RuntimeError: Cannot clone object LassoCV(...), as the constructor either does not set or modifies parameter precompute about celer HOT 6 CLOSED

Thanks for raising this @chrism2671, it should be fixed by #295 that I'll merge as soon as the CI is green.

If you use celer in research work, we kindly ask that you cite our papers (see the README) ; if it's in an industrial context, we'd love to hear more about your usecase. You can also have a look at our skglm package that implements many more models and penalties, in particular non convex ones that have better sparsifying properties.

from celer.

Wow, that was fast! And here I was struggling to install the deps to do it myself! Thank you so much! :D

from celer.

@chrism2671 this would really help us:

If you use celer in research work, we kindly ask that you cite our papers (see the README) ; if it's in an industrial context, we'd love to hear more about your usecase. You can also have a look at our skglm package that implements many more models and penalties, in particular non convex ones that have better sparsifying properties.

from celer.

I'm attempting to reimplement this paper:

https://www.researchgate.net/publication/314533287_Sparse_Signals_in_the_Cross-Section_of_Returns

On my significantly reduced dataset (1 year of data, 100 columns), this takes my laptop approximately 2-3 days using sklearn, and seems to be about 10 hours using Celer. The paper using R/glmnet, and thanks a supercomputer center in its notes.

I've worked hard to try to accelerate this (even writing my own lasso in KDB/q), and experimenting with Cuda Rapids, but Celer is the fastest by far. In my case, because of the MultiOutputRegressor, I'm doing many millions of small regressions. I do wonder if Python's inefficient multiprocessing is part of the bottleneck.

from celer.

How many rows do you have in your dataset ?
For such "large n_samples, small n_features" datasets, you should have a look at our GramCD solver in skglm which is super fast : scikit-learn-contrib/skglm#229

from celer.

It's the other way unfortunately, (n_samples=30, n_features=300)

from celer.