@@ -113,40 +113,34 @@ class BalancingLearner(BaseLearner):

                 ' strategy="npoints" is implemented.')

     def _ask_and_tell_based_on_loss_improvements(self, n):

-        chosen_points = []

-        chosen_loss_improvements = []

-        npoints = [l.npoints + len(l.pending_points)

-                   for l in self.learners]

+        selected = []  # tuples ((learner_index, point), loss_improvement)

+        npoints = [l.npoints + len(l.pending_points) for l in self.learners]

         for _ in range(n):

-            improvements_per_learner = []

-            points_per_learner = []

+            to_select = []

             for index, learner in enumerate(self.learners):

                 # Take the points from the cache

                 if index not in self._ask_cache:

                     self._ask_cache[index] = learner.ask(

                         n=1, tell_pending=False)

                 points, loss_improvements = self._ask_cache[index]

-

-                priority = (loss_improvements[0], -npoints[index])

-                improvements_per_learner.append(priority)

-                points_per_learner.append((index, points[0]))

+                to_select.append(

+                    ((index, points[0]),

+                     (loss_improvements[0], -npoints[index]))

+                )

             # Choose the optimal improvement.

             (index, point), (loss_improvement, _) = max(

-                zip(points_per_learner, improvements_per_learner),

-                key=itemgetter(1))

+                to_select, key=itemgetter(1))

             npoints[index] += 1

-            chosen_points.append((index, point))

-            chosen_loss_improvements.append(loss_improvement)

+            selected.append(((index, point), loss_improvement))

             self.tell_pending((index, point))

-        return chosen_points, chosen_loss_improvements

+        points, loss_improvements = map(list, zip(*selected))

+        return points, loss_improvements

     def _ask_and_tell_based_on_loss(self, n):

-        chosen_points = []

-        chosen_loss_improvements = []

-        npoints = [l.npoints + len(l.pending_points)

-                   for l in self.learners]

+        selected = []   # tuples ((learner_index, point), loss_improvement)

+        npoints = [l.npoints + len(l.pending_points) for l in self.learners]

         for _ in range(n):

             losses = self._losses(real=False)

             priority = zip(losses, (-n for n in npoints))

@@ -158,15 +152,14 @@ class BalancingLearner(BaseLearner):

                 self._ask_cache[index] = self.learners[index].ask(n=1)

             points, loss_improvements = self._ask_cache[index]

-            chosen_points.append((index, points[0]))

-            chosen_loss_improvements.append(loss_improvements[0])

-        return chosen_points, chosen_loss_improvements

+            selected.append(((index, points[0]), loss_improvements[0]))

+

+        points, loss_improvements = map(list, zip(*selected))

+        return points, loss_improvements

     def _ask_and_tell_based_on_npoints(self, n):

-        chosen_points = []

-        chosen_loss_improvements = []

-        npoints = [l.npoints + len(l.pending_points)

-                   for l in self.learners]

+        selected = []  # tuples ((learner_index, point), loss_improvement)

+        npoints = [l.npoints + len(l.pending_points) for l in self.learners]

         n_left = n

         while n_left > 0:

             index = np.argmin(npoints)

@@ -176,9 +169,10 @@ class BalancingLearner(BaseLearner):

             points, loss_improvements = self._ask_cache[index]

             npoints[index] += 1

             n_left -= 1

-            chosen_points.append((index, points[0]))

-            chosen_loss_improvements.append(loss_improvements[0])

-        return chosen_points, chosen_loss_improvements

+            selected.append(((index, points[0]), loss_improvements[0]))

+

+        points, loss_improvements = map(list, zip(*selected))

+        return points, loss_improvements

     def ask(self, n, tell_pending=True):

         """Chose points for learners."""