adapting MLJInterface to nesequentialcovering parameters

aclai-lab · Jun 10, 2024 · c765481 · c765481
1 parent f84be6a
commit c765481
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Showing 2 changed files with 8 additions and 6 deletions.
diff --git a/src/interfaces/MLJ.jl b/src/interfaces/MLJ.jl
@@ -6,7 +6,7 @@ export OrderedCN2Learner
 
 # using ModalDecisionTrees.MLJInterface: wrapdataset
 using ModalDecisionLists
-using ModalDecisionLists.LossFunctions: laplace_accuracy, entropy
+using ModalDecisionLists: LossFunctions
 
 # import ModalDecisionLists: SearchMethod, BeamSearch, RandSearch
 # import ModalDecisionLists: sequentialcovering
@@ -100,7 +100,7 @@ function ExtendedSequentialCovering(;
     searchmethod::SearchMethod=BeamSearch(),
     max_rulebase_length::Union{Nothing,Integer}=nothing,
     # shared parameters
-    loss_function::Function=entropy,
+    loss_function::Function=LossFunctions.entropy,
     discretizedomain::Bool=false,
     max_infogain_ratio::Real=1.0,
     significance_alpha::Union{Real,Nothing}=0.0,

diff --git a/src/searchmethods/randsearch.jl b/src/searchmethods/randsearch.jl
@@ -127,7 +127,6 @@ function extract_optimalantecedent(
 
     bestant_satmask = ones(Bool, length(y))
     bestformula = begin
-
         if !isempty(formulas)
             losses = map(((rfa, satmask),) -> begin
 
@@ -140,15 +139,18 @@ function extract_optimalantecedent(
 
             end, formulas)
             bestindex = argmin(losses)
-
             (bestant_formula, bestant_satmask) = formulas[bestindex]
-        end
-        if all(bestant_satmask) | (losses[bestindex] > loss_function(y, w; kwargs...))
+            if all(bestant_satmask) | (losses[bestindex] > loss_function(y, w; kwargs...))
+                bestant_formula = TOP
+                bestant_satmask = ones(length(y))
+            end
+        else
             bestant_formula = TOP
             bestant_satmask = ones(length(y))
         end
         (bestant_formula, bestant_satmask)
     end # bestantformula
+
     return bestformula
 end