Merge pull request #115 from JuliaAI/dev

For a 0.6.2 release

Author: ablaom

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJLinearModels"
 uuid = "6ee0df7b-362f-4a72-a706-9e79364fb692"
 authors = ["Thibaut Lienart <[email protected]>"]
-version = "0.6.1"
+version = "0.6.2"
 
 [deps]
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"

src/mlj/interface.jl

@@ -36,6 +36,7 @@ function MMI.fit(m::Union{REG_MODELS...}, verb::Int, X, y)
     features = (sch === nothing) ? nothing : sch.names
     reg     = glr(m)
     solver  = m.solver === nothing ? _solver(reg, size(Xmatrix)) : m.solver
+    verb > 0 && @info "Solver: $(solver)"
     # get the parameters
     θ = fit(reg, Xmatrix, y; solver=solver)
     # return
@@ -72,6 +73,7 @@ function MMI.fit(m::Union{CLF_MODELS...}, verb::Int, X, y)
     # NOTE: here the number of classes is either 0 or > 2
     clf = glr(m, nclasses)
     solver = m.solver === nothing ? _solver(clf, size(Xmatrix)) : m.solver
+    verb > 0 && @info "Solver: $(solver)"
     # get the parameters
     θ = fit(clf, Xmatrix, yplain, solver=solver)
     # return

test/interface/fitpredict.jl

@@ -84,3 +84,10 @@ end
     fp = MLJBase.fitted_params(mach)
     @test unique(fp.classes) == [1,2,3]
 end
+
+@testset "Fitting a classifier with verbosity=1" begin
+    mdl = LogisticClassifier()
+    X, y = MLJBase.@load_iris
+    mach = MLJBase.machine(mdl, X, y)
+    @test_logs (:info,"Training Machine{LogisticClassifier,…}.") (:info,"Solver: LBFGS()") MLJBase.fit!(mach; verbosity=1)
+end