Merge pull request #212 from JuliaAI/dev

For a 0.8.3 release

Author: ablaom

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJTuning"
 uuid = "03970b2e-30c4-11ea-3135-d1576263f10f"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "0.8.2"
+version = "0.8.3"
 
 [deps]
 ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"

src/tuned_models.jl

@@ -441,6 +441,7 @@ function event!(metamodel,
               measure     = E.measure,
               measurement = E.measurement,
               per_fold    = E.per_fold,
+              evaluation = E,
               metadata    = metadata)
     entry = merge(entry0, extras(tuning, history, state, E))
     if verbosity > 2

test/serialization.jl

@@ -51,7 +51,7 @@ end
 
     @test MLJBase.predict(smach, X) == MLJBase.predict(mach, X)
     @test fitted_params(smach) isa NamedTuple
-    @test report(smach) == report(mach)
+    @test report(smach).best_model == report(mach).best_model
 
     rm(filename)
 

test/tuned_models.jl

@@ -455,4 +455,24 @@ end
     @test MLJBase.predict(mach2, (; x = rand(2))) ≈ fill(42.0, 2)
 end
 
+@testset_accelerated "full evaluation object" accel begin
+    X, y = make_regression(100, 2)
+    dcr = DeterministicConstantRegressor()
+
+    homodel = TunedModel(
+        models=fill(dcr, 10),
+        resampling=Holdout(rng=StableRNG(1234)),
+        acceleration_resampling=accel,
+        measure=mae
+    )
+    homach = machine(homodel, X, y)
+    fit!(homach, verbosity=0);
+    horep = report(homach)
+    evaluations = getproperty.(horep.history, :evaluation)
+    measurements = getproperty.(evaluations, :measurement)
+    models = getproperty.(evaluations, :model)
+    @test all(==(measurements[1]), measurements)
+    @test all(==(dcr), models)
+end
+
 true