remove _choose_points, closes #19

adaptive/learner.py

@@ -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]