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 -
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
Could it be a case of compilation: it seems to me that we are compiling
MKL vs non MKL builds.
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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
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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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
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
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
