
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio5.py:96: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility
from mio5_utils import VarReader5
/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.7/dist-packages/sklearn/cluster/hierarchical.py:111: UserWarning: the number of connected components of the connectivity matrix is 2 > 1. Completing it to avoid stopping the tree early.
% n_components)
[2] 17621 killed PYTHONPATH= MPLCONFIGDIR= HOME= sphinx-build -b html -d _build/doctrees .
PYTHONPATH= MPLCONFIGDIR= HOME= sphinx-build -b html -d _build/doctrees . 34,94s user 5,33s system 78% cpu 51,201 total
that happened with numpy 1.7.0b1 ... now I will try with the stable numpy
1.6.2.
http://www.onerussian.com/Linux/deb/logs/python-numpy_1.7.0~b1-1_amd64.testrdepends.debian-sid/scikit-learn_0.11.0-2_amd64.build
look by the end
Cheers,
Post by Andreas Mueller
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the [1]what's new page.
Sources and windows binaries are available on sourceforge,
through pypi ([2]http://pypi.python.org/pypi/scikit-learn/0.12) or
  pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
References
Visible links
1. http://scikit-learn.org/stable/whats_new.html
2. http://pypi.python.org/pypi/scikit-learn/0.12
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
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
Jake Vanderplas
2012-09-05 20:21:10 UTC
Permalink
I ran into this problem a few weeks ago on the clustering example - I
figured it was just due to my under-powered netbook. If you reduce
n_samples in plot_cluster_comparison.py (from 1500 to, say, 500), it
should run without a problem. Perhaps we should think about doing that
in master, so this problem doesn't come up?
Jake
Post by Yaroslav Halchenko
have anyone ran into similar a situation that documentation fails to
build since running examples while doing sphinx requires too much RAM (or how
[ 2489.091989] Out of memory: Kill process 17211 (sphinx-build) score 792 or sacrifice child
[ 2489.091994] Killed process 17211 (sphinx-build) total-vm:6721740kB, anon-rss:6397432kB, file-rss:0kB
[ 2489.092768] sphinx-build: page allocation failure: order:0, mode:0x20058
while doing
$> PYTHONPATH=/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.7/dist-packages MPLCONFIGDIR=/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/build HOME=/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/build sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.1.3
...
plotting plot_cluster_comparison.py
=========================================================
Comparing different clustering algorithms on toy datasets
=========================================================
This example aims at showing characteristics of different
clustering algorithms on datasets that are "interesting"
but still in 2D. The last dataset is an example of a 'null'
situation for clustering: the data is homogeneous, and
there is no good clustering.
While these examples give some intuition about the algorithms,
this intuition might not apply to very high dimensional data.
The results could be improved by tweaking the parameters for
each clustering strategy, for instance setting the number of
clusters for the methods that needs this parameter
specified. Note that affinity propagation has a tendency to
create many clusters. Thus in this example its two parameters
(damping and per-point preference) were set to to mitigate this
behavior.
/usr/lib/python2.7/dist-packages/scipy/stats/distributions.py:30: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
import vonmises_cython
/usr/lib/python2.7/dist-packages/scipy/stats/distributions.py:30: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility
import vonmises_cython
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio4.py:15: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
from mio_utils import squeeze_element, chars_to_strings
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio4.py:15: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility
from mio_utils import squeeze_element, chars_to_strings
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio5.py:96: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
from mio5_utils import VarReader5
/usr/lib/python2.7/dist-packages/scipy/io/matlab/mio5.py:96: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility
from mio5_utils import VarReader5
/home/yoh/deb/gits/build-area/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.7/dist-packages/sklearn/cluster/hierarchical.py:111: UserWarning: the number of connected components of the connectivity matrix is 2 > 1. Completing it to avoid stopping the tree early.
% n_components)
[2] 17621 killed PYTHONPATH= MPLCONFIGDIR= HOME= sphinx-build -b html -d _build/doctrees .
PYTHONPATH= MPLCONFIGDIR= HOME= sphinx-build -b html -d _build/doctrees . 34,94s user 5,33s system 78% cpu 51,201 total
that happened with numpy 1.7.0b1 ... now I will try with the stable numpy
1.6.2.
http://www.onerussian.com/Linux/deb/logs/python-numpy_1.7.0~b1-1_amd64.testrdepends.debian-sid/scikit-learn_0.11.0-2_amd64.build
look by the end
Cheers,
Post by Andreas Mueller
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the [1]what's new page.
Sources and windows binaries are available on sourceforge,
through pypi ([2]http://pypi.python.org/pypi/scikit-learn/0.12) or
pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
References
Visible links
1. http://scikit-learn.org/stable/whats_new.html
2. http://pypi.python.org/pypi/scikit-learn/0.12
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Andreas Mueller
2012-09-05 21:33:00 UTC
Permalink
Post by Jake Vanderplas
I ran into this problem a few weeks ago on the clustering example - I
figured it was just due to my under-powered netbook. If you reduce
n_samples in plot_cluster_comparison.py (from 1500 to, say, 500), it
should run without a problem. Perhaps we should think about doing that
in master, so this problem doesn't come up?
Jake
Hm is this specific to affinity propagation maybe?
+1 for lowering the number of samples if that helps.

I didn't have a problem with the 2gb on my laptop.
Yaroslav Halchenko
2012-09-05 21:58:12 UTC
Permalink
it might well was a buginess of numpy 1.7.0b1 (in my case I had 6GB
avail).
Post by Andreas Mueller
Post by Jake Vanderplas
I ran into this problem a few weeks ago on the clustering example - I
figured it was just due to my under-powered netbook. If you reduce
n_samples in plot_cluster_comparison.py (from 1500 to, say, 500), it
should run without a problem. Perhaps we should think about doing that
in master, so this problem doesn't come up?
Jake
Hm is this specific to affinity propagation maybe?
+1 for lowering the number of samples if that helps.
I didn't have a problem with the 2gb on my laptop.
--
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
Yaroslav Halchenko
2012-09-06 16:27:23 UTC
Permalink
I am consistently getting

======================================================================
ERROR: sklearn.cluster.tests.test_spectral.test_affinities
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/pymodules/python2.6/nose/case.py", line 183, in runTest
self.test(*self.arg)
File "/tmp/buildd/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.6/dist-packages/sklearn/cluster/tests/test_spectral.py", line 121, in test_affinities
labels = sp.fit(X).labels_
File "/tmp/buildd/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.6/dist-packages/sklearn/cluster/spectral.py", line 360, in fit
random_state=self.random_state, n_init=self.n_init)
File "/tmp/buildd/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.6/dist-packages/sklearn/cluster/spectral.py", line 218, in spectral_clustering
mode=mode, random_state=random_state)
File "/tmp/buildd/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.6/dist-packages/sklearn/cluster/spectral.py", line 122, in spectral_embedding
sigma=1.0, which='LM')
File "/tmp/buildd/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.6/dist-packages/sklearn/utils/arpack.py", line 1493, in _eigsh
symmetric=True, tol=tol)
File "/tmp/buildd/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.6/dist-packages/sklearn/utils/arpack.py", line 1031, in get_OPinv_matvec
return SpLuInv(A.tocsc()).matvec
File "/tmp/buildd/scikit-learn-0.12.0/debian/python-sklearn/usr/lib/python2.6/dist-packages/sklearn/utils/arpack.py", line 899, in __init__
self.M_lu = splu(M)
File "/usr/lib/python2.6/dist-packages/scipy/sparse/linalg/dsolve/linsolve.py", line 129, in splu
diag_pivot_thresh, drop_tol, relax, panel_size)
RuntimeError: Factor is exactly singular

----------------------------------------------------------------------
Ran 835 tests in 90.631s


on amd64 (ok on 32bit) Debian squeeze, Ubuntu 10.04 and 10.10 where
scipy's are:

scikit-learn_0.12.0-1~nd10.04+1_amd64.build:Unpacking python-scipy (from .../python-scipy_0.7.0-2ubuntu0.1_amd64.deb) ...
scikit-learn_0.12.0-1~nd10.10+1_amd64.build:Unpacking python-scipy (from .../python-scipy_0.7.2-2ubuntu1_amd64.deb) ...
scikit-learn_0.12.0-1~nd60+1_amd64.build:Unpacking python-scipy (from .../python-scipy_0.7.2+dfsg1-1+squeeze1~nd60+1_amd64.deb) ...


I guess the test should skip conditioned on those older versions of
scipy? or you have a better idea?
Post by Andreas Mueller
Dear fellow Pythonistas.
I am pleased to announce the release of scikit-learn 0.12.
This release adds several new features, for example
multidimensional scaling (MDS), multi-task Lasso
and multi-output decision and regression forests.
There has also been a lot of progress in documentation
and ease of use.
Details can be found on the [1]what's new page.
Sources and windows binaries are available on sourceforge,
through pypi ([2]http://pypi.python.org/pypi/scikit-learn/0.12) or
  pip install -U scikit-learn
I want to thank all of the developers who made this release possible
and welcome our new contributors.
Keep on learning,
Andy
References
Visible links
1. http://scikit-learn.org/stable/whats_new.html
2. http://pypi.python.org/pypi/scikit-learn/0.12
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
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
Andreas Müller
2012-09-06 16:35:11 UTC
Permalink
Hi Yaroslav.
Thanks for investigating.
This is a bit unexpected as it seems to work on jenkins.
Maybe this is 32bit?

We could also try to make the test more robust.
Nelle has been looking into the algorithms a bit lately,
maybe she has some ideas.

Otherwise just skip the test for the moment and we can open
an issue and try to fix it for the future.

We might also look into having another version build on jenkins.
Olivier, do you think that is possible?

Cheers,
Andy
Nelle Varoquaux
2012-09-06 16:38:03 UTC
Permalink
Hello !

We could also try to make the test more robust.
Post by Andreas Müller
Nelle has been looking into the algorithms a bit lately,
maybe she has some ideas.
No idea: I haven't tackled this one yet.
Post by Andreas Müller
Otherwise just skip the test for the moment and we can open
an issue and try to fix it for the future.
We might also look into having another version build on jenkins.
Olivier, do you think that is possible?
Cheers,
Andy
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Yaroslav Halchenko
2012-09-06 17:59:38 UTC
Permalink
ok
https://github.com/scikit-learn/scikit-learn/issues/1121
Post by Andreas Müller
Hi Yaroslav.
Thanks for investigating.
This is a bit unexpected as it seems to work on jenkins.
Maybe this is 32bit?
We could also try to make the test more robust.
Nelle has been looking into the algorithms a bit lately,
maybe she has some ideas.
Otherwise just skip the test for the moment and we can open
an issue and try to fix it for the future.
We might also look into having another version build on jenkins.
Olivier, do you think that is possible?
Cheers,
Andy
--
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
Yaroslav Halchenko
2012-09-08 01:42:34 UTC
Permalink
meanwhile -- uploaded to Debian experimental and all releases on
NeuroDebian. While testing the installation I ran into a few issues
while sklearn.test():

* bulk of repeating deprecation warnings
* some failures from nose (I think similar ones were reported before to
be ignored -- correct me if I am wrong)
* failing doctests.

Here is the output I am getting:

$> ipython
impoPython 2.7.3 (default, Aug 26 2012, 11:57:48)
Type "copyright", "credits" or "license" for more information.

IPython 0.13 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
r
Welcome to pylab, a matplotlib-based Python environment [backend: TkAgg].
For more information, type 'help(pylab)'.

In [1]: import sklearn

In [2]: sklearn.test()
Running unit tests and doctests for sklearn
/usr/lib/python2.7/dist-packages/nose/util.py:14: DeprecationWarning: The compiler package is deprecated and removed in Python 3.x.
from compiler.consts import CO_GENERATOR
NumPy version 1.6.2
NumPy is installed in /usr/lib/pymodules/python2.7/numpy
Python version 2.7.3 (default, Aug 26 2012, 11:57:48) [GCC 4.7.1]
nose version 1.1.2
I: Seeding RNGs with 236262593
......................................................S.2.17927109077 55.4082834902
.......None
..............EE..SS.......................................................S......................................................SEE.............................................................................................................................EE........S......................---
...................................................................................................................................................../usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:267: UserWarning: Objective did not converge for target 0, you might want to increase the number of iterations
' to increase the number of iterations')
............../usr/lib/pymodules/python2.7/sklearn/metrics/cluster/supervised.py:775: RuntimeWarning: underflow encountered in double_scalars
emi += (term1[nij] * term2 * term3)
.../usr/lib/pymodules/python2.7/numpy/core/fromnumeric.py:2374: RuntimeWarning: invalid value encountered in double_scalars
return mean(axis, dtype, out)
........................................................................................................................................../usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVC is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVC instead
warnings.warn(msg, category=DeprecationWarning)
./usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVR is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVR instead
warnings.warn(msg, category=DeprecationWarning)
./usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
./usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVR is deprecated; to be removed in v0.14;
use sklearn.svm.SVR instead
warnings.warn(msg, category=DeprecationWarning)
............................/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
...../usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVR is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVR instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVR is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVR instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVR is deprecated; to be removed in v0.14;
use sklearn.svm.SVR instead
warnings.warn(msg, category=DeprecationWarning)
..../usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVC is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVC instead
warnings.warn(msg, category=DeprecationWarning)
...../usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
......../usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVC is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVR is deprecated; to be removed in v0.14;
use sklearn.svm.SVR instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVR is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVR instead
warnings.warn(msg, category=DeprecationWarning)
.....EEE............./usr/lib/pymodules/python2.7/sklearn/tree/tree.py:525: RuntimeWarning: divide by zero encountered in log
proba[k] = np.log(proba[k])
....FE..................................................../usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:597: UserWarning: precompute is ignored for sparse data
warnings.warn("precompute is ignored for sparse data")
/usr/lib/pymodules/python2.7/sklearn/qda.py:105: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
./usr/lib/pymodules/python2.7/sklearn/pls.py:284: UserWarning: X scores are null at iteration 1
warnings.warn('X scores are null at iteration %s' % k)
/usr/lib/pymodules/python2.7/sklearn/decomposition/nmf.py:497: UserWarning: Iteration limit reached during fit
warnings.warn("Iteration limit reached during fit")
/usr/lib/pymodules/python2.7/sklearn/decomposition/pca.py:337: DeprecationWarning: Using dim is deprecatedsince version 0.12, and backward compatibility won't be maintained from version 0.14 onward.
n_components = self.dim
..../usr/lib/pymodules/python2.7/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
neigh_dist, neigh_ind = self.kneighbors(X)
/usr/lib/pymodules/python2.7/sklearn/neighbors/classification.py:159: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
neigh_dist, neigh_ind = self.kneighbors(X)
/usr/lib/pymodules/python2.7/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
neigh_dist, neigh_ind = self.kneighbors(X)
/usr/lib/pymodules/python2.7/sklearn/neighbors/classification.py:159: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
neigh_dist, neigh_ind = self.kneighbors(X)
./usr/lib/pymodules/python2.7/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
neigh_dist, neigh_ind = self.kneighbors(X)
./usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:760: DeprecationWarning: Use alpha_. Using alpha is deprecatedsince version 0.12, and backward compatibility won't be maintained from version 0.14 onward.
DeprecationWarning, stacklevel=1)
/usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:760: DeprecationWarning: Use alpha_. Using alpha is deprecatedsince version 0.12, and backward compatibility won't be maintained from version 0.14 onward.
DeprecationWarning, stacklevel=1)
./usr/lib/pymodules/python2.7/sklearn/pls.py:62: UserWarning: Maximum number of iterations reached
warnings.warn('Maximum number of iterations reached')
/usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:760: DeprecationWarning: Use alpha_. Using alpha is deprecatedsince version 0.12, and backward compatibility won't be maintained from version 0.14 onward.
DeprecationWarning, stacklevel=1)
/usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:760: DeprecationWarning: Use alpha_. Using alpha is deprecatedsince version 0.12, and backward compatibility won't be maintained from version 0.14 onward.
DeprecationWarning, stacklevel=1)
............/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to beemoved in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SV is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DepretionWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utilsinit__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
...................................SSS....S....S...........................................................................................EE
======================================================================
ERROR: Doctest: sklearn.datasets.base.load_sample_image
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _

======================================================================
ERROR: Doctest: sklearn.datasets.base.load_sample_images
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _

======================================================================
ERROR: Doctest: sklearn.ensemble.gradient_boosting.GradientBoostingClassifier
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _

======================================================================
ERROR: Doctest: sklearn.ensemble.gradient_boosting.GradientBoostingRegressor
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _

======================================================================
ERROR: Doctest: sklearn.linear_model.randomized_l1.RandomizedLasso
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _

======================================================================
ERROR: Doctest: sklearn.linear_model.randomized_l1.RandomizedLogisticRegression
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _

======================================================================
ERROR: Doctest: sklearn.tree.tree.DecisionTreeClassifier
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttribuError: _

======================================================================
ERROR: Doctest: sklearn.tree.tree.DecisionTreeRegressor
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _

======================================================================
ERROR: Doctest: sklearn.tree.tree.export_graphviz
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _

======================================================================
ERROR: Doctest: sklearn.utils.extmath.pinvh
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _

======================================================================
ERROR: Doctest: sklearn._NoseTester.test
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _

======================================================================
ERROR: Doctest: sklearn.test
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _

======================================================================
FAIL: Doctest: sklearn.utils.extmath.pinvh
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/doctest.py", line 2201, in runTest
raise self.failureException(self.format_failure(new.getvalue()))
AssertionError: Failed doctest test for sklearn.utils.extmath.pinvh
File "/usr/lib/pymodules/python2.7/sklearn/utils/extmath.py", line 302, in pinvh

----------------------------------------------------------------------
File "/usr/lib/pymodules/python2.7/sklearn/utils/extmath.py", line 336, in sklearn.utils.extmath.pinvh
Failed example:
B = pinvh(a)
Exception raised:
Traceback (most recent call last):
File "/usr/lib/python2.7/doctest.py", line 1289, in __run
compileflags, 1) in test.globs
File "<doctest sklearn.utils.extmath.pinvh[3]>", line 1, in <module>
B = pinvh(a)
NameError: name 'pinvh' is not defined
----------------------------------------------------------------------
File "/usr/lib/pymodules/python2.7/sklearn/utils/extmath.py", line 337, in sklearn.utils.extmath.pinvh
Failed example:
allclose(a, dot(a, dot(B, a)))
Exception raised:
Traceback (most recent call last):
File "/usr/lib/python2.7/doctest.py", line 1289, in __run
compileflags, 1) in test.globs
File "<doctest sklearn.utils.extmath.pinvh[4]>", line 1, in <module>
allclose(a, dot(a, dot(B, a)))
NameError: name 'B' is not defined
----------------------------------------------------------------------
File "/usr/lib/pymodules/python2.7/sklearn/utils/extmath.py", line 339, in sklearn.utils.extmath.pinvh
Failed example:
allclose(B, dot(B, dot(a, B)))
Exception raised:
Traceback (most recent call last):
File "/usr/lib/python2.7/doctest.py", line 1289, in __run
compileflags, 1) in test.globs
File "<doctest sklearn.utils.extmath.pinvh[5]>", line 1, in <module>
allclose(B, dot(B, dot(a, B)))
NameError: name 'B' is not defined


----------------------------------------------------------------------
Ran 950 tests in 129.647s

FAILED (SKIP=11, errors=12, failures)
Out[2]: <nose.result.TextTestResult run=950 errors=12 failures=1>
Post by Yaroslav Halchenko
ok
https://github.com/scikit-learn/scikit-learn/issues/1121
Post by Andreas Müller
Hi Yaroslav.
Thanks for investigating.
This is a bit unexpected as it seems to work on jenkins.
Maybe this is 32bit?
We could also try to make the test more robust.
Nelle has been looking into the algorithms a bit lately,
maybe she has some ideas.
Otherwise just skip the test for the moment and we can open
an issue and try to fix it for the future.
We might also look into having another version build on jenkins.
Olivier, do you think that is possible?
Cheers,
Andy
--
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
Olivier Grisel
2012-09-08 09:56:38 UTC
Permalink
Post by Yaroslav Halchenko
meanwhile -- uploaded to Debian experimental and all releases on
NeuroDebian. While testing the installation I ran into a few issues
* bulk of repeating deprecation warnings
* some failures from nose (I think similar ones were reported before to
The attribute error failures are a nose bug and harmless. The remaining
failures on pinvh and co looks like real issues though.
--
Olivier
Aliabbas Petiwala
2012-09-08 14:57:24 UTC
Permalink
on installing using pip -U i got following error:

*error: Unable to find vcvarsall.bat*

i am on windows 7 64bit and py2.7 32bit pythonxy installed
Post by Olivier Grisel
Post by Yaroslav Halchenko
meanwhile -- uploaded to Debian experimental and all releases on
NeuroDebian. While testing the installation I ran into a few issues
* bulk of repeating deprecation warnings
* some failures from nose (I think similar ones were reported before to
The attribute error failures are a nose bug and harmless. The remaining
failures on pinvh and co looks like real issues though.
--
Olivier
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
*


Aliabbas Petiwala| Phd Scholar|Interdisciplinary Program in Education
Technology**|IIT *
**
**
*Bombay|+919664867707 | http://home.iitb.ac.in/~aliabbas/*
Brian Holt
2012-09-08 15:15:41 UTC
Permalink
Hi Aliabbas,

By coincidence I've just spent the last 2 hours debugging my windows
build and I've just finally got it sorted, so I can empathise with
you!

May I suggest that you download the Enthought 64bit distribution? It
comes with sklearn 0.11 already and works out of the box. You'll need
to supply your university email address and they will send you a link
to the windows 64bit version (for academic use).

If you want to develop on scikit-learn, then I'd still recommend the
EPD distribution but you'll want to do a few things:

1) run `enpkg -l` to list packages and `enpkg --remove x` to remove scikit-learn
2) install cygwin leaving out python (you've already got it in EPD)
3) in cygwin set `PATH=/cygdrive/c/Python27/Scripts:$PATH` to force
the use of the EPD gcc toolchain.
4) run `make`

@ogrisel I wonder if its worth adding this to the installation page?

Brian
Post by Aliabbas Petiwala
error: Unable to find vcvarsall.bat
i am on windows 7 64bit and py2.7 32bit pythonxy installed
Post by Olivier Grisel
Post by Yaroslav Halchenko
meanwhile -- uploaded to Debian experimental and all releases on
NeuroDebian. While testing the installation I ran into a few issues
* bulk of repeating deprecation warnings
* some failures from nose (I think similar ones were reported before to
The attribute error failures are a nose bug and harmless. The remaining
failures on pinvh and co looks like real issues though.
--
Olivier
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Aliabbas Petiwala| Phd Scholar|Interdisciplinary Program in Education
Technology|IIT
Bombay|+919664867707 | http://home.iitb.ac.in/~aliabbas/
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Aliabbas Petiwala
2012-09-08 15:21:31 UTC
Permalink
Thanks Brian
The following link saves the hassle :
http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn
Post by Brian Holt
Hi Aliabbas,
By coincidence I've just spent the last 2 hours debugging my windows
build and I've just finally got it sorted, so I can empathise with
you!
May I suggest that you download the Enthought 64bit distribution? It
comes with sklearn 0.11 already and works out of the box. You'll need
to supply your university email address and they will send you a link
to the windows 64bit version (for academic use).
If you want to develop on scikit-learn, then I'd still recommend the
1) run `enpkg -l` to list packages and `enpkg --remove x` to remove scikit-learn
2) install cygwin leaving out python (you've already got it in EPD)
3) in cygwin set `PATH=/cygdrive/c/Python27/Scripts:$PATH` to force
the use of the EPD gcc toolchain.
4) run `make`
@ogrisel I wonder if its worth adding this to the installation page?
Brian
Post by Aliabbas Petiwala
error: Unable to find vcvarsall.bat
i am on windows 7 64bit and py2.7 32bit pythonxy installed
Post by Olivier Grisel
Post by Yaroslav Halchenko
meanwhile -- uploaded to Debian experimental and all releases on
NeuroDebian. While testing the installation I ran into a few issues
* bulk of repeating deprecation warnings
* some failures from nose (I think similar ones were reported before
to
Post by Aliabbas Petiwala
Post by Olivier Grisel
The attribute error failures are a nose bug and harmless. The remaining
failures on pinvh and co looks like real issues though.
--
Olivier
------------------------------------------------------------------------------
Post by Aliabbas Petiwala
Post by Olivier Grisel
Live Security Virtual Conference
Exclusive live event will cover all the ways today's security and
threat landscape has changed and how IT managers can respond.
Discussions
Post by Aliabbas Petiwala
Post by Olivier Grisel
will include endpoint security, mobile security and the latest in
malware
Post by Aliabbas Petiwala
Post by Olivier Grisel
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Aliabbas Petiwala| Phd Scholar|Interdisciplinary Program in Education
Technology|IIT
Bombay|+919664867707 | http://home.iitb.ac.in/~aliabbas/
------------------------------------------------------------------------------
Post by Aliabbas Petiwala
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
*


Aliabbas Petiwala| Phd Scholar|Interdisciplinary Program in Education
Technology**|IIT *
**
**
*Bombay|+919664867707 | http://home.iitb.ac.in/~aliabbas/*
Aliabbas Petiwala
2012-09-08 15:23:19 UTC
Permalink
but that shortcut
http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learnshows runtime
errors for me.
Post by Aliabbas Petiwala
Thanks Brian
http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn
Post by Brian Holt
Hi Aliabbas,
By coincidence I've just spent the last 2 hours debugging my windows
build and I've just finally got it sorted, so I can empathise with
you!
May I suggest that you download the Enthought 64bit distribution? It
comes with sklearn 0.11 already and works out of the box. You'll need
to supply your university email address and they will send you a link
to the windows 64bit version (for academic use).
If you want to develop on scikit-learn, then I'd still recommend the
1) run `enpkg -l` to list packages and `enpkg --remove x` to remove scikit-learn
2) install cygwin leaving out python (you've already got it in EPD)
3) in cygwin set `PATH=/cygdrive/c/Python27/Scripts:$PATH` to force
the use of the EPD gcc toolchain.
4) run `make`
@ogrisel I wonder if its worth adding this to the installation page?
Brian
Post by Aliabbas Petiwala
error: Unable to find vcvarsall.bat
i am on windows 7 64bit and py2.7 32bit pythonxy installed
On Sat, Sep 8, 2012 at 3:26 PM, Olivier Grisel <
Post by Olivier Grisel
Post by Yaroslav Halchenko
meanwhile -- uploaded to Debian experimental and all releases on
NeuroDebian. While testing the installation I ran into a few issues
* bulk of repeating deprecation warnings
* some failures from nose (I think similar ones were reported before
to
Post by Aliabbas Petiwala
Post by Olivier Grisel
The attribute error failures are a nose bug and harmless. The remaining
failures on pinvh and co looks like real issues though.
--
Olivier
------------------------------------------------------------------------------
Post by Aliabbas Petiwala
Post by Olivier Grisel
Live Security Virtual Conference
Exclusive live event will cover all the ways today's security and
threat landscape has changed and how IT managers can respond.
Discussions
Post by Aliabbas Petiwala
Post by Olivier Grisel
will include endpoint security, mobile security and the latest in
malware
Post by Aliabbas Petiwala
Post by Olivier Grisel
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Aliabbas Petiwala| Phd Scholar|Interdisciplinary Program in Education
Technology|IIT
Bombay|+919664867707 | http://home.iitb.ac.in/~aliabbas/
------------------------------------------------------------------------------
Post by Aliabbas Petiwala
Live Security Virtual Conference
Exclusive live event will cover all the ways today's security and
threat landscape has changed and how IT managers can respond.
Discussions
Post by Aliabbas Petiwala
will include endpoint security, mobile security and the latest in
malware
Post by Aliabbas Petiwala
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
*
Aliabbas Petiwala| Phd Scholar|Interdisciplinary Program in Education
Technology**|IIT *
**
**
*Bombay|+919664867707 | http://home.iitb.ac.in/~aliabbas/*
--
*


Aliabbas Petiwala| Phd Scholar|Interdisciplinary Program in Education
Technology**|IIT *
**
**
*Bombay|+919664867707 | http://home.iitb.ac.in/~aliabbas/*
Aliabbas Petiwala
2012-09-08 15:29:49 UTC
Permalink
using windows installer works:
http://sourceforge.net/projects/scikit-learn/files/
but that shortcut http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learnshows runtime errors for me.
Post by Aliabbas Petiwala
Thanks Brian
http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn
Post by Brian Holt
Hi Aliabbas,
By coincidence I've just spent the last 2 hours debugging my windows
build and I've just finally got it sorted, so I can empathise with
you!
May I suggest that you download the Enthought 64bit distribution? It
comes with sklearn 0.11 already and works out of the box. You'll need
to supply your university email address and they will send you a link
to the windows 64bit version (for academic use).
If you want to develop on scikit-learn, then I'd still recommend the
1) run `enpkg -l` to list packages and `enpkg --remove x` to remove scikit-learn
2) install cygwin leaving out python (you've already got it in EPD)
3) in cygwin set `PATH=/cygdrive/c/Python27/Scripts:$PATH` to force
the use of the EPD gcc toolchain.
4) run `make`
@ogrisel I wonder if its worth adding this to the installation page?
Brian
Post by Aliabbas Petiwala
error: Unable to find vcvarsall.bat
i am on windows 7 64bit and py2.7 32bit pythonxy installed
On Sat, Sep 8, 2012 at 3:26 PM, Olivier Grisel <
Post by Olivier Grisel
Post by Yaroslav Halchenko
meanwhile -- uploaded to Debian experimental and all releases on
NeuroDebian. While testing the installation I ran into a few issues
* bulk of repeating deprecation warnings
* some failures from nose (I think similar ones were reported
before to
Post by Aliabbas Petiwala
Post by Olivier Grisel
The attribute error failures are a nose bug and harmless. The
remaining
Post by Aliabbas Petiwala
Post by Olivier Grisel
failures on pinvh and co looks like real issues though.
--
Olivier
------------------------------------------------------------------------------
Post by Aliabbas Petiwala
Post by Olivier Grisel
Live Security Virtual Conference
Exclusive live event will cover all the ways today's security and
threat landscape has changed and how IT managers can respond.
Discussions
Post by Aliabbas Petiwala
Post by Olivier Grisel
will include endpoint security, mobile security and the latest in
malware
Post by Aliabbas Petiwala
Post by Olivier Grisel
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
Aliabbas Petiwala| Phd Scholar|Interdisciplinary Program in Education
Technology|IIT
Bombay|+919664867707 | http://home.iitb.ac.in/~aliabbas/
------------------------------------------------------------------------------
Post by Aliabbas Petiwala
Live Security Virtual Conference
Exclusive live event will cover all the ways today's security and
threat landscape has changed and how IT managers can respond.
Discussions
Post by Aliabbas Petiwala
will include endpoint security, mobile security and the latest in
malware
Post by Aliabbas Petiwala
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
------------------------------------------------------------------------------
Live Security Virtual Conference
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/
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
--
*
Aliabbas Petiwala| Phd Scholar|Interdisciplinary Program in Education
Technology**|IIT *
**
**
*Bombay|+919664867707 | http://home.iitb.ac.in/~aliabbas/*
--
*
Aliabbas Petiwala| Phd Scholar|Interdisciplinary Program in Education
Technology**|IIT *
**
**
*Bombay|+919664867707 | http://home.iitb.ac.in/~aliabbas/*
--
*


Aliabbas Petiwala| Phd Scholar|Interdisciplinary Program in Education
Technology**|IIT *
**
**
*Bombay|+919664867707 | http://home.iitb.ac.in/~aliabbas/*
Andreas Mueller
2012-09-08 01:59:59 UTC
Permalink
Hi Yaroslav.
Thanks for the report.
I didn't know about the deprecation warnings.
For the other warnings: I think using the sklearn.test()
is a bad idea and using ``nosetests sklearn --exe``
should work better.

Also thanks for all the porting, it is really appreciated :)

Cheers,
Andy
Post by Yaroslav Halchenko
meanwhile -- uploaded to Debian experimental and all releases on
NeuroDebian. While testing the installation I ran into a few issues
* bulk of repeating deprecation warnings
* some failures from nose (I think similar ones were reported before to
be ignored -- correct me if I am wrong)
* failing doctests.
$> ipython
impoPython 2.7.3 (default, Aug 26 2012, 11:57:48)
Type "copyright", "credits" or "license" for more information.
IPython 0.13 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
r
Welcome to pylab, a matplotlib-based Python environment [backend: TkAgg].
For more information, type 'help(pylab)'.
In [1]: import sklearn
In [2]: sklearn.test()
Running unit tests and doctests for sklearn
/usr/lib/python2.7/dist-packages/nose/util.py:14: DeprecationWarning: The compiler package is deprecated and removed in Python 3.x.
from compiler.consts import CO_GENERATOR
NumPy version 1.6.2
NumPy is installed in /usr/lib/pymodules/python2.7/numpy
Python version 2.7.3 (default, Aug 26 2012, 11:57:48) [GCC 4.7.1]
nose version 1.1.2
I: Seeding RNGs with 236262593
......................................................S.2.17927109077 55.4082834902
.......None
..............EE..SS.......................................................S......................................................SEE.............................................................................................................................EE........S......................---
...................................................................................................................................................../usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:267: UserWarning: Objective did not converge for target 0, you might want to increase the number of iterations
' to increase the number of iterations')
............../usr/lib/pymodules/python2.7/sklearn/metrics/cluster/supervised.py:775: RuntimeWarning: underflow encountered in double_scalars
emi += (term1[nij] * term2 * term3)
.../usr/lib/pymodules/python2.7/numpy/core/fromnumeric.py:2374: RuntimeWarning: invalid value encountered in double_scalars
return mean(axis, dtype, out)
........................................................................................................................................../usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVC is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVC instead
warnings.warn(msg, category=DeprecationWarning)
./usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVR is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVR instead
warnings.warn(msg, category=DeprecationWarning)
./usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
./usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVR is deprecated; to be removed in v0.14;
use sklearn.svm.SVR instead
warnings.warn(msg, category=DeprecationWarning)
............................/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
...../usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVR is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVR instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVR is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVR instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVR is deprecated; to be removed in v0.14;
use sklearn.svm.SVR instead
warnings.warn(msg, category=DeprecationWarning)
..../usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVC is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVC instead
warnings.warn(msg, category=DeprecationWarning)
...../usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
......../usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVC is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVR is deprecated; to be removed in v0.14;
use sklearn.svm.SVR instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class NuSVR is deprecated; to be removed in v0.14;
use sklearn.svm.NuSVR instead
warnings.warn(msg, category=DeprecationWarning)
.....EEE............./usr/lib/pymodules/python2.7/sklearn/tree/tree.py:525: RuntimeWarning: divide by zero encountered in log
proba[k] = np.log(proba[k])
....FE..................................................../usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:597: UserWarning: precompute is ignored for sparse data
warnings.warn("precompute is ignored for sparse data")
/usr/lib/pymodules/python2.7/sklearn/qda.py:105: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
./usr/lib/pymodules/python2.7/sklearn/pls.py:284: UserWarning: X scores are null at iteration 1
warnings.warn('X scores are null at iteration %s' % k)
/usr/lib/pymodules/python2.7/sklearn/decomposition/nmf.py:497: UserWarning: Iteration limit reached during fit
warnings.warn("Iteration limit reached during fit")
/usr/lib/pymodules/python2.7/sklearn/decomposition/pca.py:337: DeprecationWarning: Using dim is deprecatedsince version 0.12, and backward compatibility won't be maintained from version 0.14 onward.
n_components = self.dim
..../usr/lib/pymodules/python2.7/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
neigh_dist, neigh_ind = self.kneighbors(X)
/usr/lib/pymodules/python2.7/sklearn/neighbors/classification.py:159: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
neigh_dist, neigh_ind = self.kneighbors(X)
/usr/lib/pymodules/python2.7/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
neigh_dist, neigh_ind = self.kneighbors(X)
/usr/lib/pymodules/python2.7/sklearn/neighbors/classification.py:159: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
neigh_dist, neigh_ind = self.kneighbors(X)
./usr/lib/pymodules/python2.7/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
neigh_dist, neigh_ind = self.kneighbors(X)
./usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:760: DeprecationWarning: Use alpha_. Using alpha is deprecatedsince version 0.12, and backward compatibility won't be maintained from version 0.14 onward.
DeprecationWarning, stacklevel=1)
/usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:760: DeprecationWarning: Use alpha_. Using alpha is deprecatedsince version 0.12, and backward compatibility won't be maintained from version 0.14 onward.
DeprecationWarning, stacklevel=1)
./usr/lib/pymodules/python2.7/sklearn/pls.py:62: UserWarning: Maximum number of iterations reached
warnings.warn('Maximum number of iterations reached')
/usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:760: DeprecationWarning: Use alpha_. Using alpha is deprecatedsince version 0.12, and backward compatibility won't be maintained from version 0.14 onward.
DeprecationWarning, stacklevel=1)
/usr/lib/pymodules/python2.7/sklearn/linear_model/coordinate_descent.py:760: DeprecationWarning: Use alpha_. Using alpha is deprecatedsince version 0.12, and backward compatibility won't be maintained from version 0.14 onward.
DeprecationWarning, stacklevel=1)
............/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to beemoved in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SV is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DepretionWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utilsinit__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
/usr/lib/pymodules/python2.7/sklearn/utils/__init__.py:62: DeprecationWarning: Class SVC is deprecated; to be removed in v0.14;
use sklearn.svm.SVC instead
warnings.warn(msg, category=DeprecationWarning)
...................................SSS....S....S...........................................................................................EE
======================================================================
ERROR: Doctest: sklearn.datasets.base.load_sample_image
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _
======================================================================
ERROR: Doctest: sklearn.datasets.base.load_sample_images
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _
======================================================================
ERROR: Doctest: sklearn.ensemble.gradient_boosting.GradientBoostingClassifier
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _
======================================================================
ERROR: Doctest: sklearn.ensemble.gradient_boosting.GradientBoostingRegressor
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _
======================================================================
ERROR: Doctest: sklearn.linear_model.randomized_l1.RandomizedLasso
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _
======================================================================
ERROR: Doctest: sklearn.linear_model.randomized_l1.RandomizedLogisticRegression
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _
======================================================================
ERROR: Doctest: sklearn.tree.tree.DecisionTreeClassifier
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttribuError: _
======================================================================
ERROR: Doctest: sklearn.tree.tree.DecisionTreeRegressor
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _
======================================================================
ERROR: Doctest: sklearn.tree.tree.export_graphviz
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _
======================================================================
ERROR: Doctest: sklearn.utils.extmath.pinvh
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _
======================================================================
ERROR: Doctest: sklearn._NoseTester.test
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _
======================================================================
ERROR: Doctest: sklearn.test
----------------------------------------------------------------------
File "/usr/lib/python2.7/dist-packages/nose/plugins/doctests.py", line 395, in tearDown
delattr(builtin_mod, self._result_var)
AttributeError: _
======================================================================
FAIL: Doctest: sklearn.utils.extmath.pinvh
----------------------------------------------------------------------
File "/usr/lib/python2.7/doctest.py", line 2201, in runTest
raise self.failureException(self.format_failure(new.getvalue()))
AssertionError: Failed doctest test for sklearn.utils.extmath.pinvh
File "/usr/lib/pymodules/python2.7/sklearn/utils/extmath.py", line 302, in pinvh
----------------------------------------------------------------------
File "/usr/lib/pymodules/python2.7/sklearn/utils/extmath.py", line 336, in sklearn.utils.extmath.pinvh
B = pinvh(a)
File "/usr/lib/python2.7/doctest.py", line 1289, in __run
compileflags, 1) in test.globs
File "<doctest sklearn.utils.extmath.pinvh[3]>", line 1, in <module>
B = pinvh(a)
NameError: name 'pinvh' is not defined
----------------------------------------------------------------------
File "/usr/lib/pymodules/python2.7/sklearn/utils/extmath.py", line 337, in sklearn.utils.extmath.pinvh
allclose(a, dot(a, dot(B, a)))
File "/usr/lib/python2.7/doctest.py", line 1289, in __run
compileflags, 1) in test.globs
File "<doctest sklearn.utils.extmath.pinvh[4]>", line 1, in <module>
allclose(a, dot(a, dot(B, a)))
NameError: name 'B' is not defined
----------------------------------------------------------------------
File "/usr/lib/pymodules/python2.7/sklearn/utils/extmath.py", line 339, in sklearn.utils.extmath.pinvh
allclose(B, dot(B, dot(a, B)))
File "/usr/lib/python2.7/doctest.py", line 1289, in __run
compileflags, 1) in test.globs
File "<doctest sklearn.utils.extmath.pinvh[5]>", line 1, in <module>
allclose(B, dot(B, dot(a, B)))
NameError: name 'B' is not defined
----------------------------------------------------------------------
Ran 950 tests in 129.647s
FAILED (SKIP=11, errors=12, failures)
Out[2]: <nose.result.TextTestResult run=950 errors=12 failures=1>
Post by Yaroslav Halchenko
ok
https://github.com/scikit-learn/scikit-learn/issues/1121
Post by Andreas Müller
Hi Yaroslav.
Thanks for investigating.
This is a bit unexpected as it seems to work on jenkins.
Maybe this is 32bit?
We could also try to make the test more robust.
Nelle has been looking into the algorithms a bit lately,
maybe she has some ideas.
Otherwise just skip the test for the moment and we can open
an issue and try to fix it for the future.
We might also look into having another version build on jenkins.
Olivier, do you think that is possible?
Cheers,
Andy
Yaroslav Halchenko
2012-09-10 23:29:28 UTC
Permalink
Post by Andreas Mueller
I didn't know about the deprecation warnings.
For the other warnings: I think using the sklearn.test()
is a bad idea and using ``nosetests sklearn --exe``
eh... I was just using nosetests -sv sklearn ... adding --exe indeed is
not a bad idea I guess
Post by Andreas Mueller
should work better.
well ... public API is public API! ;-)
Post by Andreas Mueller
Also thanks for all the porting, it is really appreciated :)
you are welcome! keep on great work!
--
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
Continue reading on narkive:
Loading...