Fix all tests

src/extract.jl

@@ -26,16 +26,20 @@ the effect on the outcome depends on the data.
 """
 function feature_importance(
         model::StableRules,
-        feature_name::AbstractString
+        feature_name::String
     )
     importance = 0.0
     for (i, rule) in enumerate(model.rules)
-        for clause::Split in rule.path.splits
-            if _feature_name(clause) == feature_name
+        for subclause::SubClause in _subclauses(rule)
+            if _feature_name(subclause)::String == feature_name
                 weight = model.weights[i]
                 importance += _rule_importance(weight, rule)
             end
         end
     end
     return importance
 end
+
+function feature_importance(model::StableRules, feature_name::AbstractString)
+    return feature_importance(model, string(feature_name)::String)
+end

src/ruleshow.jl

@@ -7,7 +7,7 @@ function _pretty_feature_name(subclause::SubClause)
     if feature == feature_name
         return feature
     else
-        return string(':', name)::String
+        return string(':', feature_name)::String
     end
 end
 

test/rcall.jl

@@ -2,6 +2,8 @@
 # This file defines the MLJ wrappers around R sirus and tests them.
 # Actual comparisons against other models are done in test/mlj.jl.
 #
+# Assumes sirus is installed in R `install.packages("sirus")`.
+#
 
 import MLJModelInterface:
     MLJModelInterface,

test/weights.jl

@@ -2,8 +2,8 @@ data = [0.0 2.5
         5.0 5.0
         0.0 0.0]
 
-r1 = S.Rule(S.TreePath(" X[i, 1] < 1 "), [0.1], [0.0])
-r2 = S.Rule(S.TreePath(" X[i, 2] < 2 "), [0.2], [0.0])
+r1 = S.Rule(S.Clause(" X[i, 1] < 1 "), [0.1], [0.0])
+r2 = S.Rule(S.Clause(" X[i, 2] < 2 "), [0.2], [0.0])
 
 binary_feature_data = Float16[1 0;
                               0 0;