Update to Optim@2

[devmotion]

Fixes #177.

[ablaom]

Thanks @devmotion for drafting this fix - very much appreciated.

Current fail is:

HuberReg: Test Failed at /home/runner/work/MLJLinearModels.jl/MLJLinearModels.jl/test/fit/robust.jl:57
    Expression: isapprox(J(θ_newton), 6.200183, rtol = 1.0e-5)
     Evaluated: isapprox(6.200521473589882, 6.200183; rtol = 1.0e-5)

The relevant test code is here

I can't really comment here as I don't understand where the reference value "6.200183" is coming from. There is a code comment that says "in these cases, if available, θ_newton is used as reference" but I don't follow, as θ_newton is computed but not apparently not used as the reference value. So where exactly does ""6.200183" come from?

@tlienart Great if you could clarify here.

[tlienart]

these values were reference values taken from fixed runs from sklearn, you can safely adjust the relative tolerance, I did have to play a bit with tolerances back in the days, effectively the aim here is to have something that's "close enough" but it doesn't have to be identical because the approaches are not the same.

[ablaom]

@tlienart Thanks for that lightning response and clarification.

@devmotion I think you can go ahead and relax the tolerance. Can you also please add a code comment top of file to the effect that the reference values come from sklearn?

[devmotion]

I updated the comment and the tests

[tlienart]

On second thoughts it might not have been a sklearn comparison but an optimizer comparison (it's been a while) but the conclusion is identical.

Thanks for your work guys!

[ablaom]

On second thoughts it might not have been a sklearn comparison but an optimizer comparison

@tlienart Could you please elaborate? I still don't understand what exactly you mean here.