fix sprinkling

aclai-lab · Dec 6, 2023 · 133dfaf · 133dfaf
1 parent c7b10bf
commit 133dfaf
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Showing 3 changed files with 4 additions and 4 deletions.
diff --git a/Project.toml b/Project.toml
@@ -3,7 +3,7 @@ uuid = "e54bda2e-c571-11ec-9d64-0242ac120002"
 license = "MIT"
 desc = "Julia implementation of Modal Decision Trees and Random Forest algorithms"
 authors = ["Giovanni PAGLIARINI"]
-version = "0.3.3"
+version = "0.3.4"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

diff --git a/src/interfaces/MLJ.jl b/src/interfaces/MLJ.jl
@@ -122,7 +122,7 @@ function MMI.fit(m::SymbolicModel, verbosity::Integer, X, y, var_grouping, class
             (Xnew, ynew, var_grouping, classes_seen, w) = MMI.reformat(m, Xnew, ynew; passive_mode = true)
             preds, sprinkledmodel = ModalDecisionTrees.sprinkle(model, Xnew, ynew)
             if simplify
-                sprinkledmodel = MDT.prune(model; simplify = true)
+                sprinkledmodel = MDT.prune(sprinkledmodel; simplify = true)
             end
             preds, translate_function(sprinkledmodel)
         end,

diff --git a/test/other-test-stuff.jl b/test/other-test-stuff.jl
@@ -37,9 +37,9 @@ dataset_name = "NATOPS"
 # dataset_name = "RacketSports"
 # dataset_name = "Libras"
 
-X_train, y = SoleModels.load_arff_dataset(dataset_name)
+X, y = SoleModels.load_arff_dataset(dataset_name)
 
-fitresult = MMI.fit(model, 0, X_train, Y_train);
+fitresult = MMI.fit(model, 0, X, Y);
 
 Y_test_preds, test_tree = MMI.predict(model, fitresult[1], X_test, Y_test);