Bump EarlyStopping version and fix tests

Project.toml

@@ -8,7 +8,7 @@ EarlyStopping = "792122b4-ca99-40de-a6bc-6742525f08b6"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 
 [compat]
-EarlyStopping = "0.1.8"
+EarlyStopping = "0.2"
 julia = "1"
 
 [extras]

src/train.jl

@@ -27,7 +27,7 @@ function train!(model, controls...; verbosity::Int=1)
     training_losses = IterationControl.training_losses(model)
     if verbosity > 0
         loss isa Nothing || @info "final loss: $loss"
-        training_losses isa Nothing ||
+        training_losses isa Nothing || isempty(training_losses) ||
             @info "final training loss: $(training_losses[end])"
         verbosity > 1 && @info "total control cycles: $n"
     end

test/stopping_controls.jl

@@ -35,15 +35,23 @@ end
     # A stopping criterion than uses training losses:
 
     m = SquareRooter(4)
-    c = PQ()
+    c = PQ(k=5)
+
+    # Note that `SquareRooter` does not cache training losses. It only
+    # makes the most recent losses available, which is the worst-case
+    # scenario.
 
     IC.train!(m, 3)
     state = IC.update!(c, m, 0, 1)
-    @test state.training_losses == reverse(m.training_losses)
+    train_losses = m.training_losses
+    @test reverse(state.training_losses) == train_losses # length 3
     @test !IC.done(c, state)
+
     IC.train!(m, 2)
     state = IC.update!(c, m, 0, 2, state)
-    @test state.training_losses == reverse(m.training_losses)
+    train_losses = vcat(train_losses, m.training_losses) # length 2+3 = 5
+
+    @test reverse(state.training_losses) == train_losses
     @test !IC.done(c, state)
     report = IC.takedown(c, 1, state)
     @test !report.done

test/train.jl

@@ -77,3 +77,35 @@ end
                          WithNumberDo(),
                          IterationControl.skip(WithLossDo(), predicate=3)))
 end
+
+@testset "integration test with PQ" begin
+    # get complete training losses:
+    model = SquareRooter(1e40)
+    IC.train!(model, 70)
+    tlosses = IC.training_losses(model)
+
+    # get complete losses:
+    model = SquareRooter(1e40)
+    losses = map(1:70) do _
+        IC.train!(model, 1)
+        IC.loss(model)
+    end
+
+    # train with PQ criterion:
+    K = 5
+    model = SquareRooter(1e40)
+    pq = PQ(k=K)
+    states = Any[]
+    f(s) = push!(states, s)
+    controls = [Step(1), NumberLimit(70), IC.with_state_do(pq, f=f)]
+    IC.train!(model, controls..., verbosity=0)
+
+    # check internal state of PQ makes sense, as far as `loss` and
+    # `training_losses` are concerned:
+    @test map(s->s.loss, states) == losses
+    @test map(enumerate(states)) do (j, s)
+        L = min(K, j) # what length of PQ training_losses should be
+        t= s.loss == losses[j] &&
+            s.training_losses == reverse(tlosses[j-L+1:j])
+    end |> all
+end