[Mne_analysis] GeneralizingEstimator with incremental learning
Alex Murphy
murphyalex at gmail.com
Thu Aug 6 11:11:25 EDT 2020
External Email - Use Caution
Hi Giulia,
I recently needed to incorporate SGDClassifier into some MNE pipeline and
ran into the same problem when working with the LinearModel class. I ended
up subclassing the MNE class and overloaded / wrote my own "fit" method
with my own, then called that one instead.
If you write your own class and inherit from GeneralizingEstimator, give it
its own fit function (in which you write your code that calls partial_fit
on the SGDClassifier instance until convergence or whatever your criteria
are), then it should work.
Best
Alex Murphy
On Thu, 6 Aug 2020 at 14:56, <mne_analysis-request at nmr.mgh.harvard.edu>
wrote:
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> Today's Topics:
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> 1. GeneralizingEstimator with incremental learning /
> .partial_fit (Giulia Gennari)
> 2. Re: GeneralizingEstimator with incremental learning /
> .partial_fit (Jean-R?mi KING)
> 3. Re: GeneralizingEstimator with incremental learning /
> .partial_fit (Giulia Gennari)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Wed, 5 Aug 2020 18:38:35 +0200
> From: Giulia Gennari <giulia.gennari1991 at gmail.com>
> Subject: [Mne_analysis] GeneralizingEstimator with incremental
> learning / .partial_fit
> To: mne_analysis at nmr.mgh.harvard.edu
> Message-ID:
> <CAMn-mVzF+1URRkCr3jf=
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> Hi!
>
> I would need to try decoding with incremental learning (EEG data).
> I was planning to use logistic regression by means of the SGDClassifier
> <
> https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
> >
> .
> I would then need to call .partial_fit to make my estimator learn on each
> of my training sets.
> However:
>
> 'GeneralizingEstimator' object has no attribute 'partial_fit'
>
> Same issue for SlidingEstimator.
> Is there a way to work around this limitation?
>
> Thank you so so much in advance!
>
> Giulia Gennari
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> Message: 2
> Date: Wed, 5 Aug 2020 19:17:24 +0200
> From: Jean-R?mi KING <jeanremi.king at gmail.com>
> Subject: Re: [Mne_analysis] GeneralizingEstimator with incremental
> learning / .partial_fit
> To: Discussion and support forum for the users of MNE Software
> <mne_analysis at nmr.mgh.harvard.edu>
> Message-ID:
> <
> CAOcgdchCrC+ibarOSLBmaEr7tSgVFk71dxj1s5zAMekBZnvgCA at mail.gmail.com>
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> Hi Giulia,
>
> I think you should be able to change the method:
>
> model = sklearn.linear_model.SGDClassifier()
> model.fit = model.partial_fit
> slider = mne.decoding.SlidingEstimator(model)
> for X, y in train_batches:
> slider.fit(X, y)
>
> Best
>
> JR
>
> On Wed, 5 Aug 2020 at 18:40, Giulia Gennari <giulia.gennari1991 at gmail.com>
> wrote:
>
> > External Email - Use Caution
> >
> > Hi!
> >
> > I would need to try decoding with incremental learning (EEG data).
> > I was planning to use logistic regression by means of the SGDClassifier
> > <
> https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
> >
> > .
> > I would then need to call .partial_fit to make my estimator learn on each
> > of my training sets.
> > However:
> >
> > 'GeneralizingEstimator' object has no attribute 'partial_fit'
> >
> > Same issue for SlidingEstimator.
> > Is there a way to work around this limitation?
> >
> > Thank you so so much in advance!
> >
> > Giulia Gennari
> > _______________________________________________
> > Mne_analysis mailing list
> > Mne_analysis at nmr.mgh.harvard.edu
> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
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> ------------------------------
>
> Message: 3
> Date: Thu, 6 Aug 2020 15:54:37 +0200
> From: Giulia Gennari <giulia.gennari1991 at gmail.com>
> Subject: Re: [Mne_analysis] GeneralizingEstimator with incremental
> learning / .partial_fit
> To: Discussion and support forum for the users of MNE Software
> <mne_analysis at nmr.mgh.harvard.edu>
> Message-ID:
> <CAMn-mVydRwoZTBtuTj_UhQNX7TV=
> ufd99xsTfSmFymF+1ZaAWA at mail.gmail.com>
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>
> Dear Jean-R?mi,
>
> Thank you for the nice suggestion!
>
> Just to make sure that this is working (I apologize for my ignorance):
>
> When I run:
> model = SGDClassifier(loss='log', class_weight='balanced')
> model.fit = model.partial_fit
> slider1 = SlidingEstimator(model, scoring='roc_auc')
> slider1.fit(X_train, y_train)
>
> or
>
> clf = make_pipeline(Vectorizer(), StandardScaler(), model)
> slider2 = SlidingEstimator(clf, scoring='roc_auc')
> slider2.fit(X_train, y_train)
>
> I do not get any error, while I would expect:
>
> ValueError: class_weight 'balanced' is not supported for partial_fit.
> In order to use 'balanced' weights, use
> compute_class_weight('balanced', classes, y). Pass the resulting
> weights as the class_weight parameter.
>
>
> Since this is what I get with:
> model.fit(X_train[:,:,single_time_point], y_train)
>
> Is there a good reason for that? E.g. class weights are computed internally
> beforehand by SlidingEstimator?
>
> Thank you again!
>
> Giulia
>
> On Wed, Aug 5, 2020 at 7:18 PM Jean-R?mi KING <jeanremi.king at gmail.com>
> wrote:
>
> > External Email - Use Caution
> >
> > Hi Giulia,
> >
> > I think you should be able to change the method:
> >
> > model = sklearn.linear_model.SGDClassifier()
> > model.fit = model.partial_fit
> > slider = mne.decoding.SlidingEstimator(model)
> > for X, y in train_batches:
> > slider.fit(X, y)
> >
> > Best
> >
> > JR
> >
> > On Wed, 5 Aug 2020 at 18:40, Giulia Gennari <
> giulia.gennari1991 at gmail.com>
> > wrote:
> >
> >> External Email - Use Caution
> >>
> >> Hi!
> >>
> >> I would need to try decoding with incremental learning (EEG data).
> >> I was planning to use logistic regression by means of the SGDClassifier
> >> <
> https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
> >
> >> .
> >> I would then need to call .partial_fit to make my estimator learn on
> each
> >> of my training sets.
> >> However:
> >>
> >> 'GeneralizingEstimator' object has no attribute 'partial_fit'
> >>
> >> Same issue for SlidingEstimator.
> >> Is there a way to work around this limitation?
> >>
> >> Thank you so so much in advance!
> >>
> >> Giulia Gennari
> >> _______________________________________________
> >> Mne_analysis mailing list
> >> Mne_analysis at nmr.mgh.harvard.edu
> >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
> >
> > _______________________________________________
> > Mne_analysis mailing list
> > Mne_analysis at nmr.mgh.harvard.edu
> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
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