On 09/29/2016 11:07 PM, Joel Nothman wrote:
(this has been in drafts a few days and I'm sure there's plenty I've
missed from the lists below)
Well done, everyone! The size of this release - and the group of
people that contributed to it - is even a bit overwhelming. Thanks for
managing the
Hi all,
Today I updated to the latest release of scikit-learn, and I went to test out
the LassoCV module in linear_model. I've tried both approaches below, and my
accuracy seems very poor, while using the same exact data with glmnet in R for
example will give me ~ 75% accuracy:
from sklearn
Actually, just to follow up on this, I believe I see what's wrong. I've
overlooked the fact that I'm attempting to compare classification from lasso in
the R glmnet package to lassocv here, and the linear model that supports
classification would be logistic regression in scikitlearn. Sorry!
Hi Roberto,
As answered previously, there is no multi-threading available for NMF in
scikit-learn.
However, if you want to compute *multiple* NMF in parallel, you can use
joblib with a threading backend,
as the 'cd' solver releases the GIL (through cython code) during a large
part of the time.
Th
I’m running nmf on a machine with 16 cores.
Is there an option to run nmf with multithreading? I know numpy does, but all I
see is one single process with 100% CPU usage.
Thanks,
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