Fixes

Author: matthewsedam

adlib/learners/outlier_removal_learner.py

@@ -40,7 +40,7 @@ def train(self):
 
         base_cutoff = cutoff
         factor = 1
-        max_cutoff = cutoff * 100
+        max_cutoff = cutoff * 200
 
         if self.verbose:
             print('\nBase cutoff:', cutoff, '\nMax cutoff:', max_cutoff, '\n')

adlib/learners/simple_learner.py

@@ -22,7 +22,7 @@ def set_model(self, model):
 
     def set_params(self, params: Dict):
         if 'model' in params:
-            self.model = self.set_model(params['model'])
+            self.set_model(params['model'])
         self.model.set_params(params)
 
     def train(self):
@@ -60,10 +60,6 @@ def predict_log_proba(self, testing_instances):
     def decision_function(self, X):
         return self.model.learner.decision_function(X)
 
-    def set_params(self, params: Dict):
-        if params['model'] is not None:
-            self.model = self.set_model(params['model'])
-
     def get_weight(self):
         if self.model.learner.kernel == 'rbf':
             return None
@@ -75,6 +71,3 @@ def get_weight(self):
 
     def get_constant(self):
         return self.model.learner.intercept_
-
-    def decision_function(self, X):
-        return self.model.learner.decision_function(X)

adlib/tests/learners/dp_learner_test.py

@@ -121,7 +121,7 @@ def test(self):
                       list(self.dp_learner_testing_pred_labels),
                       end - begin)
 
-            self.results.append(result)
+            self.results.append(deepcopy(result))
 
         if self.verbose:
             print('\nEND', self.learner_names[0] if len(self.learner_names) == 1 else 'learner',
@@ -201,7 +201,7 @@ def attack(self, instances):
     def _retrain(self):
         # Retrain the model with poisoned data
         learning_model = svm.SVC(probability=True, kernel='linear')
-        self.attack_learner = SimpleLearner(learning_model, self.attack_instances)
+        self.attack_learner = SimpleLearner(learning_model, deepcopy(self.attack_instances))
         self.attack_learner.train()
 
         self.attack_training_pred_labels = self.attack_learner.predict(self.training_instances)