✨ Fix table bug

src/MLJTransforms.jl

@@ -5,7 +5,7 @@ using Tables
 # https://github.com/JuliaAI/MLJBase.jl/issues/1002
 import ScientificTypes: elscitype, schema, coerce, ScientificTimeType
 using MLJModelInterface # exports `scitype`, which will call `ScientificTypes.scitype`,
-                        # once MLJBase is loaded (but this is not a dependency!)
+# once MLJBase is loaded (but this is not a dependency!)
 using CategoricalArrays
 using TableOperations
 using StatsBase
@@ -29,27 +29,27 @@ include("utils.jl")
 include("encoders/target_encoding/errors.jl")
 include("encoders/target_encoding/target_encoding.jl")
 include("encoders/target_encoding/interface_mlj.jl")
-export  TargetEncoder
+export TargetEncoder
 
 # Ordinal encoding
 include("encoders/ordinal_encoding/ordinal_encoding.jl")
 include("encoders/ordinal_encoding/interface_mlj.jl")
-export  OrdinalEncoder
+export OrdinalEncoder
 
 # Frequency encoding
 include("encoders/frequency_encoding/frequency_encoding.jl")
 include("encoders/frequency_encoding/interface_mlj.jl")
 export frequency_encoder_fit, frequency_encoder_transform, FrequencyEncoder
-export  FrequencyEncoder
+export FrequencyEncoder
 
 # Cardinality reduction
 include("transformers/cardinality_reducer/cardinality_reducer.jl")
 include("transformers/cardinality_reducer/interface_mlj.jl")
 export cardinality_reducer_fit, cardinality_reducer_transform, CardinalityReducer
-export  CardinalityReducer
+export CardinalityReducer
 include("encoders/missingness_encoding/missingness_encoding.jl")
 include("encoders/missingness_encoding/interface_mlj.jl")
-export  MissingnessEncoder
+export MissingnessEncoder
 
 # Contrast encoder
 include("encoders/contrast_encoder/contrast_encoder.jl")
@@ -65,7 +65,6 @@ include("transformers/other_transformers/one_hot_encoder.jl")
 include("transformers/other_transformers/standardizer.jl")
 include("transformers/other_transformers/univariate_boxcox_transformer.jl")
 include("transformers/other_transformers/univariate_discretizer.jl")
-include("transformers/other_transformers/metadata_shared.jl")
 
 export UnivariateDiscretizer,
     UnivariateStandardizer, Standardizer, UnivariateBoxCoxTransformer,

src/generic.jl

@@ -59,7 +59,7 @@ function generic_fit(X,
     # 4. Use feature mapper to compute the mapping of each level in each column
     encoded_features = Symbol[]# to store column that were actually encoded
     for feat_name in feat_names
-        feat_col = Tables.getcolumn(X, feat_name)
+        feat_col = MMI.selectcols(X, feat_name)
         feat_type = elscitype(feat_col)
         feat_has_allowed_type =
             feat_type <: Union{Missing, Multiclass} ||
@@ -149,7 +149,7 @@ function generic_transform(
     new_feat_names = Symbol[]
     new_cols = []
     for feat_name in feat_names
-        col = Tables.getcolumn(X, feat_name)
+        col = MMI.selectcols(X, feat_name)
         # Create the transformation function for each column
         if feat_name in keys(mapping_per_feat_level)
             if !ignore_unknown

src/transformers/other_transformers/continuous_encoder.jl

@@ -75,6 +75,14 @@ metadata_model(ContinuousEncoder,
     output_scitype = Table(Continuous),
     load_path    = "MLJTransforms.ContinuousEncoder")
 
+# Package metadata for docstring generation
+metadata_pkg(ContinuousEncoder,
+    package_name = "MLJTransforms",
+    package_uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6",
+    package_url = "https://github.com/JuliaAI/MLJTransforms.jl",
+    is_pure_julia = true,
+    package_license = "MIT")
+
 """
 $(MLJModelInterface.doc_header(ContinuousEncoder))
 

src/transformers/other_transformers/fill_imputer.jl

@@ -194,6 +194,14 @@ metadata_model(FillImputer,
     output_scitype = Table,
     load_path = "MLJTransforms.FillImputer")
 
+# Package metadata for docstring generation
+metadata_pkg(UnivariateFillImputer,
+    package_name = "MLJTransforms",
+    package_uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6",
+    package_url = "https://github.com/JuliaAI/MLJTransforms.jl",
+    is_pure_julia = true,
+    package_license = "MIT")
+
 """
 $(MLJModelInterface.doc_header(UnivariateFillImputer))
 
@@ -294,7 +302,13 @@ For imputing tabular data, use [`FillImputer`](@ref).
 """
 UnivariateFillImputer
 
-
+# Package metadata for docstring generation  
+metadata_pkg(FillImputer,
+    package_name = "MLJTransforms",
+    package_uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6",
+    package_url = "https://github.com/JuliaAI/MLJTransforms.jl",
+    is_pure_julia = true,
+    package_license = "MIT")
 
 """
 $(MLJModelInterface.doc_header(FillImputer))

src/transformers/other_transformers/interaction_transformer.jl

@@ -40,6 +40,14 @@ metadata_model(InteractionTransformer,
     human_name = "interaction transformer",
     load_path = "MLJTransforms.InteractionTransformer")
 
+# Package metadata for docstring generation
+metadata_pkg(InteractionTransformer,
+    package_name = "MLJTransforms",
+    package_uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6",
+    package_url = "https://github.com/JuliaAI/MLJTransforms.jl",
+    is_pure_julia = true,
+    package_license = "MIT")
+
 """
 $(MLJModelInterface.doc_header(InteractionTransformer))
 

src/transformers/other_transformers/one_hot_encoder.jl

@@ -165,6 +165,14 @@ metadata_model(OneHotEncoder,
     human_name = "one-hot encoder",
     load_path = "MLJTransforms.OneHotEncoder")
 
+# Package metadata for docstring generation
+metadata_pkg(OneHotEncoder,
+    package_name = "MLJTransforms",
+    package_uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6",
+    package_url = "https://github.com/JuliaAI/MLJTransforms.jl",
+    is_pure_julia = true,
+    package_license = "MIT")
+
 """
 $(MLJModelInterface.doc_header(OneHotEncoder))
 

src/transformers/other_transformers/standardizer.jl

@@ -236,6 +236,14 @@ metadata_model(Standardizer,
     output_scitype = Union{Table, AbstractVector{<:Continuous}},
     load_path = "MLJTransforms.Standardizer")
 
+# Package metadata for docstring generation
+metadata_pkg(Standardizer,
+    package_name = "MLJTransforms",
+    package_uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6",
+    package_url = "https://github.com/JuliaAI/MLJTransforms.jl",
+    is_pure_julia = true,
+    package_license = "MIT")
+
 """
 $(MLJModelInterface.doc_header(Standardizer))
 

src/transformers/other_transformers/univariate_boxcox_transformer.jl

@@ -93,6 +93,14 @@ metadata_model(UnivariateBoxCoxTransformer,
     human_name = "single variable Box-Cox transformer",
     load_path = "MLJTransforms.UnivariateBoxCoxTransformer")
 
+# Package metadata for docstring generation
+metadata_pkg(UnivariateBoxCoxTransformer,
+    package_name = "MLJTransforms",
+    package_uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6",
+    package_url = "https://github.com/JuliaAI/MLJTransforms.jl",
+    is_pure_julia = true,
+    package_license = "MIT")
+
 """
 $(MLJModelInterface.doc_header(UnivariateBoxCoxTransformer))
 

src/transformers/other_transformers/univariate_discretizer.jl

@@ -96,6 +96,13 @@ metadata_model(UnivariateDiscretizer,
     human_name = "single variable discretizer",
     load_path = "MLJTransforms.UnivariateDiscretizer")
 
+# Package metadata for docstring generation
+metadata_pkg(UnivariateDiscretizer,
+    package_name = "MLJTransforms",
+    package_uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6",
+    package_url = "https://github.com/JuliaAI/MLJTransforms.jl",
+    is_pure_julia = true,
+    package_license = "MIT")
 
 """
 $(MLJModelInterface.doc_header(UnivariateDiscretizer))

src/transformers/other_transformers/univariate_time_type_to_continuous.jl

@@ -131,6 +131,14 @@ metadata_model(UnivariateTimeTypeToContinuous,
          "continuous representations of temporally typed data",
     load_path = "MLJTransforms.UnivariateTimeTypeToContinuous")
 
+# Package metadata for docstring generation
+metadata_pkg(UnivariateTimeTypeToContinuous,
+    package_name = "MLJTransforms",
+    package_uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6",
+    package_url = "https://github.com/JuliaAI/MLJTransforms.jl",
+    is_pure_julia = true,
+    package_license = "MIT")
+
 """
 $(MLJModelInterface.doc_header(UnivariateTimeTypeToContinuous))
 

test/generic_table_types.jl

@@ -0,0 +1,69 @@
+using Test
+using Tables
+using CategoricalArrays
+
+
+
+@testset "Generic Table Types Support" begin
+
+    # Create test data as in the issue
+    x = vcat(collect("abc"), fill('d', 100))
+    x = coerce(x, Multiclass)
+
+    # Column table (NamedTuple of vectors) - this already works
+    coltable = (; x)
+
+    # Row table (Vector of NamedTuples) - this was failing
+    rowtable = Tables.rowtable(coltable)
+
+    # List of models that were affected by the issue
+    models_to_test = [
+        CardinalityReducer(),
+        FrequencyEncoder(),
+        MissingnessEncoder(),
+        OrdinalEncoder(),
+    ]
+
+    @testset "Model: $(string(typeof(model)))" for model in models_to_test
+
+        @testset "Column Table Support" begin
+            mach_col = machine(model, coltable)
+            MLJBase.fit!(mach_col, verbosity=0)
+            result_col = MLJBase.transform(mach_col, coltable)
+
+            @test !isempty(Tables.columntable(result_col))
+        end
+
+        @testset "Row Table Support" begin
+            # This should now work after the fix
+            mach_row = machine(model, rowtable)
+            MLJBase.fit!(mach_row, verbosity=0)
+            result_row = MLJBase.transform(mach_row, rowtable)
+
+            @test !isempty(Tables.columntable(result_row))
+        end
+
+        @testset "Consistency Between Table Types" begin
+            # Results should be equivalent regardless of table type
+            mach_col = machine(model, coltable)
+            MLJBase.fit!(mach_col, verbosity=0)
+            result_col = MLJBase.transform(mach_col, coltable)
+
+            mach_row = machine(model, rowtable) 
+            MLJBase.fit!(mach_row, verbosity=0)
+            result_row = MLJBase.transform(mach_row, rowtable)
+
+            # Convert both to column tables for comparison
+            result_col_ct = Tables.columntable(result_col)
+            result_row_ct = Tables.columntable(result_row)
+
+            # Should have same column names
+            @test keys(result_col_ct) == keys(result_row_ct)
+
+            # Should have same values (allowing for potential ordering differences in table types)
+            for col_name in keys(result_col_ct)
+                @test Set(result_col_ct[col_name]) == Set(result_row_ct[col_name])
+            end
+        end
+    end
+end

test/runtests.jl

@@ -20,6 +20,7 @@ _get(x) = CategoricalArrays.DataAPI.unwrap(x)
 
 include("utils.jl")
 include("generic.jl")
+include("generic_table_types.jl")  # Test for issue #42 fix
 include("encoders/target_encoding.jl")
 include("encoders/ordinal_encoding.jl")
 include("encoders/frequency_encoder.jl")