rename npoints to total_points

Author: basnijholt

adaptive/learner/balancing_learner.py

@@ -114,7 +114,7 @@ def strategy(self, strategy):
 
     def _ask_and_tell_based_on_loss_improvements(self, n):
         selected = []  # tuples ((learner_index, point), loss_improvement)
-        npoints = [l.npoints + len(l.pending_points) for l in self.learners]
+        total_points = [l.npoints + len(l.pending_points) for l in self.learners]
         for _ in range(n):
             to_select = []
             for index, learner in enumerate(self.learners):
@@ -125,13 +125,13 @@ def _ask_and_tell_based_on_loss_improvements(self, n):
                 points, loss_improvements = self._ask_cache[index]
                 to_select.append(
                     ((index, points[0]),
-                     (loss_improvements[0], -npoints[index]))
+                     (loss_improvements[0], -total_points[index]))
                 )
 
             # Choose the optimal improvement.
             (index, point), (loss_improvement, _) = max(
                 to_select, key=itemgetter(1))
-            npoints[index] += 1
+            total_points[index] += 1
             selected.append(((index, point), loss_improvement))
             self.tell_pending((index, point))
 
@@ -140,12 +140,12 @@ def _ask_and_tell_based_on_loss_improvements(self, n):
 
     def _ask_and_tell_based_on_loss(self, n):
         selected = []   # tuples ((learner_index, point), loss_improvement)
-        npoints = [l.npoints + len(l.pending_points) for l in self.learners]
+        total_points = [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))
+            priority = zip(losses, (-n for n in total_points))
             index = max(enumerate(priority), key=itemgetter(1))[0]
-            npoints[index] += 1
+            total_points[index] += 1
 
             # Take the points from the cache
             if index not in self._ask_cache:
@@ -159,15 +159,15 @@ def _ask_and_tell_based_on_loss(self, n):
 
     def _ask_and_tell_based_on_npoints(self, n):
         selected = []  # tuples ((learner_index, point), loss_improvement)
-        npoints = [l.npoints + len(l.pending_points) for l in self.learners]
+        total_points = [l.npoints + len(l.pending_points) for l in self.learners]
         n_left = n
         while n_left > 0:
-            index = np.argmin(npoints)
+            index = np.argmin(total_points)
             # Take the points from the cache
             if index not in self._ask_cache:
                 self._ask_cache[index] = self.learners[index].ask(n=1)
             points, loss_improvements = self._ask_cache[index]
-            npoints[index] += 1
+            total_points[index] += 1
             n_left -= 1
             selected.append(((index, points[0]), loss_improvements[0]))