@@ -12,7 +12,7 @@ import scipy.spatial

 import pytest

 from ..learner import *

-from ..runner import replay_log, BlockingRunner, SequentialExecutor

+from ..runner import simple, replay_log

 def generate_random_parametrization(f):

@@ -407,29 +407,53 @@ def test_termination_on_discontinuities():

 def test_loss_at_machine_precision_interval_is_zero():

     """The loss of an interval smaller than _dx_eps

-    should be set to zero. If this is not the case, this test

-    will go on forever."""

+    should be set to zero."""

     def f(x):

         return 1 if x == 0 else 0

+    def goal(l):

+        return l.loss() < 0.01 or l.npoints >= 1000

+

     learner = Learner1D(f, bounds=(-1, 1))

-    ex = SequentialExecutor()

-    runner = BlockingRunner(learner, executor=ex, 

-        goal=lambda l: l.loss() < 0.001)

+    simple(learner, goal=goal)

+

+    # this means loss < 0.01 was reached

+    assert learner.npoints != 1000

 def small_deviations(x):

     import random

-    return 0 if x < 1 else 1 + 10**(-random.randint(12, 14))

+    return 0 if x <= 1 else 1 + 10**(-random.randint(12, 14))

 def test_small_deviations():

     """This tests whether the Learner1D can handle small deviations.

     See https://gitlab.kwant-project.org/qt/adaptive/merge_requests/73 and

     https://gitlab.kwant-project.org/qt/adaptive/issues/61."""

-    learner = Learner1D(small_deviations, bounds=(0, 2))

-    runner = BlockingRunner(learner,

-        goal=lambda l: l.npoints > 5000)

+

+    eps = 5e-14

+    learner = Learner1D(small_deviations, bounds=(1 - eps, 1 + eps))

+

+    # Some non-determinism is needed to make this test fail so we keep

+    # a list of points that will be evaluated later to emulate

+    # parallel execution

+    stash = []

+

+    for i in range(100):

+        xs, _ = learner.ask(10)

+

+        # Save 5 random points out of `xs` for later

+        random.shuffle(xs)

+        for _ in range(5):

+            stash.append(xs.pop())

+

+        for x in xs:

+            learner.tell(x, learner.function(x))

+

+        # Evaluate and add 5 random points from `stash`

+        random.shuffle(stash)

+        for _ in range(5):

+            learner.tell(stash.pop(), learner.function(x))

 @pytest.mark.xfail