From ace25a1e94fbe60d9e9680493e4dd7f250215dc1 Mon Sep 17 00:00:00 2001
From: Elias Carvalho <eliascarvdev@gmail.com>
Date: Fri, 15 Dec 2023 15:43:46 -0300
Subject: [PATCH] Rename internal functions

---
 src/cverrors/wcv.jl | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)

diff --git a/src/cverrors/wcv.jl b/src/cverrors/wcv.jl
index 1cc7e32..62d7310 100644
--- a/src/cverrors/wcv.jl
+++ b/src/cverrors/wcv.jl
@@ -34,8 +34,7 @@ WeightedValidation(weighting::W, folding::F; lambda::T=one(T), loss=Dict()) wher
   WeightedValidation{W,F,T}(weighting, folding, lambda, loss)
 
 function cverror(setup, geotable, method::WeightedValidation)
-  # retrieve problem info
-  ovars = _outputvars(setup, geotable)
+  ovars = _outputs(setup, geotable)
   loss = method.loss
   for var in ovars
     if var ∉ keys(loss)
@@ -52,8 +51,8 @@ function cverror(setup, geotable, method::WeightedValidation)
 
   # error for a fold
   function ε(f)
-    # solve sub-problem
-    solution = _solution(setup, geotable, f)
+    # fold prediction
+    pred = _prediction(setup, geotable, f)
 
     # holdout set
     holdout = view(geotable, f[2])
@@ -64,7 +63,7 @@ function cverror(setup, geotable, method::WeightedValidation)
     # loss for each variable
     losses = map(ovars) do var
       ℒ = loss[var]
-      ŷ = getproperty(solution, var)
+      ŷ = getproperty(pred, var)
       y = getproperty(holdout, var)
       var => mean(ℒ, ŷ, y, 𝓌, normalize=false)
     end
@@ -79,18 +78,18 @@ function cverror(setup, geotable, method::WeightedValidation)
   Dict(var => mean(get.(εs, var, 0)) for var in ovars)
 end
 
-# output variables of the problem
-_outputvars(::InterpSetup, gtb) = setdiff(propertynames(gtb), [:geometry])
-_outputvars(s::LearnSetup, gtb) = s.output
+# output variables
+_outputs(::InterpSetup, gtb) = setdiff(propertynames(gtb), [:geometry])
+_outputs(s::LearnSetup, gtb) = s.output
 
-# solution for a given fold
-function _solution(s::InterpSetup{I}, geotable, f) where {I}
+# prediction for a given fold
+function _prediction(s::InterpSetup{I}, geotable, f) where {I}
   sdat = view(geotable, f[1])
   sdom = view(domain(geotable), f[2])
   sdat |> I(sdom, s.model)
 end
 
-function _solution(s::LearnSetup, geotable, f)
+function _prediction(s::LearnSetup, geotable, f)
   source = view(geotable, f[1])
   target = view(geotable, f[2])
   target |> Learn(source, s.model, s.input => s.output)