"Second complexity does not > imply better prediction. " Complexity doesn't imply prediction? Perhaps you're having a translation error.
On Sat, Oct 12, 2019 at 2:04 PM <scikit-learn-requ...@python.org> wrote: > Send scikit-learn mailing list submissions to > scikit-learn@python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/scikit-learn > or, via email, send a message with subject or body 'help' to > scikit-learn-requ...@python.org > > You can reach the person managing the list at > scikit-learn-ow...@python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of scikit-learn digest..." > > > Today's Topics: > > 1. Re: scikit-learn Digest, Vol 43, Issue 24 (Mike Smith) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Sat, 12 Oct 2019 14:04:12 -0700 > From: Mike Smith <javaeur...@gmail.com> > To: scikit-learn@python.org > Subject: Re: [scikit-learn] scikit-learn Digest, Vol 43, Issue 24 > Message-ID: > <CAEWZffD-hNviFkyxuM8CgDR3XSWOyn= > 4lry2njvjwvvr4rg...@mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > "... > If I should expect good results on a pc, scikit says that needing > gpu power is > > obsolete, since certain scikit models perform better (than ml designed > for gpu) > > that are not designed for gpu, for that reason. Is this true?" > > Where do you see this written? I think that you are looking for overly > simple stories that you are not true." > > Gael, see the below from the scikit-learn FAQ. You can also find this > yourself at the main FAQ: > > [image: 2019-10-12 14_00_05-Frequently Asked Questions ? scikit-learn > 0.21.3 documentation.png] > > > On Sat, Oct 12, 2019 at 9:03 AM <scikit-learn-requ...@python.org> wrote: > > > Send scikit-learn mailing list submissions to > > scikit-learn@python.org > > > > To subscribe or unsubscribe via the World Wide Web, visit > > https://mail.python.org/mailman/listinfo/scikit-learn > > or, via email, send a message with subject or body 'help' to > > scikit-learn-requ...@python.org > > > > You can reach the person managing the list at > > scikit-learn-ow...@python.org > > > > When replying, please edit your Subject line so it is more specific > > than "Re: Contents of scikit-learn digest..." > > > > > > Today's Topics: > > > > 1. Re: Is scikit-learn implying neural nets are the best > > regressor? (Gael Varoquaux) > > > > > > ---------------------------------------------------------------------- > > > > Message: 1 > > Date: Fri, 11 Oct 2019 13:34:33 -0400 > > From: Gael Varoquaux <gael.varoqu...@normalesup.org> > > To: Scikit-learn mailing list <scikit-learn@python.org> > > Subject: Re: [scikit-learn] Is scikit-learn implying neural nets are > > the best regressor? > > Message-ID: <20191011173433.bbywiqnwjjpvs...@phare.normalesup.org> > > Content-Type: text/plain; charset=iso-8859-1 > > > > On Fri, Oct 11, 2019 at 10:10:32AM -0700, Mike Smith wrote: > > > In other words, according to that arrangement, is scikit-learn implying > > that > > > section 1.17 is the best regressor out of the listed, 1.1 to 1.17? > > > > No. > > > > First they are not ordered in order of complexity (Naive Bayes is > > arguably simpler than Gaussian Processes). Second complexity does not > > imply better prediction. > > > > > If I should expect good results on a pc, scikit says that needing gpu > > power is > > > obsolete, since certain scikit models perform better (than ml designed > > for gpu) > > > that are not designed for gpu, for that reason. Is this true? > > > > Where do you see this written? I think that you are looking for overly > > simple stories that you are not true. > > > > > How much hardware is a practical expectation for running the best > > > scikit models and getting the best results? > > > > This is too vague a question for which there is no answer. > > > > Ga?l > > > > > On Fri, Oct 11, 2019 at 9:02 AM <scikit-learn-requ...@python.org> > wrote: > > > > > Send scikit-learn mailing list submissions to > > > ? ? ? ? scikit-learn@python.org > > > > > To subscribe or unsubscribe via the World Wide Web, visit > > > ? ? ? ? https://mail.python.org/mailman/listinfo/scikit-learn > > > or, via email, send a message with subject or body 'help' to > > > ? ? ? ? scikit-learn-requ...@python.org > > > > > You can reach the person managing the list at > > > ? ? ? ? scikit-learn-ow...@python.org > > > > > When replying, please edit your Subject line so it is more specific > > > than "Re: Contents of scikit-learn digest..." > > > > > > > Today's Topics: > > > > > ? ?1. Re: logistic regression results are not stable between > > > ? ? ? solvers (Andreas Mueller) > > > > > > > > > ---------------------------------------------------------------------- > > > > > Message: 1 > > > Date: Fri, 11 Oct 2019 15:42:58 +0200 > > > From: Andreas Mueller <t3k...@gmail.com> > > > To: scikit-learn@python.org > > > Subject: Re: [scikit-learn] logistic regression results are not > > stable > > > ? ? ? ? between solvers > > > Message-ID: <d55949d6-3355-f892-f6b3-030edf1c7...@gmail.com> > > > Content-Type: text/plain; charset="utf-8"; Format="flowed" > > > > > > > > > On 10/10/19 1:14 PM, Beno?t Presles wrote: > > > > > > Thanks for your answers. > > > > > > On my real data, I do not have so many samples. I have a bit more > > than > > > > 200 samples in total and I also would like to get some results > with > > > > unpenalized logisitic regression. > > > > What do you suggest? Should I switch to the lbfgs solver? > > > Yes. > > > > Am I sure that with this solver I will not have any convergence > > issue > > > > and always get the good result? Indeed, I did not get any > > convergence > > > > warning with saga, so I thought everything was fine. I noticed > some > > > > issues only when I decided to test several solvers. Without > > comparing > > > > the results across solvers, how to be sure that the optimisation > > goes > > > > well? Shouldn't scikit-learn warn the user somehow if it is not > > the case? > > > We should attempt to warn in the SAGA solver if it doesn't > converge. > > > That it doesn't raise a convergence warning should probably be > > > considered a bug. > > > It uses the maximum weight change as a stopping criterion right > now. > > > We could probably compute the dual objective once in the end to see > > if > > > we converged, right? Or is that not possible with SAGA? If not, we > > might > > > want to caution that no convergence warning will be raised. > > > > > > > > At last, I was using saga because I also wanted to do some > feature > > > > selection by using l1 penalty which is not supported by lbfgs... > > > You can use liblinear then. > > > > > > > > > > Best regards, > > > > Ben > > > > > > > > Le 09/10/2019 ? 23:39, Guillaume Lema?tre a ?crit?: > > > >> Ups I did not see the answer of Roman. Sorry about that. It is > > coming > > > >> back to the same conclusion :) > > > > > >> On Wed, 9 Oct 2019 at 23:37, Guillaume Lema?tre > > > >> <g.lemaitr...@gmail.com <mailto:g.lemaitr...@gmail.com>> wrote: > > > > > >>? ? ?Uhm actually increasing to 10000 samples solve the > convergence > > > issue. > > > >>? ? ?SAGA is not designed to work with a so small sample size > most > > > >>? ? ?probably. > > > > > >>? ? ?On Wed, 9 Oct 2019 at 23:36, Guillaume Lema?tre > > > >>? ? ?<g.lemaitr...@gmail.com <mailto:g.lemaitr...@gmail.com>> > > wrote: > > > > > >>? ? ? ? ?I slightly change the bench such that it uses pipeline > and > > > >>? ? ? ? ?plotted the coefficient: > > > > > >>? ? ? ? ?https://gist.github.com/glemaitre/ > > > 8fcc24bdfc7dc38ca0c09c56e26b9386 > > > > > >>? ? ? ? ?I only see one of the 10 splits where SAGA is not > > converging, > > > >>? ? ? ? ?otherwise the coefficients > > > >>? ? ? ? ?look very close (I don't attach the figure here but they > > can > > > >>? ? ? ? ?be plotted using the snippet). > > > >>? ? ? ? ?So apart from this second split, the other differences > > seems > > > >>? ? ? ? ?to be numerical instability. > > > > > >>? ? ? ? ?Where I have some concern is regarding the convergence > > rate > > > >>? ? ? ? ?of SAGA but I have no > > > >>? ? ? ? ?intuition to know if this is normal or not. > > > > > >>? ? ? ? ?On Wed, 9 Oct 2019 at 23:22, Roman Yurchak > > > >>? ? ? ? ?<rth.yurc...@gmail.com <mailto:rth.yurc...@gmail.com>> > > wrote: > > > > > >>? ? ? ? ? ? ?Ben, > > > > > >>? ? ? ? ? ? ?I can confirm your results with penalty='none' and > > C=1e9. > > > >>? ? ? ? ? ? ?In both cases, > > > >>? ? ? ? ? ? ?you are running a mostly unpenalized logisitic > > > >>? ? ? ? ? ? ?regression. Usually > > > >>? ? ? ? ? ? ?that's less numerically stable than with a small > > > >>? ? ? ? ? ? ?regularization, > > > >>? ? ? ? ? ? ?depending on the data collinearity. > > > > > >>? ? ? ? ? ? ?Running that same code with > > > >>? ? ? ? ? ? ?? - larger penalty ( smaller C values) > > > >>? ? ? ? ? ? ?? - or larger number of samples > > > >>? ? ? ? ? ? ?? yields for me the same coefficients (up to some > > > tolerance). > > > > > >>? ? ? ? ? ? ?You can also see that SAGA convergence is not good > by > > the > > > >>? ? ? ? ? ? ?fact that it > > > >>? ? ? ? ? ? ?needs 196000 epochs/iterations to converge. > > > > > >>? ? ? ? ? ? ?Actually, I have often seen convergence issues with > > SAG > > > >>? ? ? ? ? ? ?on small > > > >>? ? ? ? ? ? ?datasets (in unit tests), not fully sure why. > > > > > >>? ? ? ? ? ? ?-- > > > >>? ? ? ? ? ? ?Roman > > > > > >>? ? ? ? ? ? ?On 09/10/2019 22:10, serafim loukas wrote: > > > >>? ? ? ? ? ? ?> The predictions across solver are exactly the same > > when > > > >>? ? ? ? ? ? ?I run the code. > > > >>? ? ? ? ? ? ?> I am using 0.21.3 version. What is yours? > > > >>? ? ? ? ? ? ?> > > > >>? ? ? ? ? ? ?> > > > >>? ? ? ? ? ? ?> In [13]: import sklearn > > > >>? ? ? ? ? ? ?> > > > >>? ? ? ? ? ? ?> In [14]: sklearn.__version__ > > > >>? ? ? ? ? ? ?> Out[14]: '0.21.3' > > > >>? ? ? ? ? ? ?> > > > >>? ? ? ? ? ? ?> > > > >>? ? ? ? ? ? ?> Serafeim > > > >>? ? ? ? ? ? ?> > > > >>? ? ? ? ? ? ?> > > > >>? ? ? ? ? ? ?> > > > >>? ? ? ? ? ? ?>> On 9 Oct 2019, at 21:44, Beno?t Presles > > > >>? ? ? ? ? ? ?<benoit.pres...@u-bourgogne.fr > > > >>? ? ? ? ? ? ?<mailto:benoit.pres...@u-bourgogne.fr> > > > >>? ? ? ? ? ? ?>> <mailto:benoit.pres...@u-bourgogne.fr > > > >>? ? ? ? ? ? ?<mailto:benoit.pres...@u-bourgogne.fr>>> wrote: > > > >>? ? ? ? ? ? ?>> > > > >>? ? ? ? ? ? ?>> (y_pred_lbfgs==y_pred_saga).all() == False > > > >>? ? ? ? ? ? ?> > > > >>? ? ? ? ? ? ?> > > > >>? ? ? ? ? ? ?> _______________________________________________ > > > >>? ? ? ? ? ? ?> scikit-learn mailing list > > > >>? ? ? ? ? ? ?> scikit-learn@python.org <mailto: > > scikit-learn@python.org> > > > >>? ? ? ? ? ? ?> > > https://mail.python.org/mailman/listinfo/scikit-learn > > > >>? ? ? ? ? ? ?> > > > > > >>? ? ? ? ? ? ?_______________________________________________ > > > >>? ? ? ? ? ? ?scikit-learn mailing list > > > >>? ? ? ? ? ? ?scikit-learn@python.org <mailto: > > scikit-learn@python.org> > > > >>? ? ? ? ? ? ? > https://mail.python.org/mailman/listinfo/scikit-learn > > > > > > > > > >>? ? ? ? ?-- > > > >>? ? ? ? ?Guillaume Lemaitre > > > >>? ? ? ? ?Scikit-learn @ Inria Foundation > > > >>? ? ? ? ?https://glemaitre.github.io/ > > > > > > > > > >>? ? ?-- > > > >>? ? ?Guillaume Lemaitre > > > >>? ? ?Scikit-learn @ Inria Foundation > > > >>? ? ?https://glemaitre.github.io/ > > > > > > > > > >> -- > > > >> Guillaume Lemaitre > > > >> Scikit-learn @ Inria Foundation > > > >> https://glemaitre.github.io/ > > > > > >> _______________________________________________ > > > >> scikit-learn mailing list > > > >> scikit-learn@python.org > > > >> https://mail.python.org/mailman/listinfo/scikit-learn > > > > > > _______________________________________________ > > > > scikit-learn mailing list > > > > scikit-learn@python.org > > > > https://mail.python.org/mailman/listinfo/scikit-learn > > > > > -------------- next part -------------- > > > An HTML attachment was scrubbed... > > > URL: < > > http://mail.python.org/pipermail/scikit-learn/attachments/20191011/ > > > a7052cd9/attachment-0001.html> > > > > > ------------------------------ > > > > > Subject: Digest Footer > > > > > _______________________________________________ > > > scikit-learn mailing list > > > scikit-learn@python.org > > > https://mail.python.org/mailman/listinfo/scikit-learn > > > > > > > ------------------------------ > > > > > End of scikit-learn Digest, Vol 43, Issue 21 > > > ******************************************** > > > > > > > _______________________________________________ > > > scikit-learn mailing list > > > scikit-learn@python.org > > > https://mail.python.org/mailman/listinfo/scikit-learn > > > > > > -- > > Gael Varoquaux > > Research Director, INRIA Visiting professor, McGill > > http://gael-varoquaux.info > http://twitter.com/GaelVaroquaux > > > > > > ------------------------------ > > > > Subject: Digest Footer > > > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > > > > > ------------------------------ > > > > End of scikit-learn Digest, Vol 43, Issue 24 > > ******************************************** > > > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > http://mail.python.org/pipermail/scikit-learn/attachments/20191012/6959d075/attachment.html > > > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: 2019-10-12 14_00_05-Frequently Asked Questions ? scikit-learn 0.21.3 > documentation.png > Type: image/png > Size: 26245 bytes > Desc: not available > URL: < > http://mail.python.org/pipermail/scikit-learn/attachments/20191012/6959d075/attachment.png > > > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > > ------------------------------ > > End of scikit-learn Digest, Vol 43, Issue 25 > ******************************************** >
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