Le 28 mars 2012 09:04, Alexandre Gramfort <[email protected]> a écrit : > hi Immanuel, > > glad to hear about your interest. > > to contribute a quick patch to the coordinate descent code what do you think > of adding the positivity constraint to Lasso and ElasticNet? > > the idea is to accept an update a coef only if the value is positive. > > the loop concerned is this one: > > https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/cd_fast.pyx#L87 > > A second step is to update the dual gap computation to this case (not > the most urgent). > > We could have > >>>> pos_lasso = Lasso(alpha=0.01, positive=True) > > The patch should be small but contain a test and properly updated docstrings. > > You should also update an existing example to advertise the feature.
+1 for starting with a first patch on the current CD implementation to get familiar with the existing code base. As for the content of the proposal itself, it would be good to include extensive profiling sessions on realistic datasets (e.g. microarray data) both on individual estimator runs and on regularization paths with warm restarts. Also David experienced poor performance compared to other implementation when using the CD models in a sparse coding. Would be great to ensure comparable performance with state of the art for this use case as well. Investigating with OpenMP via cython prange might be possible solution. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
