[MRG] Logistic Regression CV

Continuation of the work by @fabianp and @GaelVaroquaux 

TODO's

- [x] I think the some of the private functions in logistic.py can be simplified. In particular, it is my intuition that we could squash some of the functions _logistic* and _logistic_*_intercept without sacrificing a significant amount of performance

- [x] sparse matrix support (any help from the people who actually use sparse matrices would be great here)

- [x] implement multinomial logistic or raise a meaningful error for multiclass pointing to OvA, OvO meta-estimators

- [x] More tests for LogisticCV object (and some are failing)

- [x] Document tolerance and stopping criterions
I'll continue tomorrow


On Fri, Jul 11, 2014 at 3:21 PM, Manoj Kumar <notifications@github.com>
wrote:

> In sklearn/linear_model/logistic.py:
>
> > +        if s.shape[0] > n_features:
> > +            ret[:n_features] += s[-1] * dd_intercept
> > +            ret[-1] = dd_intercept.dot(s[:n_features])
> > +            ret[-1] += d.sum() * s[-1]
> > +        return ret
> > +
> > +    return out, grad, Hs
> > +
> > +
> > +def logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True,
> > +                             max_iter=100, tol=1e-4, verbose=0,
> > +                             solver='liblinear', coef=None, copy=True,
> > +                             class_weight=None):
> > +    """Compute a Logistic Regression model for a list of regularization
> > +    parameters.
> > +
>
> @fabianp <https://github.com/fabianp> thanks for the reviews, do you have
> any more comments coming up?
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/scikit-learn/scikit-learn/pull/2862/files#r14819862>.
>