On 11/12/2015 08:29 PM, Sebastian Raschka wrote:
I agree, this sounds like a very useful addition. Why I was a bit
hesitant about adding it to the initial implementation was that I
was/am not sure what happens if, for example, in cases such as
cross_val_score w. n_jobs > -1
VotingClassifier w. n_jobs > -1
RandomForest w. n_jobs > -1
Using the code from cross_val_score, would this safely execute? I
think if the outer functions would use up all of the CPUs already, the
inner functions would basically run on a single CPU without throwing
errors!? I could try adding multiprocessing to the VotingClassifier, I
think that’s very useful in certain situations where the estimators
inside don’t use multiprocessing themselves. Or I could leave this
feature for you to implement if you like ;) ??
On Nov 11, 2015, at 8:27 PM, Scott Turner <[email protected]
<mailto:[email protected]>> wrote:
On Wed, Nov 11, 2015 at 6:18 PM,
<[email protected]
<mailto:[email protected]>> wrote:
@Scott: See
https://github.com/scikit-learn/scikit-learn/pull/5794 for
checking the VotingClassifier
It has also occurred to me that VotingClassifier ought to be capable
of using multiple CPUs to run the base classifiers, ala
cross_val_score. The code there is fairly easy to copy.
-- Scott
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Sorry for interruption, i can handle this if no one wants. I've already
started implementation of it 20 minutes ago :)
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