scikit-learn/scikit-learn — pull request #992 by ibayer (2012-08-01 22:10:08+00:00)
Title: WIP: enet strong rules

This PR aims to extend the current ``coordinate_descent.enet_path`` to uses an active set of features and strong rule filtering ([0] p. 20).

A prototype is available here:
https://gist.github.com/3188143

I suggest to integrate the prototype the following way:

* add two options to enet_coordinate_descent (done)
* -- possibility to skip the dual gap calculation ``calc_dual_gap``
* -- do the iterations only over a provided subset of features ``iter_set``
* make these options accessible though ``enet.fit``
* replace the loop over the range of alphas in ``coordinate_descent_enet_path`` (line 106 - 122) with the
loop in enet_path from the gist.
* -- in the loop from the gist, replace the ``shrinkage()`` calls by calls to ``enet.fit``

[0] Tibshirani, R., J. Bien, J. Friedman, T. Hastie, N. Simon, J. Taylor, and R.J. Tibshirani. “Strong Rules for Discarding Predictors in Lasso-type Problems.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) (2011).