training_scitype -> fit_data_scitype

Author: ablaom

src/MLJModelInterface.jl

@@ -4,7 +4,7 @@ const MODEL_TRAITS = [
     :input_scitype,
     :output_scitype,
     :target_scitype,
-    :training_scitype,
+    :fit_data_scitype,
     :predict_scitype,
     :transform_scitype,
     :inverse_transform_scitype,

src/model_traits.jl

@@ -21,9 +21,9 @@ for M in ABSTRACT_MODEL_SUBTYPES
     @eval(StatisticalTraits.abstract_type(::Type{<:$M}) = $M)
 end
 
-StatisticalTraits.training_scitype(M::Type{<:Model}) = input_scitype(M)
-StatisticalTraits.training_scitype(::Type{<:Static}) = Tuple{}
-function StatisticalTraits.training_scitype(M::Type{<:Supervised})
+StatisticalTraits.fit_data_scitype(M::Type{<:Model}) = input_scitype(M)
+StatisticalTraits.fit_data_scitype(::Type{<:Static}) = Tuple{}
+function StatisticalTraits.fit_data_scitype(M::Type{<:Supervised})
     I = input_scitype(M)
     T = target_scitype(M)
     ret = Tuple{I,T}

test/model_traits.jl

@@ -129,17 +129,17 @@ M.input_scitype(::Type{<:S2}) = Table(Continuous)
     @test inverse_transform_scitype(S2()) == Table(Continuous)
 end
 
-@testset "abstract_type, training_scitype" begin
+@testset "abstract_type, fit_data_scitype" begin
     @test abstract_type(P2()) == Probabilistic
     @test abstract_type(S1()) == Supervised
     @test abstract_type(U1()) == Unsupervised
     @test abstract_type(D1()) == Deterministic
     @test abstract_type(P1()) == Probabilistic
 
-    @test training_scitype(P2()) ==
+    @test fit_data_scitype(P2()) ==
         Tuple{Table(Continuous),AbstractVector{<:Multiclass}}
-    @test training_scitype(U2()) == Table(Continuous)
-    @test training_scitype(S2()) == Tuple{}
+    @test fit_data_scitype(U2()) == Table(Continuous)
+    @test fit_data_scitype(S2()) == Tuple{}
 end
 
 true