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 >
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