Merge pull request #18 from JuliaAI/training-losses-bug-fix

Training losses bug fix
JuliaAI · Apr 22, 2021 · d892b94 · d892b94
2 parents d2e2bd1 + 28801bb
commit d892b94
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Showing 3 changed files with 6 additions and 13 deletions.
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "MLJIteration"
 uuid = "614be32b-d00c-4edb-bd02-1eb411ab5e55"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.2.2"
+version = "0.2.3"
 
 [deps]
 IterationControl = "b3c1a2ee-3fec-4384-bf48-272ea71de57c"

diff --git a/src/ic_model.jl b/src/ic_model.jl
@@ -2,13 +2,9 @@
 # (cf. MLJBase/src/resampling.jl). It is for the wrapped type that we
 # will be overloading the methods of `IterationControl`.
 
-const ERR_TRAINING_LOSSES =
-    ArgumentError("Attempt to inspect training losses for "*
-                  "a model that doesn't report them. ")
 const ERR_EVALUATION =
     ArgumentError("There are no evaluation objects if `resampling=nothing`. ")
 
-
 mlj_model(mach::Machine) = mach.model
 mlj_model(mach::Machine{<:Resampler}) = mach.model.model
 
@@ -63,7 +59,7 @@ IterationControl.expose(ic_model::ICModel{<:Machine{<:Resampler}}) =
 # overloading `loss` - for `resampling === nothing`:
 function IterationControl.loss(m::ICModel)
     losses = training_losses(IterationControl.expose(m))
-    losses === nothing && throw(ERR_TRAINING_LOSSES)
+    losses isa Nothing && return nothing
     return last(losses)
 end
 
@@ -75,7 +71,7 @@ IterationControl.loss(m::ICModel{<:Machine{<:Resampler}}) =
 function IterationControl.training_losses(m::ICModel)
     mach = IterationControl.expose(m)
     losses = training_losses(mach)
-    losses === nothing && throw(ERR_TRAINING_LOSSES)
+    losses isa Nothing && return nothing
     s = length(losses)
     return view(losses, (s - m.Δi[] + 1):s)
 end

diff --git a/test/ic_model.jl b/test/ic_model.jl
@@ -72,16 +72,13 @@ MLJBase.predict(::FooBar, ::Any, Xnew) = ones(length(Xnew))
                 y)
     fit!(resampling_machine, verbosity=0)
     ic_model = MLJIteration.ICModel(resampling_machine, :n, 0)
-    @test_throws(MLJIteration.ERR_TRAINING_LOSSES,
-                 IterationControl.training_losses(ic_model))
+    @test IterationControl.training_losses(ic_model) === nothing
 
     mach = machine(FooBar(10), X, y)
     fit!(mach, verbosity=0)
     ic_model = MLJIteration.ICModel(mach, :n, 0)
-    @test_throws(MLJIteration.ERR_TRAINING_LOSSES,
-                 IterationControl.training_losses(ic_model))
-    @test_throws(MLJIteration.ERR_TRAINING_LOSSES,
-                 IterationControl.loss(ic_model))
+    @test IterationControl.training_losses(ic_model) === nothing
+    @test IterationControl.loss(ic_model) === nothing
 end
 
 @testset "ICModel interface for users" begin