@@ -251,7 +251,6 @@ class Learner1D(BaseLearner):

             self._oldscale = deepcopy(self._scale)

-

     def ask(self, n, 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

@@ -273,11 +272,11 @@ class Learner1D(BaseLearner):

         if len(points) == 2:

             # First time

             loss_improvements = [np.inf] * n

-            points = np.linspace(*self.bounds, n)

+            points = np.linspace(*self.bounds, n).tolist()

         elif len(points) == 1:

             # Second time, if we previously returned just self.bounds[0]

             loss_improvements = [np.inf] * n

-            points = np.linspace(*self.bounds, n + 1)[1:]

+            points = np.linspace(*self.bounds, n + 1)[1:].tolist()

         else:

             def xs(x, n):

                 if n == 1:

@@ -336,7 +335,6 @@ class Learner1D(BaseLearner):

         return p.redim(x=dict(range=plot_bounds))

-

     def remove_unfinished(self):

         self.pending_points = set()

         self.losses_combined = deepcopy(self.losses)