✨ Fix table bug

Author: EssamWisam

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

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")