Bugfix: Correct call of method SklearnCounterfactual.__build_result_dict

ceml/sklearn/lda.py

@@ -153,7 +153,10 @@ def solve(self, x_orig, y_target, regularization, features_whitelist, return_as_
         if self.model.predict([xcf]) != y_target:
             raise Exception("No counterfactual found - Consider changing parameters 'regularization', 'features_whitelist', 'optimizer' and try again")
 
-        return self.__build_result_dict(xcf, y_target, delta) if return_as_dict else xcf, y_target, delta
+        if return_as_dict is True:
+            return self._SklearnCounterfactual__build_result_dict(xcf, y_target, delta)
+        else:
+            return xcf, y_target, delta
 
 
 def lda_generate_counterfactual(model, x, y_target, features_whitelist=None, regularization="l1", C=1.0, optimizer="mp", return_as_dict=True, done=None):

ceml/sklearn/linearregression.py

@@ -127,7 +127,10 @@ def solve(self, x_orig, y_target, regularization, features_whitelist, return_as_
         delta = x_orig - xcf
         y_cf = self.model.predict([xcf])
 
-        return self.__build_result_dict(xcf, y_target, delta) if return_as_dict else xcf, y_cf, delta
+        if return_as_dict is True:
+            return self._SklearnCounterfactual__build_result_dict(xcf, y_target, delta)
+        else:
+            return xcf, y_target, delta
 
 
 def linearregression_generate_counterfactual(model, x, y_target, features_whitelist=None, regularization="l1", C=1.0, optimizer="mp", return_as_dict=True, done=None):

ceml/sklearn/lvq.py

@@ -334,7 +334,10 @@ def solve(self, x_orig, y_target, regularization, features_whitelist, return_as_
         if self.model.predict([xcf]) != y_target:
             raise Exception("No counterfactual found - Consider changing parameters 'regularization', 'features_whitelist', 'optimizer' and try again")
 
-        return self.__build_result_dict(xcf, y_target, delta) if return_as_dict else xcf, y_target, delta
+        if return_as_dict is True:
+            return self._SklearnCounterfactual__build_result_dict(xcf, y_target, delta)
+        else:
+            return xcf, y_target, delta
 
 
 def lvq_generate_counterfactual(model, x, y_target, features_whitelist=None, dist="l2", regularization="l1", C=1.0, optimizer="auto", return_as_dict=True, done=None):

ceml/sklearn/naivebayes.py

@@ -167,7 +167,10 @@ def solve(self, x_orig, y_target, regularization, features_whitelist, return_as_
         if self.model.predict([xcf]) != y_target:
             raise Exception("No counterfactual found - Consider changing parameters 'regularization', 'features_whitelist', 'optimizer' and try again")
 
-        return self.__build_result_dict(xcf, y_target, delta) if return_as_dict else xcf, y_target, delta
+        if return_as_dict is True:
+            return self._SklearnCounterfactual__build_result_dict(xcf, y_target, delta)
+        else:
+            return xcf, y_target, delta
 
 
 def gaussiannb_generate_counterfactual(model, x, y_target, features_whitelist=None, regularization="l1", C=1.0, optimizer="auto", return_as_dict=True, done=None):

ceml/sklearn/qda.py

@@ -170,7 +170,10 @@ def solve(self, x_orig, y_target, regularization, features_whitelist, return_as_
         if self.model.predict([xcf]) != y_target:
             raise Exception("No counterfactual found - Consider changing parameters 'regularization', 'features_whitelist', 'optimizer' and try again")
 
-        return self.__build_result_dict(xcf, y_target, delta) if return_as_dict else xcf, y_target, delta
+        if return_as_dict is True:
+            return self._SklearnCounterfactual__build_result_dict(xcf, y_target, delta)
+        else:
+            return xcf, y_target, delta
 
 
 def qda_generate_counterfactual(model, x, y_target, features_whitelist=None, regularization="l1", C=1.0, optimizer="auto", return_as_dict=True, done=None):

ceml/sklearn/softmaxregression.py

@@ -157,7 +157,10 @@ def solve(self, x_orig, y_target, regularization, features_whitelist, return_as_
         if self.model.predict([xcf]) != y_target:
             raise Exception("No counterfactual found - Consider changing parameters 'regularization', 'features_whitelist', 'optimizer' and try again")
 
-        return self.__build_result_dict(xcf, y_target, delta) if return_as_dict else xcf, y_target, delta
+        if return_as_dict is True:
+            return self._SklearnCounterfactual__build_result_dict(xcf, y_target, delta)
+        else:
+            return xcf, y_target, delta
 
 
 def softmaxregression_generate_counterfactual(model, x, y_target, features_whitelist=None, regularization="l1", C=1.0, optimizer="mp", return_as_dict=True, done=None):