On Thu, Oct 5, 2017 at 3:00 PM, Stuart Reynolds <stu...@stuartreynolds.net> wrote:
> Hi Sean, > > I'll have a look glmnet (looks like its compiled from fortran!). Does > it offer much over statsmodel's GLM? This looks great for researchy > stuff, although a little less performant. > GLMNet is/wraps the original Fortran implementation of elastic net. I expect that it is much faster than the python version in statsmodels. I have no idea what option they support and what restrictions they have on the data. I have no guess on speed difference for the non-penalized version. I assume it's Fortran loops with coordinate descend versus iterative linear algebra. Josef > > - Stu > > > > On Thu, Oct 5, 2017 at 10:32 AM, Sean Violante <sean.viola...@gmail.com> > wrote: > > Stuart > > have you tried glmnet ( in R) there is a python version > > https://web.stanford.edu/~hastie/glmnet_python/ .... > > > > > > > > > > On Thu, Oct 5, 2017 at 6:34 PM, Stuart Reynolds < > stu...@stuartreynolds.net> > > wrote: > >> > >> Thanks Josef. Was very useful. > >> > >> result.remove_data() reduces a 5 parameter Logit result object from > >> megabytes to 5Kb (as compared to a minimum uncompressed size of the > >> parameters of ~320 bytes). Is big improvement. I'll experiment with > >> what you suggest -- since this is still >10x larger than possible. I > >> think the difference is mostly attribute names. > >> I don't mind the lack of a multinomial support. I've often had better > >> results mixing independent models for each class. > >> > >> I'll experiment with the different solvers. I tried the Logit model > >> in the past -- its fit function only exposed a maxiter, and not a > >> tolerance -- meaning I had to set maxiter very high. The newer > >> statsmodels GLM module looks great and seem to solve this. > >> > >> For other who come this way, I think the magic for ridge regression is: > >> > >> from statsmodels.genmod.generalized_linear_model import GLM > >> from statsmodels.genmod.generalized_linear_model import > families > >> from statsmodels.genmod.generalized_linear_model.families > import > >> links > >> > >> model = GLM(y, Xtrain, family=families.Binomial(link= > links.Logit)) > >> result = model.fit_regularized(method='elastic_net', > >> alpha=l2weight, L1_wt=0.0, tol=...) > >> result.remove_data() > >> result.predict(Xtest) > >> > >> One last thing -- its clear that it should be possible to do something > >> like scikit's LogisticRegressionCV in order to quickly optimize a > >> single parameter by re-using past coefficients. > >> Are there any wrappers in statsmodels for doing this or should I roll my > >> own? > >> > >> > >> - Stu > >> > >> > >> On Wed, Oct 4, 2017 at 3:43 PM, <josef.p...@gmail.com> wrote: > >> > > >> > > >> > On Wed, Oct 4, 2017 at 4:26 PM, Stuart Reynolds > >> > <stu...@stuartreynolds.net> > >> > wrote: > >> >> > >> >> Hi Andy, > >> >> Thanks -- I'll give another statsmodels another go. > >> >> I remember I had some fitting speed issues with it in the past, and > >> >> also some issues related their models keeping references to the data > >> >> (=disaster for serialization and multiprocessing) -- although that > was > >> >> a long time ago. > >> > > >> > > >> > The second has not changed and will not change, but there is a > >> > remove_data > >> > method that deletes all references to full, data sized arrays. > However, > >> > once > >> > the data is removed, it is not possible anymore to compute any new > >> > results > >> > statistics which are almost all lazily computed. > >> > The fitting speed depends a lot on the optimizer, convergence criteria > >> > and > >> > difficulty of the problem, and availability of good starting > parameters. > >> > Almost all nonlinear estimation problems use the scipy optimizers, all > >> > unconstrained optimizers can be used. There are no optimized special > >> > methods > >> > for cases with a very large number of features. > >> > > >> > Multinomial/multiclass models don't support continuous response (yet), > >> > all > >> > other GLM and discrete models allow for continuous data in the > interval > >> > extension of the domain. > >> > > >> > Josef > >> > > >> > > >> >> > >> >> - Stuart > >> >> > >> >> On Wed, Oct 4, 2017 at 1:09 PM, Andreas Mueller <t3k...@gmail.com> > >> >> wrote: > >> >> > Hi Stuart. > >> >> > There is no interface to do this in scikit-learn (and maybe we > should > >> >> > at > >> >> > this to the FAQ). > >> >> > Yes, in principle this would be possible with several of the > models. > >> >> > > >> >> > I think statsmodels can do that, and I think I saw another glm > >> >> > package > >> >> > for Python that does that? > >> >> > > >> >> > It's certainly a legitimate use-case but would require substantial > >> >> > changes to the code. I think so far we decided not to support > >> >> > this in scikit-learn. Basically we don't have a concept of a link > >> >> > function, and it's a concept that only applies to a subset of > models. > >> >> > We try to have a consistent interface for all our estimators, and > >> >> > this doesn't really fit well within that interface. > >> >> > > >> >> > Hth, > >> >> > Andy > >> >> > > >> >> > > >> >> > On 10/04/2017 03:58 PM, Stuart Reynolds wrote: > >> >> >> > >> >> >> I'd like to fit a model that maps a matrix of continuous inputs > to a > >> >> >> target that's between 0 and 1 (a probability). > >> >> >> > >> >> >> In principle, I'd expect logistic regression should work out of > the > >> >> >> box with no modification (although its often posed as being > strictly > >> >> >> for classification, its loss function allows for fitting targets > in > >> >> >> the range 0 to 1, and not strictly zero or one.) > >> >> >> > >> >> >> However, scikit's LogisticRegression and LogisticRegressionCV > reject > >> >> >> target arrays that are continuous. Other LR implementations allow > a > >> >> >> matrix of probability estimates. Looking at: > >> >> >> > >> >> >> > >> >> >> > >> >> >> http://scikit-learn-general.narkive.com/4dSCktaM/using- > logistic-regression-on-a-continuous-target-variable > >> >> >> and the fix here: > >> >> >> https://github.com/scikit-learn/scikit-learn/pull/5084, which > >> >> >> disables > >> >> >> continuous inputs, it looks like there was some reason for this. > So > >> >> >> ... I'm looking for alternatives. > >> >> >> > >> >> >> SGDClassifier allows log loss and (if I understood the docs > >> >> >> correctly) > >> >> >> adds a logistic link function, but also rejects continuous > targets. > >> >> >> Oddly, SGDRegressor only allows ‘squared_loss’, ‘huber’, > >> >> >> ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’, and > doesn't > >> >> >> seems to give a logistic function. > >> >> >> > >> >> >> In principle, GLM allow this, but scikit's docs say the GLM models > >> >> >> only allows strict linear functions of their input, and doesn't > >> >> >> allow > >> >> >> a logistic link function. The docs direct people to the > >> >> >> LogisticRegression class for this case. > >> >> >> > >> >> >> In R, there is: > >> >> >> > >> >> >> glm(Total_Service_Points_Won/Total_Service_Points_Played ~ ... , > >> >> >> family = binomial(link=logit), weights = > >> >> >> Total_Service_Points_Played) > >> >> >> which would be ideal. > >> >> >> > >> >> >> Is something similar available in scikit? (Or any continuous model > >> >> >> that takes and 0 to 1 target and outputs a 0 to 1 target?) > >> >> >> > >> >> >> I was surprised to see that the implementation of > >> >> >> CalibratedClassifierCV(method="sigmoid") uses an internal > >> >> >> implementation of logistic regression to do its logistic > regressing > >> >> >> -- > >> >> >> which I can use, although I'd prefer to use a user-facing library. > >> >> >> > >> >> >> Thanks, > >> >> >> - Stuart > >> >> >> _______________________________________________ > >> >> >> 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 > >> >> _______________________________________________ > >> >> 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 > >> > > >> _______________________________________________ > >> 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 > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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