@@ -72,6 +72,7 @@ class BaseLearner(metaclass=abc.ABCMeta):

             (possibly by interpolation).

         """

+    @abc.abstractmethod

     def choose_points(self, n, add_data=True):

         """Choose the next 'n' points to evaluate.

@@ -85,22 +86,7 @@ class BaseLearner(metaclass=abc.ABCMeta):

             values. Set this to False if you do not

             want to modify the state of the learner.

         """

-        points, loss_improvements = self._choose_points(n)

-        if add_data:

-            self.add_data(points, itertools.repeat(None))

-        return points, loss_improvements

-

-    @abc.abstractmethod

-    def _choose_points(self, n):

-        """Choose the next 'n' points to evaluate.

-

-        Should be overridden by subclasses.

-

-        Parameters

-        ----------

-        n : int

-            The number of points to choose.

-        """

+        pass

     def __getstate__(self):

         return copy(self.__dict__)

@@ -139,9 +125,11 @@ class AverageLearner(BaseLearner):

         self.sum_f = 0

         self.sum_f_sq = 0

-    def _choose_points(self, n=10):

+    def choose_points(self, n=10, add_data=True):

         points = list(range(self.n_requested, self.n_requested + n))

         loss_improvements = [None] * n

+        if add_data:

+            self.add_data(points, itertools.repeat(None))

         return points, loss_improvements

     def add_point(self, n, value):

@@ -324,7 +312,7 @@ class Learner1D(BaseLearner):

                     self._oldscale = self._scale

-    def _choose_points(self, n=10):

+    def choose_points(self, n=10, add_data=True):

         """Return n points that are expected to maximally reduce the loss."""

         # Find out how to divide the n points over the intervals

         # by finding  positive integer n_i that minimize max(L_i / n_i) subject

@@ -373,7 +361,10 @@ class Learner1D(BaseLearner):

                                  itertools.repeat(-quality, n)

                                  for quality, x, n in quals))

-        return (xs, loss_improvements)

+        if add_data:

+            self.add_data(points, itertools.repeat(None))

+

+        return xs, loss_improvements

     def interpolate(self, extra_points=None):

         xs = list(self.data.keys())

@@ -462,9 +453,6 @@ class BalancingLearner(BaseLearner):

         else:

             return self._choose_and_add_points(n)

-    def _choose_points(self, n):

-        pass

-

     def add_point(self, x, y):

         index, x = x

         self.learners[index].add_point(x, y)

@@ -793,9 +781,6 @@ class Learner2D(BaseLearner):

             else:

                 dev[jsimplex] = 0

-    def _choose_points(self, n):

-        pass

-

     def _choose_and_add_points(self, n):

         if n <= len(self._stack):

             points = self._stack[:n]