Rectification for RF

pyxai/sources/core/explainer/explainerRF.py

@@ -551,17 +551,14 @@ def rectify(self, *, conditions, label):
         Tools.verbose("")
         Tools.verbose("-------------- Rectification information:")
         tree_decision_rule = self._random_forest.forest[0].decision_rule_to_tree(conditions)
-
+        if label == 1:
+            tree_decision_rule = tree_decision_rule.negating_tree()
         Tools.verbose("Classification Rule - Number of nodes:", tree_decision_rule.n_nodes())
         Tools.verbose("Model - Number of nodes:", self._random_forest.n_nodes())
 
-        for i, tree in enumerate(self._random_forest.forest):
-            Tools.verbose("Model - Number of nodes "+str(i)+":"+ str(tree.n_nodes()))
         for i, tree in enumerate(self._random_forest.forest):
             if label == 1:
                 # When label is 1, we have to inverse the decision rule and disjoint the two trees.  
-                # Bug ici 
-                tree_decision_rule = tree_decision_rule.negating_tree()
                 self._random_forest.forest[i] = tree.disjoint_tree(tree_decision_rule)
             elif label == 0:
                 # When label is 0, we have to concatenate the two trees.  
@@ -571,13 +568,13 @@ def rectify(self, *, conditions, label):
 
         Tools.verbose("Model - Number of nodes (after rectification):", self._random_forest.n_nodes())    
 
-        #for i, tree in enumerate(self._random_forest.forest):    
-        #    self._random_forest.forest[i] = self.simplify_theory(tree)
-        #Tools.verbose("Model - Number of nodes (after simplification using the theory):", self._random_forest.n_nodes())
+        for i, tree in enumerate(self._random_forest.forest):    
+            self._random_forest.forest[i] = self.simplify_theory(tree)
+        Tools.verbose("Model - Number of nodes (after simplification using the theory):", self._random_forest.n_nodes())
 
-        #for i, tree in enumerate(self._random_forest.forest):    
-        #    tree.simplify()
-        #Tools.verbose("Model - Number of nodes (after elimination of redundant nodes):", self._random_forest.n_nodes())
+        for i, tree in enumerate(self._random_forest.forest):    
+            tree.simplify()
+        Tools.verbose("Model - Number of nodes (after elimination of redundant nodes):", self._random_forest.n_nodes())
 
         if self._instance is not None:
             self.set_instance(self._instance)

pyxai/sources/core/structure/randomForest.py

@@ -34,7 +34,6 @@ def predict_instance(self, instance):
         n_votes = numpy.zeros(self.n_classes)
         for tree in self.forest:
             n_votes[tree.predict_instance(instance)] += 1
-        print("predict: ",n_votes)
         return numpy.argmax(n_votes)
 
 

pyxai/tests/functionality/Rectify.py

@@ -2,7 +2,7 @@
 import math
 
 
-#Tools.set_verbose(0)
+Tools.set_verbose(0)
 
 import unittest
 class TestRectify(unittest.TestCase):
@@ -12,14 +12,12 @@ def test_rectify_5(self):
 
         dict_information = learner.get_instances(model, n=1, indexes=Learning.TEST, correct=False, details=True)
 
-        #all_dict_information = learner.get_instances(model, indexes=Learning.ALL, details=True)
+        all_dict_information = learner.get_instances(model, indexes=Learning.ALL, details=True)
 
         instance = dict_information["instance"]
         label = dict_information["label"]
         prediction = dict_information["prediction"]
-        print("prediction:", prediction)
-        print("before:", model.predict_instance(instance))
-
+
         compas_types = {
             "numerical": ["Number_of_Priors"],
             "binary": ["Misdemeanor", "score_factor", "Female"],
@@ -31,10 +29,7 @@ def test_rectify_5(self):
         explainer = Explainer.initialize(model, instance=instance, features_type=compas_types)
         reason = explainer.majoritary_reason(n=1)
 
-        print("explanation:", reason)
-        print("explanation:", explainer.to_features(reason))
         model = explainer.rectify(conditions=reason, label=1)
-        print("after:", model.predict_instance(instance))
 
         self.assertEqual(model.predict_instance(instance), 1)
 
@@ -155,8 +150,6 @@ def test_rectify_3(self):
 
         explainer = Explainer.initialize(model, instance=instance, features_type=compas_types)
         minimal_reason = explainer.minimal_sufficient_reason(n=1)
-        print("explanation:", minimal_reason)
-        print("explanation:", explainer.to_features(minimal_reason))
         model = explainer.rectify(conditions=minimal_reason, label=1)
 
         self.assertEqual(model.predict_instance(instance), 1)