(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
You're right we don't need this anymore (this is residual hack from the
original plot_oasis_vbm.py script). Will cleanup asap.

On Thu, Oct 2, 2014 at 5:38 PM, Gael Varoquaux <notifications@github.com>
wrote:

> In plot_oasis_smoothlasso_vbm.py:
>
> > +
> > +n_subjects = 100   # more subjects requires more memory
> > +memory = Memory("cache")
> > +
> > +### Load Oasis dataset ########################################################
> > +dataset_files = datasets.fetch_oasis_vbm(n_subjects=n_subjects)
> > +age = dataset_files.ext_vars['age'].astype(float)
> > +
> > +### Preprocess data ###########################################################
> > +nifti_masker = NiftiMasker(
> > +    standardize=False,
> > +    smoothing_fwhm=2,
> > +    memory=memory)  # cache options
> > +# remove features with too low between-subject variance
> > +gm_maps_masked = nifti_masker.fit_transform(dataset_files.gray_matter_maps)
> > +gm_maps_masked[:, gm_maps_masked.var(0) < 0.01] = 0.
>
> That use of a first masker to remove features feels hackish. Do we
> actually need this, now that we do univariate feature screening?
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nilearn/nilearn/pull/219/files#r18347190>.
>



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