Introduce 'tell_pending' which replaces 'tell(x, None)'

and I also tackle

• lower_bound <= learner.ask()[0] <= upper_bound
• if we tell(x, y) with x outside the bounds, the point should be added to data, but not any of the other internal datastructures. This will mean that the point will be "invisible" to the learning algorithm, and only useful for plotting and data extraction

for both the Learner1D and Learner2D in that MR. I included this in here to avoid merge conflicts later.

adaptive/learner/balancing_learner.py

@@ -71,7 +71,7 @@ class BalancingLearner(BaseLearner):
             for index, learner in enumerate(self.learners):
                 if index not in self._points:
                     self._points[index] = learner.ask(
-                        n=1, add_data=False)
+                        n=1, tell_pending=False)
                 point, loss_improvement = self._points[index]
                 improvements_per_learner.append(loss_improvement[0])
                 pairs.append((index, point[0]))
@@ -79,13 +79,13 @@ class BalancingLearner(BaseLearner):
                        key=itemgetter(1))
             points.append(x)
             loss_improvements.append(l)
-            self.tell(x, None)
+            self.tell_pending(x)
 
         return points, loss_improvements
 
-    def ask(self, n, add_data=True):
+    def ask(self, n, tell_pending=True):
         """Chose points for learners."""
-        if not add_data:
+        if not tell_pending:
             with restore(*self.learners):
                 return self._ask_and_tell(n)
         else:
@@ -97,6 +97,12 @@ class BalancingLearner(BaseLearner):
         self._loss.pop(index, None)
         self.learners[index].tell(x, y)
 
+    def tell_pending(self, x):
+        index, x = x
+        self._points.pop(index, None)
+        self._loss.pop(index, None)
+        self.learners[index].tell_pending(x)
+
     def loss(self, real=True):
         losses = []
         for index, learner in enumerate(self.learners):