Use level names for categories

src/MLJTransforms.jl

@@ -7,7 +7,7 @@ using MLJModelInterface
 using TableOperations
 using StatsBase
 using LinearAlgebra
-
+using OrderedCollections: OrderedDict
 # Other transformers
 using Combinatorics
 import Distributions

src/encoders/contrast_encoder/contrast_encoder.jl

@@ -125,7 +125,7 @@ function contrast_encoder_fit(
             throw(ArgumentError("Mode $feat_mode is not supported."))
         end
 
-        vector_given_value_given_feature = Dict(level=>contrastmatrix[l, :] for (l, level) in enumerate(feat_levels))
+        vector_given_value_given_feature = OrderedDict(level=>contrastmatrix[l, :] for (l, level) in enumerate(feat_levels))
         return vector_given_value_given_feature
     end
 
@@ -159,5 +159,5 @@ Use a fitted contrast encoder to encode the levels of selected categorical varia
 """
 function contrast_encoder_transform(X, cache::Dict)
     vector_given_value_given_feature = cache[:vector_given_value_given_feature]
-    return generic_transform(X, vector_given_value_given_feature, single_feat = false)
+    return generic_transform(X, vector_given_value_given_feature, single_feat = false; use_levelnames = true)
 end

src/encoders/contrast_encoder/interface_mlj.jl

@@ -148,12 +148,12 @@ mach = fit!(machine(encoder, X))
 Xnew = transform(mach, X)
 
 julia > Xnew
-    (name_1 = [1.0, 0.0, 0.0, 0.0],
-    name_2 = [0.0, 1.0, 0.0, 1.0],
+    (name_John = [1.0, 0.0, 0.0, 0.0],
+    name_Mary = [0.0, 1.0, 0.0, 1.0],
     height = [1.85, 1.67, 1.5, 1.67],
-    favnum_1 = [0.0, 1.0, 0.0, -1.0],
-    favnum_2 = [2.0, -1.0, 0.0, -1.0],
-    favnum_3 = [-1.0, -1.0, 3.0, -1.0],
+    favnum_5 = [0.0, 1.0, 0.0, -1.0],
+    favnum_7 = [2.0, -1.0, 0.0, -1.0],
+    favnum_10 = [-1.0, -1.0, 3.0, -1.0],
     age = [23, 23, 14, 23],)
 ```
 

src/encoders/target_encoding/interface_mlj.jl

@@ -51,6 +51,8 @@ struct TargetEncoderResult{
     y_stat_given_feat_level::Dict{A, A}
     task::S            # "Regression", "Classification" 
     num_classes::I     # num_classes in case of classification
+    y_classes::A      # y_classes in case of classification
+
 end
 
 
@@ -76,6 +78,7 @@ function MMI.fit(transformer::TargetEncoder, verbosity::Int, X, y)
         generic_cache[:y_stat_given_feat_level],
         generic_cache[:task],
         generic_cache[:num_classes],
+        generic_cache[:y_classes],
     )
     report = (encoded_features = generic_cache[:encoded_features],)        # report only has list of encoded features
     cache = nothing
@@ -90,6 +93,7 @@ function MMI.transform(transformer::TargetEncoder, fitresult, Xnew)
             fitresult.y_stat_given_feat_level,
         :num_classes => fitresult.num_classes,
         :task => fitresult.task,
+        :y_classes => fitresult.y_classes,
     )
     Xnew_transf = target_encoder_transform(Xnew, generic_cache)
     return Xnew_transf

src/encoders/target_encoding/target_encoding.jl

@@ -216,6 +216,7 @@ function target_encoder_fit(
         :num_classes => (task == "Regression") ? -1 : length(y_classes),
         :y_stat_given_feat_level => y_stat_given_feat_level,
         :encoded_features => encoded_features,
+        :y_classes => (task == "Regression") ? nothing : y_classes,
     )
     return cache
 end
@@ -244,11 +245,13 @@ function target_encoder_transform(X, cache)
     task = cache[:task]
     y_stat_given_feat_level = cache[:y_stat_given_feat_level]
     num_classes = cache[:num_classes]
+    y_classes = cache[:y_classes]
 
     return generic_transform(
         X,
         y_stat_given_feat_level;
         single_feat = task == "Regression" || (task == "Classification" && num_classes < 3),
-    )
+        use_levelnames = true,
+        custom_levels = y_classes,)
 end
 

src/generic.jl

@@ -66,19 +66,37 @@ end
 """
 **Private method.**
 
-Function to generate new feature names: feat_name_0, feat_name_1,..., feat_name_n
+Function to generate new feature names: feat_name_0, feat_name_1,..., feat_name_n or if possible,
+feat_name_level_0, feat_name_level_1,..., feat_name_level_n
 """
-function generate_new_feat_names(feat_name, num_inds, existing_names)
-    conflict = true        # will be kept true as long as there is a conflict
-    count = 1            # number of conflicts+1 = number of underscores
+function generate_new_feat_names(
+    feat_name,
+    num_inds,
+    levels,
+    existing_names;
+    use_levelnames = true,
+)
+    # Convert levels (e.g. KeySet or Tuple) to an indexable vector
+    levels_vec = collect(levels)
+
+    conflict = true        # true while there's a name clash
+    count = 1              # number of underscores in the suffix
+    new_column_names = Symbol[]
 
-    new_column_names = []
     while conflict
         suffix = repeat("_", count)
-        new_column_names = [Symbol("$(feat_name)$(suffix)$i") for i in 1:num_inds]
+        if use_levelnames
+            # Always use the first num_inds level names
+            new_column_names = [ Symbol("$(feat_name)$(suffix)$(levels_vec[i])") for i in 1:num_inds ]
+        else
+            # Always use numeric indices
+            new_column_names = [ Symbol("$(feat_name)$(suffix)$i") for i in 1:num_inds ]
+        end
+        # Check for collisions
         conflict = any(name -> name in existing_names, new_column_names)
         count += 1
     end
+
     return new_column_names
 end
 
@@ -88,26 +106,30 @@ end
 **Private method.**
 
 Given a table `X` and a dictionary `mapping_per_feat_level` which maps each level for each column in
-a subset of categorical features of X into a scalar or a vector (as specified in single_feat)
+a subset of categorical features of X into a scalar or a vector (as specified in `single_feat`)
 
   - transforms each value (some level) in each column in `X` using the function in `mapping_per_feat_level`
-    into a scalar (single_feat=true)
+    into a scalar (`single_feat=true`)
 
   - transforms each value (some level) in each column in `X` using the function in `mapping_per_feat_level`
-    into a set of k features where k is the length of the vector (single_feat=false)
+    into a set of `k` features where `k` is the length of the vector (`single_feat=false`)
   - In both cases it attempts to preserve the type of the table.
   - In the latter case, it assumes that all levels under the same category are mapped to vectors of the same length. Such
     assumption is necessary because any column in X must correspond to a constant number of features
     in the output table (which is equal to k).
   - Features not in the dictionary are mapped to themselves (i.e., not changed).
-  - Levels not in the nested dictionary are mapped to themselves if `ignore unknown` is true else raise an error.
-  - If `ensure_categorical` is true, then any input categorical column will remain categorical
+  - Levels not in the nested dictionary are mapped to themselves if `identity_map_unknown` is true else raise an error.
+  - use_levelnames: if true, the new feature names are generated using the level names when the transform generates multiple features;
+    else they are generated using the indices of the levels.
+  - custom_levels: if not `nothing`, then the levels of the categorical features are replaced by the custom_levels
 """
 function generic_transform(
     X,
     mapping_per_feat_level;
     single_feat = true,
     ignore_unknown = false,
+    use_levelnames = false,
+    custom_levels = nothing,
     ensure_categorical = false,
 )
     feat_names = Tables.schema(X).names
@@ -149,7 +171,9 @@ function generic_transform(
                 feat_names_with_inds = generate_new_feat_names(
                     feat_name,
                     length(first(mapping_per_feat_level[feat_name])[2]),
-                    feat_names,
+                    (custom_levels === nothing) ? keys(mapping_per_feat_level[feat_name]) : custom_levels,
+                    feat_names;
+                    use_levelnames = use_levelnames,
                 )
                 push!(new_feat_names, feat_names_with_inds...)
             end

test/encoders/target_encoding.jl

@@ -277,22 +277,21 @@ end
     X_tr = target_encoder_transform(X, cache)
 
     enc = (col, level) -> cache[:y_stat_given_feat_level][col][level]
-
     target = (
-        A_1 = [enc(:A, X[:A][i])[1] for i in 1:10],
-        A_2 = [enc(:A, X[:A][i])[2] for i in 1:10],
-        A_3 = [enc(:A, X[:A][i])[3] for i in 1:10],
+        A_0 = [enc(:A, X[:A][i])[1] for i in 1:10],
+        A_1 = [enc(:A, X[:A][i])[2] for i in 1:10],
+        A_2 = [enc(:A, X[:A][i])[3] for i in 1:10],
         B = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0],
-        C_1 = [enc(:C, X[:C][i])[1] for i in 1:10],
-        C_2 = [enc(:C, X[:C][i])[2] for i in 1:10],
-        C_3 = [enc(:C, X[:C][i])[3] for i in 1:10],
-        D_1 = [enc(:D, X[:D][i])[1] for i in 1:10],
-        D_2 = [enc(:D, X[:D][i])[2] for i in 1:10],
-        D_3 = [enc(:D, X[:D][i])[3] for i in 1:10],
+        C_0 = [enc(:C, X[:C][i])[1] for i in 1:10],
+        C_1 = [enc(:C, X[:C][i])[2] for i in 1:10],
+        C_2 = [enc(:C, X[:C][i])[3] for i in 1:10],
+        D_0 = [enc(:D, X[:D][i])[1] for i in 1:10],
+        D_1 = [enc(:D, X[:D][i])[2] for i in 1:10],
+        D_2 = [enc(:D, X[:D][i])[3] for i in 1:10],
         E = [1, 2, 3, 4, 5, 6, 6, 3, 2, 1],
-        F_1 = [enc(:F, X[:F][i])[1] for i in 1:10],
-        F_2 = [enc(:F, X[:F][i])[2] for i in 1:10],
-        F_3 = [enc(:F, X[:F][i])[3] for i in 1:10],
+        F_0 = [enc(:F, X[:F][i])[1] for i in 1:10],
+        F_1 = [enc(:F, X[:F][i])[2] for i in 1:10],
+        F_2 = [enc(:F, X[:F][i])[3] for i in 1:10],
     )
     for col in keys(target)
         @test all(X_tr[col] .== target[col])

test/generic.jl

@@ -27,21 +27,38 @@ push!(dataset_forms, create_dummy_dataset(:regression, as_dataframe=false, retur
 push!(dataset_forms, create_dummy_dataset(:regression, as_dataframe=true, return_y=false))
 
 @testset "Generate New feature names Function Tests" begin
-    # Test 1: No initial conflicts
-    @testset "No Initial Conflicts" begin
-        existing_names = []
-        names = generate_new_feat_names("feat", 3, existing_names)
-        @test names == [Symbol("feat_1"), Symbol("feat_2"), Symbol("feat_3")]
+    levels = ("A", "B", "C")
+
+    # Test 1: No initial conflicts, indices mode (use_levelnames=false)
+    @testset "No Initial Conflicts (Indices)" begin
+        existing_names = Symbol[]
+        names = generate_new_feat_names("feat", 2, levels, existing_names; use_levelnames=false)
+        @test names == [Symbol("feat_1"), Symbol("feat_2")]
+    end
+
+    # Test 2: No conflicts, level-names mode (default use_levelnames=true)
+    @testset "No Initial Conflicts (Level Names)" begin
+        existing_names = Symbol[]
+        names = generate_new_feat_names("feat", 3, levels, existing_names)
+        @test names == [Symbol("feat_A"), Symbol("feat_B"), Symbol("feat_C")]
     end
 
-    # Test 2: Handle initial conflict by adding underscores
-    @testset "Initial Conflict Resolution" begin
-        existing_names = [Symbol("feat_1"), Symbol("feat_2"), Symbol("feat_3")]
-        names = generate_new_feat_names("feat", 3, existing_names)
-        @test names == [Symbol("feat__1"), Symbol("feat__2"), Symbol("feat__3")]
+    # Test 3: Handle initial conflict by adding underscores (indices)
+    @testset "Initial Conflict Resolution (Indices)" begin
+        existing_names = [Symbol("feat_1"), Symbol("feat_2")]
+        names = generate_new_feat_names("feat", 2, levels, existing_names; use_levelnames=false)
+        @test names == [Symbol("feat__1"), Symbol("feat__2")]
+    end
+
+    # Test 4: Handle initial conflict by adding underscores (level names)
+    @testset "Initial Conflict Resolution (Level Names)" begin
+        existing_names = [Symbol("feat_A"), Symbol("feat_B"), Symbol("feat_C")]
+        names = generate_new_feat_names("feat", 3, levels, existing_names)
+        @test names == [Symbol("feat__A"), Symbol("feat__B"), Symbol("feat__C")]
     end
 end
 
+
 # Dummy encoder that maps each level to its hash (some arbitrary function)
 function dummy_encoder_fit(
     X,