BalancingLearner: test the "cycle" strategy

adaptive/tests/test_balancing_learner.py

@@ -6,6 +6,9 @@
 from adaptive.runner import simple
 
 
+strategies = ['loss', 'loss_improvements', 'npoints', 'cycle']
+
+
 def test_balancing_learner_loss_cache():
     learner = Learner1D(lambda x: x, bounds=(-1, 1))
     learner.tell(-1, -1)
@@ -26,7 +29,7 @@ def test_balancing_learner_loss_cache():
     assert bl.loss(real=True) == real_loss
 
 
-@pytest.mark.parametrize('strategy', ['loss', 'loss_improvements', 'npoints'])
+@pytest.mark.parametrize('strategy', strategies)
 def test_distribute_first_points_over_learners(strategy):
     for initial_points in [0, 3]:
         learners = [Learner1D(lambda x: x, bounds=(-1, 1)) for i in range(10)]
@@ -41,7 +44,7 @@ def test_distribute_first_points_over_learners(strategy):
         assert len(set(i_learner)) == len(learners)
 
 
-@pytest.mark.parametrize('strategy', ['loss', 'loss_improvements', 'npoints'])
+@pytest.mark.parametrize('strategy', strategies)
 def test_ask_0(strategy):
     learners = [Learner1D(lambda x: x, bounds=(-1, 1)) for i in range(10)]
     learner = BalancingLearner(learners, strategy=strategy)
@@ -53,6 +56,7 @@ def test_ask_0(strategy):
     ('loss', lambda l: l.loss() < 0.1),
     ('loss_improvements', lambda l: l.loss() < 0.1),
     ('npoints', lambda bl: all(l.npoints > 10 for l in bl.learners)),
+    ('cycle', lambda l: l.loss() < 0.1),
 ])
 def test_strategies(strategy, goal):
     learners = [Learner1D(lambda x: x, bounds=(-1, 1)) for i in range(10)]