dump MLJModelInterface references in tests

Author: ablaom

test/encoders/contrast_encoder.jl

@@ -358,7 +358,7 @@ end
     encoder = ContrastEncoder(ignore = true, ordered_factor = false)
     mach = machine(encoder, X)
     fit!(mach)
-    Xnew_transf = MMI.transform(mach, X)
+    Xnew_transf = MLJBase.transform(mach, X)
 
     # same output
     @test X_transf == Xnew_transf
@@ -392,7 +392,7 @@ end
             buildmatrix = matrix_func[i],
         )
         mach = fit!(machine(encoder, X))
-        Xnew = MMI.transform(mach, X)
+        Xnew = MLJBase.transform(mach, X)
 
         # Test Consistency with Types
         scs = schema(Xnew).scitypes

test/encoders/frequency_encoder.jl

@@ -5,7 +5,7 @@ using MLJTransforms: frequency_encoder_fit, frequency_encoder_transform
 
     X = dataset_forms[1]
     normalize = [false, true]
-    A_col, C_col, D_col, F_col = MMI.selectcols(X, [1, 3, 4, 6])
+    A_col, C_col, D_col, F_col = selectcols(X, [1, 3, 4, 6])
     for norm in normalize
         result = frequency_encoder_fit(X; normalize = norm)[:statistic_given_feat_val]
         enc =
@@ -72,7 +72,7 @@ end
         encoder = FrequencyEncoder(ignore = true, ordered_factor = false)
         mach = machine(encoder, X)
         fit!(mach)
-        Xnew_transf = MMI.transform(mach, X)
+        Xnew_transf = MLJBase.transform(mach, X)
 
         # same output
         @test X_transf == Xnew_transf
@@ -111,7 +111,7 @@ end
 
     encoder = FrequencyEncoder(ordered_factor = false, normalize = false)
     mach = fit!(machine(encoder, X))
-    Xnew = MMI.transform(mach, X)
+    Xnew = MLJBase.transform(mach, X)
 
 
     scs = schema(Xnew).scitypes

test/encoders/missingness_encoding.jl

@@ -170,7 +170,7 @@ end
     encoder = MissingnessEncoder(ignore = true, ordered_factor = false)
     mach = machine(encoder, X)
     fit!(mach)
-    Xnew_transf = MMI.transform(mach, X)
+    Xnew_transf =  MLJBase.transform(mach, X)
 
     # same output
     @test isequal(X_transf, Xnew_transf)
@@ -197,7 +197,7 @@ end
 
     encoder = MissingnessEncoder()
     mach = fit!(machine(encoder, Xm))
-    Xnew = MMI.transform(mach, Xm)
+    Xnew = MLJBase.transform(mach, Xm)
 
     schema(Xm)
     schema(Xnew)

test/encoders/ordinal_encoding.jl

@@ -15,7 +15,7 @@ push!(
     @test ordinal_encoder_fit(dataset_forms[1]) == ordinal_encoder_fit(dataset_forms[2])
     X = dataset_forms[1]
     result = ordinal_encoder_fit(X)[:index_given_feat_level]
-    A_col, C_col, D_col, F_col = MMI.selectcols(X, [1, 3, 4, 6])
+    A_col, C_col, D_col, F_col = selectcols(X, [1, 3, 4, 6])
     true_output = Dict{Symbol, Dict{Any, AbstractFloat}}(
         :F => Dict(
             "m" => findfirst(==("m"), levels(F_col)),
@@ -70,7 +70,7 @@ end
         encoder = OrdinalEncoder(ignore = true, ordered_factor = false)
         mach = machine(encoder, X)
         fit!(mach)
-        Xnew_transf = MMI.transform(mach, X)
+        Xnew_transf = MLJBase.transform(mach, X)
 
         # same output
         @test X_transf == Xnew_transf
@@ -108,7 +108,7 @@ end
 
     encoder = OrdinalEncoder(ordered_factor = false)
     mach = fit!(machine(encoder, X))
-    Xnew = MMI.transform(mach, X)
+    Xnew = MLJBase.transform(mach, X)
 
     scs = schema(Xnew).scitypes
     ts  = schema(Xnew).types
@@ -123,7 +123,7 @@ end
     ## Int32 case
     encoder = OrdinalEncoder(ordered_factor = false, output_type = Int32)
     mach = fit!(machine(encoder, X))
-    Xnew = MMI.transform(mach, X)
+    Xnew = MLJBase.transform(mach, X)
     scs = schema(Xnew).scitypes
     ts = schema(Xnew).types
     # Check scitypes for previously categorical features

test/encoders/target_encoding.jl

@@ -63,7 +63,7 @@ end
     X, y = classification_forms[1]
     n = length(y)
 
-    A_col, C_col, D_col, F_col = MMI.selectcols(X, [1, 3, 4, 6])
+    A_col, C_col, D_col, F_col = selectcols(X, [1, 3, 4, 6])
     true_output = Dict{Symbol, Dict{Any, AbstractFloat}}(
         :F => Dict(
             "m" => sum(y[F_col.=="m"] .== 0) / length(y[F_col.=="m"]),
@@ -119,7 +119,7 @@ end
     n = length(y)
     μ̂ = mean(y)
 
-    A_col, C_col, D_col, F_col = MMI.selectcols(X, [1, 3, 4, 6])
+    A_col, C_col, D_col, F_col = selectcols(X, [1, 3, 4, 6])
     true_output = Dict{Symbol, Dict{Any, AbstractFloat}}(
         :F => Dict(
             "m" => mean(y[F_col.=="m"]),
@@ -172,7 +172,7 @@ end
     y_classes = classes(y)
     n = length(y)
 
-    A_col, C_col, D_col, F_col = MMI.selectcols(X, [1, 3, 4, 6])
+    A_col, C_col, D_col, F_col = selectcols(X, [1, 3, 4, 6])
     true_output = Dict{Symbol, Dict{Any, AbstractVector{AbstractFloat}}}(
         :F => Dict(
             "m" =>
@@ -320,7 +320,7 @@ end
             TargetEncoder(ignore = true, ordered_factor = false, lambda = 0.5, m = 1.0)
         mach = machine(encoder, X, y)
         fit!(mach)
-        Xnew_transf = MMI.transform(mach, X)
+        Xnew_transf = MLJBase.transform(mach, X)
 
         # same output
         @test X_transf == Xnew_transf
@@ -386,7 +386,7 @@ end
 
     encoder = TargetEncoder(ordered_factor = false, lambda = 1.0, m = 0)
     mach = fit!(machine(encoder, X, y))
-    Xnew = MMI.transform(mach, X)
+    Xnew = MLJBase.transform(mach, X)
 
     scs = schema(Xnew).scitypes
     ts  = schema(Xnew).types

test/generic.jl

@@ -142,7 +142,7 @@ end
 
 @testset "Test generic fit output" begin
     X = dataset_forms[1]
-    A_col, C_col, D_col, F_col = MMI.selectcols(X, [1, 3, 4, 6])
+    A_col, C_col, D_col, F_col = selectcols(X, [1, 3, 4, 6])
     result = dummy_encoder_fit(X)[:hash_given_feat_val]
     enc = (col, level) -> (hash(level))
     true_output = Dict{Symbol, Dict{Any, Any}}(

test/runtests.jl

@@ -7,8 +7,6 @@ using StatsBase
 using LinearAlgebra
 import StatsModels
 using Random
-import MLJModelInterface
-const MMI = MLJModelInterface
 using LinearAlgebra
 
 # Other transformers

test/transformers/cardinality_reducer.jl

@@ -208,7 +208,7 @@ end
     encoder = CardinalityReducer(min_frequency = 0.1, ignore = true, ordered_factor = false)
     mach = machine(encoder, X)
     fit!(mach)
-    Xnew_transf = MMI.transform(mach, X)
+    Xnew_transf = MLJBase.transform(mach, X)
 
     # same output
     @test X_transf == Xnew_transf
@@ -240,11 +240,11 @@ end
 
     encoder = CardinalityReducer(ordered_factor = false, min_frequency = 3)
     mach = fit!(machine(encoder, X))
-    Xnew = MMI.transform(mach, X)
+    Xnew = MLJBase.transform(mach, X)
     @test schema(X).types == schema(Xnew).types
     @test all(s -> (s <: Multiclass), schema(Xnew).scitypes)
 end
 
 
 # Look into MLJModelInterfaceTest
-# Add tests to ensure categorical feature properties are as expected
+# Add tests to ensure categorical feature properties are as expected