Custom XGBoost Loss function w/ Zygote. Julia Computing blog post

Specific Domains Machine Learning
,
23 
using Distributed
nprocs() == 1 && addprocs()
@everywhere using AutoMLPipeline, DataFrames

#Get models.
sk= AutoMLPipeline.SKLearners.learner_dict |> keys |> collect;
sk= sk |> x-> sort(x,lt=(x,y)->lowercase(x)<lowercase(y));
m_cl= sk[occursin.("Classifier", sk)];
m_cl= m_cl ∪ sk[occursin.("NB", sk)];
m_cl= m_cl ∪ sk[occursin.("SVC", sk)];
m_cl= m_cl ∪ ["LDA", "QDA"];

iris = AutoMLPipeline.Utils.getiris();
X = iris[:,1:4];
Y = iris[:,end] |> Vector;

learners = @distributed (vcat) for m in m_cl
    learner = SKLearner(m)
    pcmc = AutoMLPipeline.@pipeline learner
    println(learner.name)
    mean,sd,folds,err = crossvalidate(pcmc,X,Y,"accuracy_score",10)
    DataFrame(name=learner.name,mean=mean,sd=sd,folds=folds,errors=err)
end;
@show learners;
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