https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/least_angle.py
It looks like that's alpha_min=0 by default in master, so it should be fixed. -- Cp On Fri, Jun 29, 2012 at 9:05 AM, Alexandre Gramfort <[email protected]> wrote: > you should be able to call lars_path with min alpha=0 > > see : > > https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/tests/test_least_angle.py#L57 > > Alex > > On Fri, Jun 29, 2012 at 1:40 AM, Charles-Pierre Astolfi <[email protected]> > wrote: >> Btw, let >> >>>>> alphas, _, coefs = lars_path(X, y) >> >> Wouldn't it be nice to have 0 in alphas ? (with the plain linear >> regression as its coefs). >> That would make interpolation implementation more straightforward, and >> that way, the regularization path would be totally defined. >> As of now, it is not possible to tell directly the solution of Lasso >> between zero and min(alpha). >> >> That'd make another PR I guess :) >> >> -- >> Cp >> >> >> On Thu, Jun 28, 2012 at 10:01 PM, Alexandre Gramfort >> <[email protected]> wrote: >>> +1 >>> >>> look for interpolate in least_angle.py to see where we already use the >>> linear interpolation >>> >>> Alex >>> >>> On Thu, Jun 28, 2012 at 6:42 PM, Olivier Grisel >>> <[email protected]> wrote: >>>> 2012/6/28 Charles-Pierre Astolfi <[email protected]>: >>>>> Hi scikiters, >>>>> >>>>> I am trying to compute lasso/lars for several values of the >>>>> regularization parameter. >>>>> >>>>> A clean way to do this, is like this: >>>>>>>> alphas = [some long list of positive values] >>>>>>>> models = linear_model.lars_path(X, y, alphas=alphas) >>>> >>>> Nope you cannot path the list of alphas to `lars_path` as it will >>>> automatically find the relevant inflection points analytically, see >>>> for instance this example : >>>> >>>> http://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars.html >>>> >>>>> Unfortunately, it seems to be very slow, compared to lars_path + >>>>> manual interpolation on each alpha. >>>> >>>> Ok so in the previous comment you probably meant lasso_path instead of >>>> lars_path. As in: >>>> >>>> http://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_coordinate_descent_path.html >>>> >>>>> My understanding is that it is >>>>> because the returned model is an ElasticNet and, as such, cannot use >>>>> lars regularization path to efficiently compute the solutions for >>>>> several alphas. >>>> >>>> Yes it is an iterated Coordinate Descent optimization of the LASSO >>>> objective function rather than a LASSO/LARS optimizer but it does warm >>>> restarts so it should still be competitive with LARS. However I think >>>> that the convergence checks in the current implementation are >>>> expensive right now so fine grids my still be expensive. There are two >>>> ongoing GSoC projects that might fix this perf issue at some point. >>>> >>>>> Am I missing something? >>>> >>>> You might now be aware that the Lasso CD optimization is very >>>> expensive when alpha is getting very small. Try to truncate your >>>> alphas list down to the smallest values that are still easy to >>>> optimize for. >>>> >>>>> If I need speed, should I interpolate myself? >>>> >>>> It's probably a good idea and could be a nice new feature to >>>> contribute to scikit-learn. >>>> >>>>> Is it worth a pull request? >>>> >>>> Sure :) >>>> >>>> -- >>>> Olivier >>>> http://twitter.com/ogrisel - http://github.com/ogrisel >>>> >>>> ------------------------------------------------------------------------------ >>>> Live Security Virtual Conference >>>> Exclusive live event will cover all the ways today's security and >>>> threat landscape has changed and how IT managers can respond. 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