A bit late, but heartfelt congrats to Raghav :)
On Tue, Oct 4, 2016 at 12:43 PM Joel Nothman wrote:
> Congratulations, Raghav! Thanks for your dedication, as a student and
> mentor in GSoC, but at all other times too!
>
> On 4 October 2016 at 19:14, Jaques Grobler
> wrote:
>
> Congrats Raghav!
Hi,
After upgrading to scikit-learn 0.18 HashingVectorizer is about 10 times
slower.
Before:
scikit-learn 0.17. Numpy 1.11.2. Python 3.5.2 AMD64
Vectorizing 20newsgroup 11314 documents
Vectorization completed in 4.594092130661011 seconds, resulting shape
(11314, 1048576)
After upgrade:
scik
I cannot reproduce such a degradation on my machine:
(sklearn-0.17)ogrisel@is146148:~/code/scikit-learn$ python
~/tmp/bench_vectorizer.py
scikit-learn 0.17.1. Numpy 1.11.2. Python 3.5.0 x86_64
Vectorizing 20newsgroup 11314 documents
Vectorization completed in 4.033604383468628 seconds, resulting
Thank you for your response, have Windows 7 Enterprise 64 bit / Intel Xeon
E5 2640 CPU, same problem on two similar machines
python-3.5.2-amd64.exe - fresh installation
numpy-1.11.2+mkl-cp35-cp35m-win_amd64.whl - from Christoph Gohlke
scipy-0.18.1-cp35-cp35m-win_amd64.whl
pip install scikit-lean
Congratulations to all contributors!
I would like to update to the new version using conda, but apparently it is not
available:
~$ conda update scikit-learn
Fetching package metadata ...
Solving package specifications: ..
# All requested packages already installed.
# packages in env
Hi Piotr,
I've been there - most probably some package is blocking you to update via
numpy dependency. Try to update numpy first and the conflicting package
should pop up: "conda update numpy=1.11"
Pozdrawiam, | Best regards,
Maciek Wójcikowski
mac...@wojcikowski.pl
2016-10-11 14:32 GMT+0
Hi Maciek,
thank you very much! Numpy and opencv were indeed the conflicted packages.
Apperently my version of opencv was using numpy 1.10, so I uninstalled opencv,
updated numpy and updated scikit to 0.18.
Thank's for the fast help!
Best regards,
Piotr
On 11.10.2016 14:39, Maciek Wójcikowski
That's really weird. I don't have a windows machine handy at the
moment. It would be nice if someone else could confirm.
Could you please run the Python profiler on this to see where the time
is spent on the slow setup?
--
Olivier
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I just tested it on my Ubuntu machine and could not see any performance
issues (5.68 seconds in scikit-learn 0.17 vs. 6.67 seconds in
scikit-learn 0.18)
However, on another Windows 10 machine I could indeed see this issue:
scikit-learn 0.17.1. Numpy 1.11.1. Python 2.7.12 AMD64
Vectorizing 20new
Could it be a case of compilation: it seems to me that we are compiling
MKL vs non MKL builds.
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On Tue, Oct 11, 2016 at 10:49 PM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> Could it be a case of compilation: it seems to me that we are compiling
> MKL vs non MKL builds.
>
The hashing vectorizer is written in Cython and doesn't use BLAS, though.
Mathieu
Please open an issue on the issue tracker:
https://github.com/scikit-learn/scikit-learn/issues
On 10/11/2016 08:19 AM, Gabriel Trautmann wrote:
Thank you for your response, have Windows 7 Enterprise 64 bit / Intel
Xeon E5 2640 CPU, same problem on two similar machines
python-3.5.2-amd64.exe -
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