src/rules.py

@@ -71,7 +71,7 @@ class Rule:
 
 
 # https://mljar.com/blog/extract-rules-decision-tree/
-def get_rules(tree, feature_names, classes=None) -> List[Rule]:
+def get_rules(tree, feature_names) -> List[Rule]:
     tree_ = tree.tree_
     feature_name = [
         feature_names[i] if i != TREE_UNDEFINED else "undefined!" for i in tree_.feature
@@ -91,11 +91,7 @@ def get_rules(tree, feature_names, classes=None) -> List[Rule]:
             p2.append(RuleAtom(name, ComparisonType.GREATER, threshold))
             recurse(tree_.children_right[node], p2, rules)
         else:
-            if classes is None:
+            rules.append(Rule(antecedent, tree_.value[node][0][0]))
-                rules.append(Rule(antecedent, tree_.value[node][0][0]))
-            else:
-                value = np.argmax(tree_.value[node][0])
-                rules.append(Rule(antecedent, classes[value]))
 
     recurse(0, antecedent, rules)
 
@@ -306,8 +302,6 @@ def _get_fuzzy_rules(
     for rule in rules:
         antecedent = []
         for atom in rule.get_antecedent():
-            if fuzzy_variables.get(atom.get_varaible(), None) is None:
-                continue
             antecedent.append(
                 _get_fuzzy_rule_atom(
                     fuzzy_variables[atom.get_varaible()], atom.get_value()