> Also, is the warm_start option relevant here? In theory, the coefficients
> from an estimator fit with N features could be used for fitting an
> estimator with N - 1 features. In practice though, RFE and RFECV might not
> copy the coef_ array between runs.
>
>
warm_start can be used but you have to change
estimator = clone(self.estimator)
estimator.fit(X[:, features], y)
in rfe.py to something like:
if getattr(estimator, "warm_start", False):
estimator.coef_ = estimator.coef_[:, features]
estimator.fit(X[:, features], y)
else:
estimator = clone(self.estimator)
estimator.fit(X[:, features], y)
(I haven't checked whether this actually works or not but you get the idea)
As we said in the issue you opened recently, SGDRegressor doesn't monitor
convergence therefore warm_start won't make things faster out of the box.
But you can still try to reduce the number of iterations by hand (since the
solution is warm-started, you can expect SGD to find a solution in a fewer
iterations).
Mathieu
------------------------------------------------------------------------------
For Developers, A Lot Can Happen In A Second.
Boundary is the first to Know...and Tell You.
Monitor Your Applications in Ultra-Fine Resolution. Try it FREE!
http://p.sf.net/sfu/Boundary-d2dvs2
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
