test all the different loss functions in each test

Improve the tests

• test all the different loss functions in each test
• add Learner1D._recompute_losses_factor to remove xfail from two tests
• speed up the tests

adaptive/learner/learner1D.py

@@ -228,6 +228,11 @@ class Learner1D(BaseLearner):
         self.losses = {}
         self.losses_combined = {}
 
+        # When the scale changes by a factor 2, the losses are
+        # recomputed. This is tunable such that we can test
+        # the learners behavior in the tests.
+        self._recompute_losses_factor = 2
+
         self.data = {}
         self.pending_points = set()
 
@@ -447,7 +452,7 @@ class Learner1D(BaseLearner):
         self._update_losses(x, real=True)
 
         # If the scale has increased enough, recompute all losses.
-        if self._scale[1] > 2 * self._oldscale[1]:
+        if self._scale[1] > self._recompute_losses_factor * self._oldscale[1]:
 
             for interval in self.losses:
                 self._update_interpolated_loss_in_interval(*interval)
@@ -563,10 +568,10 @@ class Learner1D(BaseLearner):
         def finite_loss(loss, xs):
             # If the loss is infinite we return the
             # distance between the two points.
-            loss = loss if not math.isinf(loss)
-                else (xs[1] - xs[0]) / self._scale[0]
+            if math.isinf(loss):
+                loss = (xs[1] - xs[0]) / self._scale[0]
 
-            # We round the loss to 12 digets such that losses
+            # We round the loss to 12 digits such that losses
             # are equal up to numerical precision will be considered
             # equal.
             return round(loss, ndigits=12)