MLJ w/Scikitlearn: passing return_std to predict

[evolbio]

Thank you, I appreciate all of the help you have given on this. I agree that all of the Bayesian routines in SLK should probabilistic models in MLJ, that would be best in the long run.

For now, I managed to call ScikitLearn.jl directly. In doing that, I found that calling ScilearnKit.jl is easy from REPL, but to make the call from a function in the way that I needed, I had to resort to an obscure workaround:

Error when calling ScikitLearn from a function in a Package

Thanks! This works, using ScikitLearn, PyCall const LogisticRegression = PyNULL() function __init__() @eval @sk_import linear_model: LogisticRegression end function logistic_skl(points::AbstractMatrix{<:Real}, labels::AbstractVector{Bool}) log_reg = fit!(LogisticRegression(penalty="l2"), points', labels) w = vec(log_reg.coef_) b = only(log_reg.intercept_) return w, b end However, I do get a warning from modifying the const: WARNING: redefinition of constant LogisticReg…

Perhaps there is some other way, but I could not find it. It seems strange that something as basic as calling via a function requires an undocumented hack. In any case, I have a simple workaround for now that allows me to move ahead.