For ElasticNetCV, inside the function _alpha_grid() it computes the maximum
regularization strength alpha, with a given dataset X, target Y, and L1
ratio, for which there will be at least one nonzero coefficient. I'm
wondering if/how the same could be computed for sklearn's L1/L2-regularized
NMF. I'm also interested in computing a minimum alpha (the smallest at
which there are more nonzero coefficients than with alpha=0).
Does anyone know how this could be done?
Thanks,
James Jensen
PhD student, Bioinformatics and Systems Biology
Trey Ideker lab
University of California, San Diego
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