Code cleanup

Orange/evaluation/scoring.py

@@ -200,7 +200,8 @@ class AUC(ClassificationScore):
     is_binary = True
     long_name = "Area under ROC curve"
 
-    def calculate_weights(self, results):
+    @staticmethod
+    def calculate_weights(results):
         classes = np.unique(results.actual)
         class_cases = [sum(results.actual == class_)
                        for class_ in classes]
@@ -212,14 +213,14 @@ def calculate_weights(self, results):
         else:
             return weights / wsum
 
-    def single_class_auc(self, results, target):
+    @staticmethod
+    def single_class_auc(results, target):
         y = np.array(results.actual == target, dtype=int)
         return np.fromiter(
             (skl_metrics.roc_auc_score(y, probabilities[:, int(target)])
              for probabilities in results.probabilities),
             dtype=np.float64, count=len(results.predicted))
 
-
     def multi_class_auc(self, results):
         classes = np.unique(results.actual)
         weights = self.calculate_weights(results)
@@ -285,14 +286,15 @@ def compute_score(self, results, eps=1e-15, normalize=True,
 class Specificity(ClassificationScore):
     is_binary = True
 
-    def calculate_weights(self, results):
+    @staticmethod
+    def calculate_weights(results):
         classes, counts = np.unique(results.actual, return_counts=True)
         n = np.array(results.actual).shape[0]
         return counts / n, classes
 
     @staticmethod
     def specificity(y_true, y_pred):
-        tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
+        tn, fp, _, _ = confusion_matrix(y_true, y_pred).ravel()
         return tn / (tn + fp)
 
     def single_class_specificity(self, results, target):
@@ -306,7 +308,7 @@ def single_class_specificity(self, results, target):
     def multi_class_specificity(self, results):
         weights, classes = self.calculate_weights(results)
         scores = np.array([self.single_class_specificity(results, class_)
-                              for class_ in classes])
+                           for class_ in classes])
         return np.sum(scores.T * weights, axis=1)
 
     def compute_score(self, results, target=None, average="binary"):