> @agramfort , Okay. What about my previous questions then?
>
> 1] It seems MultiTaskElasticNet uses, cd_fast.enet_coordinate_descent_multi_task(), Do you think something like this
>
> clf = MultiTaskElasticNet(alpha=[0.1,0.2])
> clf.fit([[0,0], [1, 1], [2, 2]], [[0, 1], [1, 2], [2, 2]])
>
> be useful? that is having different alphas across different tasks? [This would also mean may/maynot changing the cd_fast.pyx file]

have a look at the cost function minimized by the MultiTaskElasticNet estimator

in this case it's obvious there can be only 1 alpha.

> If we don't have this, then we are basically stuck to the same problem, i.e which alpha to choose across all tasks, since MultiTask also accepts only float values for alpha.

to me when you treat tasks independently as in ElasticNetCV you should
have one alpha per task hence one estimator per task. As i recommended
before ElasicNetCV should raise an error when y.ndim == 2, and ideally
recommend to fit and ElasicNetCV per task or to use
MultiTaskElasticNetCV once it exists.