✍🏻 Rename vec_given_feat_val

Author: EssamWisam

src/encoders/contrast_encoder/contrast_encoder.jl

@@ -123,18 +123,18 @@ function contrast_encoder_fit(
             throw(ArgumentError("Mode $feat_mode is not supported."))
         end
 
-        vec_given_feat_val = Dict(level=>contrastmatrix[l, :] for (l, level) in enumerate(feat_levels))
-        return vec_given_feat_val
+        vector_given_value_given_feature = Dict(level=>contrastmatrix[l, :] for (l, level) in enumerate(feat_levels))
+        return vector_given_value_given_feature
     end
 
     # 2. Pass it to generic_fit
-    vec_given_feat_val, encoded_features = generic_fit(
+    vector_given_value_given_feature, encoded_features = generic_fit(
         X, features; ignore = ignore, ordered_factor = ordered_factor,
         feature_mapper = feature_mapper,
     )
 
     cache = Dict(
-        :vec_given_feat_val  => vec_given_feat_val,
+        :vector_given_value_given_feature  => vector_given_value_given_feature,
         :encoded_features => encoded_features,
     )
 
@@ -156,6 +156,6 @@ Use a fitted contrast encoder to encode the levels of selected categorical varia
   - `X_tr`: The table with selected features after the selected features are encoded by contrast encoding.
 """
 function contrast_encoder_transform(X, cache::Dict)
-    vec_given_feat_val = cache[:vec_given_feat_val]
-    return generic_transform(X, vec_given_feat_val, single_feat = false)
+    vector_given_value_given_feature = cache[:vector_given_value_given_feature]
+    return generic_transform(X, vector_given_value_given_feature, single_feat = false)
 end

src/encoders/contrast_encoder/interface_mlj.jl

@@ -23,7 +23,7 @@ end;
 
 # 4. Fitted parameters (for user access)
 MMI.fitted_params(::ContrastEncoder, fitresult) = (
-    vec_given_feat_val = fitresult,
+    vector_given_value_given_feature = fitresult,
 )
 
 # 5. Fit method
@@ -36,7 +36,7 @@ function MMI.fit(transformer::ContrastEncoder, verbosity::Int, X)
         buildmatrix = transformer.buildmatrix,
         ordered_factor = transformer.ordered_factor,
     )
-    fitresult = generic_cache[:vec_given_feat_val]
+    fitresult = generic_cache[:vector_given_value_given_feature]
 
     report = (encoded_features = generic_cache[:encoded_features],)        # report only has list of encoded features
     cache = nothing
@@ -47,7 +47,7 @@ end;
 # 6. Transform method
 function MMI.transform(transformer::ContrastEncoder, fitresult, Xnew)
     generic_cache = Dict(
-        :vec_given_feat_val =>
+        :vector_given_value_given_feature =>
             fitresult,
     )
     Xnew_transf = contrast_encoder_transform(Xnew, generic_cache)
@@ -112,7 +112,7 @@ Only relevant if `mode` is `:contrast` or `:hypothesis`.
 
 The fields of `fitted_params(mach)` are:
 
-- `vec_given_feat_val`: A dictionary that maps each level for each column in a subset of the categorical features of X into its frequency.
+- `vector_given_value_given_feature`: A dictionary that maps each level for each column in a subset of the categorical features of X into its frequency.
 
 # Report
 

test/encoders/contrast_encoder.jl

@@ -53,8 +53,8 @@ end
     contrast_matrix = get_dummy_contrast(k)
     print()
     for (i, level) in enumerate(levels(X.name))
-        println(cache[:vec_given_feat_val])
-        @test cache[:vec_given_feat_val][:name][level] == contrast_matrix[i, :]
+        println(cache[:vector_given_value_given_feature])
+        @test cache[:vector_given_value_given_feature][:name][level] == contrast_matrix[i, :]
     end
 end
 
@@ -82,7 +82,7 @@ end
     k = length(levels(X.favnum))
     contrast_matrix = get_sum_contrast(k)
     for (i, level) in enumerate(levels(X.favnum))
-        @test cache[:vec_given_feat_val][:favnum][level] == contrast_matrix[i, :]
+        @test cache[:vector_given_value_given_feature][:favnum][level] == contrast_matrix[i, :]
     end
 end
 
@@ -101,7 +101,7 @@ end
     k = length(levels(X.favnum))
     contrast_matrix = get_backward_diff_contrast(k)
     for (i, level) in enumerate(levels(X.favnum))
-        @test cache[:vec_given_feat_val][:favnum][level] == contrast_matrix[i, :]
+        @test cache[:vector_given_value_given_feature][:favnum][level] == contrast_matrix[i, :]
     end
 end
 
@@ -118,7 +118,7 @@ end
     k = length(levels(X.favnum))
     contrast_matrix = get_forward_diff_contrast(k)
     for (i, level) in enumerate(levels(X.favnum))
-        @test cache[:vec_given_feat_val][:favnum][level] == contrast_matrix[i, :]
+        @test cache[:vector_given_value_given_feature][:favnum][level] == contrast_matrix[i, :]
     end
 end
 
@@ -140,7 +140,7 @@ end
      k = length(levels(X.name))
      contrast_matrix = get_helmert_contrast(k)
      for (i, level) in enumerate(levels(X.name))
-         @test cache[:vec_given_feat_val][:name][level] == contrast_matrix[i, :]
+         @test cache[:vector_given_value_given_feature][:name][level] == contrast_matrix[i, :]
      end
 end
 
@@ -284,8 +284,8 @@ end
     @test X_transf == Xnew_transf
 
     # fitted parameters is correct
-    vec_given_feat_val = fitted_params(mach).vec_given_feat_val
-    @test vec_given_feat_val == generic_cache[:vec_given_feat_val]
+    vector_given_value_given_feature = fitted_params(mach).vector_given_value_given_feature
+    @test vector_given_value_given_feature == generic_cache[:vector_given_value_given_feature]
 
     # Test report
     @test report(mach) == (encoded_features = generic_cache[:encoded_features],)