(WIP) Sparse models: S-LASSO and TV-l1

- Supports TV-l1 and S-LASSO priors
- Supports logistic and squared losses
- Has cross validation
- Can automatically select alpha by CV (+ automatic computation of useful alpha ranges for the CV)
- Warning: User must supply l1_ratio
Smoothing is already done in the NifitMasker. How do we reconcile these
smoothings (the one in NiftiMasker and the proposed one in path_scores) ?

On Wed, Oct 8, 2014 at 4:16 PM, Gael Varoquaux <notifications@github.com>
wrote:

> In nilearn/decoding/space_net.py:
>
> > +        List of regularization parameters being considered.
> > +
> > +    l1_ratio : float in the interval [0, 1]; optinal (default .5)
> > +        Constant that mixes L1 and TV (resp. Smooth Lasso) penalization.
> > +        l1_ratio == 0: just smooth. l1_ratio == 1: just lasso.
> > +
> > +    solver : function handle
> > +       See for example tv.TVl1Classifier documentation.
> > +
> > +    """
> > +
> > +    alphas = sorted(alphas)[::-1]
> > +
> > +    # univariate feature screening
> > +    mask = mask.copy()
> > +    if screening_percentile < 100.:
>
> We need to smooth the data that we are passing to the SelectPercentile
> here, using nilearn.image.image._smooth_array with fwm='fast'. PR #258
> <https://github.com/nilearn/nilearn/pull/258> will enable you to pass
> affine=None there.
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nilearn/nilearn/pull/219/files#r18584555>.
>



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