Merge pull request #26 from JuliaAI/dev

For a 0.1.4 Release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJBalancing"
 uuid = "45f359ea-796d-4f51-95a5-deb1a414c586"
 authors = ["Essam Wisam <[email protected]>", "Anthony Blaom <[email protected]> and contributors"]
-version = "0.1.3"
+version = "0.1.4"
 
 [deps]
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"

src/balanced_bagging.jl

@@ -144,17 +144,16 @@ MMI.metadata_pkg(
     package_uuid = "45f359ea-796d-4f51-95a5-deb1a414c586",
     package_url = "https://github.com/JuliaAI/MLJBalancing.jl",
     is_pure_julia = true,
+    is_wrapper = true,
 )
 
 MMI.metadata_model(
     BalancedBaggingClassifier,
-    input_scitype = Union{Union{Infinite,Finite}},
-    output_scitype = Union{Union{Infinite,Finite}},
-    target_scitype = AbstractVector,
+    target_scitype = AbstractVector{<:Finite},
     load_path = "MLJBalancing." * string(BalancedBaggingClassifier),
 )
 
-MMI.iteration_parameter(::Type{<:BalancedBaggingClassifier{P}}) where {P} =
+MMI.iteration_parameter(::Type{<:BalancedBaggingClassifier{<:Any,<:Any,P}}) where P =
     MLJBase.prepend(:model, iteration_parameter(P))
 for trait in [
     :input_scitype,
@@ -173,7 +172,8 @@ for trait in [
     :prediction_type,
 ]
     quote
-        MMI.$trait(::Type{<:BalancedBaggingClassifier{P}}) where {P} = MMI.$trait(P)
+        MMI.$trait(::Type{<:BalancedBaggingClassifier{<:Any,<:Any, P}}) where {P} =
+            MMI.$trait(P)
     end |> eval
 end
 

test/balanced_bagging.jl

@@ -120,6 +120,10 @@ end
     mach = machine(modelo, X, y)
     fit!(mach)
     @test report(mach) == (chosen_T = 9,)
+
+    ## traits
+    @test fit_data_scitype(modelo) == fit_data_scitype(model)
+    @test is_wrapper(modelo) 
 end
 
 
@@ -137,4 +141,4 @@ end
     @test_logs (:warn, MLJBalancing.WRN_MODEL_GIVEN) begin
         BalancedBaggingClassifier(model; model=model, T=T, rng=R)
     end
-end
+end

test/balanced_model.jl

@@ -52,7 +52,12 @@
 	fit!(mach)
 	y_pred2 = MLJBase.predict(mach, X_test)
 
-	@test y_pred ≈ y_pred2
+        @test y_pred ≈ y_pred2
+
+        # traits:
+        @test fit_data_scitype(balanced_model) ==  fit_data_scitype(model_prob)
+        @test is_wrapper(balanced_model)
+
 
 	### 2. Make a pipeline of the three balancers and a deterministic model
 	## ordinary way
@@ -99,4 +104,4 @@ end
     @test_logs (:warn, MLJBalancing.WRN_MODEL_GIVEN) begin
         BalancedModel(model; model=model, balancer1=balancer1)
     end
-end
+end