✍🏻 Rename report variable to better

Author: EssamWisam

src/encoders/missingness_encoding/interface_mlj.jl

@@ -28,7 +28,7 @@ end;
 
 # 4. Fitted parameters (for user access)
 MMI.fitted_params(::MissingnessEncoder, fitresult) = (
-    new_cat_given_col_val = fitresult,
+    label_for_missing_given_feature = fitresult,
 )
 
 # 5. Fit method
@@ -40,7 +40,7 @@ function MMI.fit(transformer::MissingnessEncoder, verbosity::Int, X)
         ordered_factor = transformer.ordered_factor,
         label_for_missing = transformer.label_for_missing,
     )
-    fitresult = generic_cache[:new_cat_given_col_val]
+    fitresult = generic_cache[:label_for_missing_given_feature]
 
     report = (encoded_features = generic_cache[:encoded_features],)        # report only has list of encoded features
     cache = nothing
@@ -51,7 +51,7 @@ end;
 # 6. Transform method
 function MMI.transform(transformer::MissingnessEncoder, fitresult, Xnew)
     generic_cache = Dict(
-        :new_cat_given_col_val =>
+        :label_for_missing_given_feature =>
             fitresult,
     )
     Xnew_transf = missingness_encoder_transform(Xnew, generic_cache)
@@ -119,7 +119,7 @@ and if the raw type subtypes `Number` then the new value is the lowest value in
 
 The fields of `fitted_params(mach)` are:
 
-- `new_cat_given_col_val`: A dictionary that for each column, maps `missing` into some value according to `label_for_missing`
+- `label_for_missing_given_feature`: A dictionary that for each column, maps `missing` into some value according to `label_for_missing`
 
 # Report
 

src/encoders/missingness_encoding/missingness_encoding.jl

@@ -22,7 +22,7 @@ types that are in `Char`, `AbstractString`, and `Number`.
 
 # Returns (in a dict)
 
-  - `new_cat_given_col_val`: A dictionary that for each column, maps `missing` into some value according to `label_for_missing`
+  - `label_for_missing_given_feature`: A dictionary that for each column, maps `missing` into some value according to `label_for_missing`
   - `encoded_features`: The subset of the categorical features of X that were encoded
 """
 function missingness_encoder_fit(
@@ -74,26 +74,26 @@ function missingness_encoder_fit(
         end
 
         # Nonmissing levels remain as is
-        new_cat_given_col_val = Dict{Missing, col_type}()
+        label_for_missing_given_feature = Dict{Missing, col_type}()
 
         # Missing levels are mapped
         if elgrandtype in keys(label_for_missing)
-            new_cat_given_col_val[missing] = label_for_missing[elgrandtype]
+            label_for_missing_given_feature[missing] = label_for_missing[elgrandtype]
         elseif elgrandtype == Number
-            new_cat_given_col_val[missing] = minimum(feat_levels) - 1
+            label_for_missing_given_feature[missing] = minimum(feat_levels) - 1
         else
             throw(ArgumentError(UNSPECIFIED_COL_TYPE_ME(col_type, label_for_missing)))
         end
 
-        return new_cat_given_col_val::Dict{Missing, col_type}
+        return label_for_missing_given_feature::Dict{Missing, col_type}
     end
 
     # 2. Pass it to generic_fit
-    new_cat_given_col_val, encoded_features = generic_fit(
+    label_for_missing_given_feature, encoded_features = generic_fit(
         X, features; ignore = ignore, ordered_factor = ordered_factor, feature_mapper = feature_mapper,
     )
     cache = Dict(
-        :new_cat_given_col_val => new_cat_given_col_val,
+        :label_for_missing_given_feature => label_for_missing_given_feature,
         :encoded_features => encoded_features,
     )
     return cache
@@ -115,7 +115,7 @@ Apply a fitted missingness encoder to a table given the output of `missingness_e
   - `X_tr`: The table with selected features after the selected features are transformed by missingness encoder
 """
 function missingness_encoder_transform(X, cache::Dict)
-    new_cat_given_col_val = cache[:new_cat_given_col_val]
-    return generic_transform(X, new_cat_given_col_val; ignore_unknown = true)
+    label_for_missing_given_feature = cache[:label_for_missing_given_feature]
+    return generic_transform(X, label_for_missing_given_feature; ignore_unknown = true)
 end
 

test/encoders/missingness_encoding.jl

@@ -28,8 +28,8 @@ end
 @testset "Default for Numbers Set Correctly" begin
     X = generate_X_with_missingness()
     cache = missingness_encoder_fit(X)
-    new_cat_given_col_val = cache[:new_cat_given_col_val]
-    @test new_cat_given_col_val[:C][missing] == minimum(levels(X.C)) - 1
+    label_for_missing_given_feature = cache[:label_for_missing_given_feature]
+    @test label_for_missing_given_feature[:C][missing] == minimum(levels(X.C)) - 1
 end
 
 
@@ -57,7 +57,7 @@ end
     result = missingness_encoder_fit(
         X;
         label_for_missing = Dict(AbstractString => "MissingOne", Char => 'X', Number => -90),
-    )[:new_cat_given_col_val]
+    )[:label_for_missing_given_feature]
 
     true_output = Dict{Symbol, Dict{Any, Any}}(
         :A => Dict([(missing, "MissingOne")]),
@@ -148,8 +148,8 @@ end
     @test isequal(X_transf, Xnew_transf)
 
     # fitted parameters is correct
-    new_cat_given_col_val = fitted_params(mach).new_cat_given_col_val
-    @test new_cat_given_col_val == generic_cache[:new_cat_given_col_val]
+    label_for_missing_given_feature = fitted_params(mach).label_for_missing_given_feature
+    @test label_for_missing_given_feature == generic_cache[:label_for_missing_given_feature]
 
     # Test report
     @test report(mach) == (encoded_features = generic_cache[:encoded_features],)