[scikit-learn] Can fit a model with a target array of probabilities?

josef.pktd at gmail.com josef.pktd at gmail.com
Thu Oct 5 15:07:43 EDT 2017 

On Thu, Oct 5, 2017 at 3:00 PM, Stuart Reynolds <stuart at 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.violante at 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 <
> stuart at 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.pktd at gmail.com> wrote:
> >> >
> >> >
> >> > On Wed, Oct 4, 2017 at 4:26 PM, Stuart Reynolds
> >> > <stuart at 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 <t3kcit at 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
> >> >> >> _______________________________________________
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> >> >> >> scikit-learn at python.org
> >> >> >> https://mail.python.org/mailman/listinfo/scikit-learn
> >> >> >
> >> >> >
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