ensembletrees listrules

Author: PasoStudio73

Project.toml

@@ -51,10 +51,10 @@ Graphs = "1.8"
 HTTP = "1.9"
 IterTools = "1"
 Lazy = "0.15.1"
-MLJ = "0.20"
-MLJBase = "1.6"
+MLJ = "0.19 - 0.20"
+MLJBase = "1.6 - 1.7"
 MLJDecisionTreeInterface = "0.4"
-MLJModelInterface = "1.8.0"
+MLJModelInterface = "1.8"
 PrettyTables = "2.2"
 ProgressMeter = "1"
 Random = "1"

src/rule-extraction.jl

@@ -373,20 +373,22 @@ _listrules(m::DecisionTree; kwargs...) = _listrules(root(m); kwargs...)
 
 function _listrules(
     m::DecisionEnsemble;
-    suppress_parity_warning = false,
+    # weights::Union{Nothing, AbstractVector} = nothing,
+    suppress_parity_warning = true,
     kwargs...
 )
-    error("TODO check method & implement more efficient strategies for specific cases.")
-    modelrules = [listrules(subm; kwargs...) for subm in models(m)]
+    # error("TODO check method & implement more efficient strategies for specific cases.")
+    modelrules = [_listrules(subm; kwargs...) for subm in models(m)]
     @assert all(r->consequent(r) isa ConstantModel, Iterators.flatten(modelrules))
 
     IterTools.imap(rulecombination->begin
         rulecombination = collect(rulecombination)
         ant = join_antecedents(antecedent.(rulecombination))
-        cons = bestguess(outcome.(consequent.(rulecombination)); suppress_parity_warning)
-        infos = info.(rulecombination)
-        # TODO @show infos; info = (;)
-        Rule(ant, cons)
+        o_cons = bestguess(outcome.(consequent.(rulecombination)), m.weights; suppress_parity_warning)
+        i_cons = merge(info.(consequent.(rulecombination))...)
+        cons = ConstantModel(o_cons, i_cons)
+        infos = merge(info.(rulecombination)...)
+        Rule(ant, cons, infos)
         end, Iterators.product(modelrules...)
     )
 end