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
