Merge pull request #219 from JuliaAI/constructor

Overload `constructor` trait for `TunedModel` types

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJTuning"
 uuid = "03970b2e-30c4-11ea-3135-d1576263f10f"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "0.8.6"
+version = "0.8.7"
 
 [deps]
 ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"
@@ -18,7 +18,7 @@ StatisticalMeasuresBase = "c062fc1d-0d66-479b-b6ac-8b44719de4cc"
 ComputationalResources = "0.3"
 Distributions = "0.22,0.23,0.24, 0.25"
 LatinHypercubeSampling = "1.7.2"
-MLJBase = "1.3"
+MLJBase = "1.4"
 ProgressMeter = "1.7.1"
 RecipesBase = "0.8,0.9,1"
 StatisticalMeasuresBase = "0.1.1"

src/tuned_models.jl

@@ -905,17 +905,19 @@ function MLJBase.feature_importances(::EitherTunedModel, fitresult, report)
  return MLJBase.feature_importances(fitresult)
 end
 
+
+
+
+
 ## METADATA
 
 MLJBase.is_wrapper(::Type{<:EitherTunedModel}) = true
 MLJBase.supports_weights(::Type{<:EitherTunedModel{<:Any,M,L}}) where {M,L} =
  MLJBase.supports_weights(M)
 MLJBase.supports_class_weights(::Type{<:EitherTunedModel{<:Any,M,L}}) where {M,L} =
  MLJBase.supports_class_weights(M)
-MLJBase.load_path(::Type{<:ProbabilisticTunedModel}) =
- "MLJTuning.ProbabilisticTunedModel"
-MLJBase.load_path(::Type{<:DeterministicTunedModel}) =
- "MLJTuning.DeterministicTunedModel"
+MLJBase.load_path(::Type{<:EitherTunedModel}) =
+ "MLJTuning.TunedModel"
 MLJBase.package_name(::Type{<:EitherTunedModel}) = "MLJTuning"
 MLJBase.package_uuid(::Type{<:EitherTunedModel}) =
  "03970b2e-30c4-11ea-3135-d1576263f10f"
@@ -928,3 +930,4 @@ MLJBase.input_scitype(::Type{<:EitherTunedModel{T,M,L}}) where {T,M,L} =
  MLJBase.input_scitype(M)
 MLJBase.target_scitype(::Type{<:EitherTunedModel{T,M,L}}) where {T,M,L} =
  MLJBase.target_scitype(M)
+MLJBase.constructor(::Type{<:EitherTunedModel}) = TunedModel

test/tuned_models.jl

@@ -69,6 +69,8 @@ end
  TunedModel(first(r), last(r), range=r, measure=l2),
  )
  tm = @test_logs TunedModel(model=first(r), range=r, measure=l2)
+ @test MLJBase.constructor(tm) == TunedModel
+ @test MLJBase.load_path(tm) == "MLJTuning.TunedModel"
  @test tm.tuning isa RandomSearch
  @test input_scitype(tm) == Table(Continuous)