Replace point goals

Author: basnijholt

adaptive/tests/test_learner1d.py

@@ -298,7 +298,7 @@ def test_equal(l1, l2):
     for function in [f, f_vec]:
         learner = Learner1D(function, bounds=(-1, 1))
         learner2 = Learner1D(function, bounds=(-1, 1))
-        simple(learner, goal=lambda l: l.npoints > 200)
+        simple(learner, goal=200)
         xs, ys = zip(*learner.data.items())
 
         # Make the scale huge to no get a scale doubling
@@ -374,7 +374,7 @@ def f(x):
     loss = curvature_loss_function()
     assert loss.nth_neighbors == 1
     learner = Learner1D(f, (-1, 1), loss_per_interval=loss)
-    simple(learner, goal=lambda l: l.npoints > 100)
+    simple(learner, goal=100)
     assert learner.npoints > 100
 
 
@@ -385,7 +385,7 @@ def f(x):
     loss = curvature_loss_function()
     assert loss.nth_neighbors == 1
     learner = Learner1D(f, (-1, 1), loss_per_interval=loss)
-    simple(learner, goal=lambda l: l.npoints > 100)
+    simple(learner, goal=100)
     assert learner.npoints > 100
 
 
@@ -398,7 +398,7 @@ def f(x):
         return x + a**2 / (a**2 + x**2)
 
     learner = Learner1D(f, bounds=(-1, 1))
-    simple(learner, lambda l: l.npoints > 100)
+    simple(learner, 100)
 
 
 def test_inf_loss_with_missing_bounds():
@@ -408,6 +408,6 @@ def test_inf_loss_with_missing_bounds():
         loss_per_interval=curvature_loss_function(),
     )
     # must be done in parallel because otherwise the bounds will be evaluated first
-    BlockingRunner(learner, goal=lambda learner: learner.loss() < 0.01)
+    BlockingRunner(learner, goal=0.01)
 
     learner.npoints > 20

adaptive/tests/test_runner.py

@@ -43,7 +43,7 @@ def f(x):
         return x
 
     learner = Learner1D(f, (-1, 1))
-    runner(learner, lambda l: l.npoints > 10)
+    runner(learner, 10)
     assert len(learner.data) > 10
 
 
@@ -152,5 +152,5 @@ def test_loky_executor(loky_executor):
 
 def test_default_executor():
     learner = Learner1D(linear, (-1, 1))
-    runner = AsyncRunner(learner, goal=lambda l: l.npoints > 10)
+    runner = AsyncRunner(learner, goal=10)
     asyncio.get_event_loop().run_until_complete(runner.task)

adaptive/tests/unit/test_learnernd_integration.py

@@ -16,29 +16,29 @@ def ring_of_fire(xy, d=0.75):
 
 def test_learnerND_runs_to_10_points():
     learner = LearnerND(ring_of_fire, bounds=[(-1, 1), (-1, 1)])
-    SimpleRunner(learner, goal=lambda l: l.npoints >= 10)
+    SimpleRunner(learner, goal=10)
     assert learner.npoints == 10
 
 
 @pytest.mark.parametrize("execution_number", range(5))
 def test_learnerND_runs_to_10_points_Blocking(execution_number):
     learner = LearnerND(ring_of_fire, bounds=[(-1, 1), (-1, 1)])
-    BlockingRunner(learner, goal=lambda l: l.npoints >= 10)
+    BlockingRunner(learner, goal=10)
     assert learner.npoints >= 10
 
 
 def test_learnerND_curvature_runs_to_10_points():
     loss = curvature_loss_function()
     learner = LearnerND(ring_of_fire, bounds=[(-1, 1), (-1, 1)], loss_per_simplex=loss)
-    SimpleRunner(learner, goal=lambda l: l.npoints >= 10)
+    SimpleRunner(learner, goal=10)
     assert learner.npoints == 10
 
 
 @pytest.mark.parametrize("execution_number", range(5))
 def test_learnerND_curvature_runs_to_10_points_Blocking(execution_number):
     loss = curvature_loss_function()
     learner = LearnerND(ring_of_fire, bounds=[(-1, 1), (-1, 1)], loss_per_simplex=loss)
-    BlockingRunner(learner, goal=lambda l: l.npoints >= 10)
+    BlockingRunner(learner, goal=10)
     assert learner.npoints >= 10
 
 

docs/source/tutorial/tutorial.Learner1D.md

@@ -187,7 +187,7 @@ learner_h = adaptive.Learner1D(sin_exp, (-1, 1), loss_per_interval=uniform_loss)
 learner_1 = adaptive.Learner1D(sin_exp, (-1, 1), loss_per_interval=default_loss)
 learner_2 = adaptive.Learner1D(sin_exp, (-1, 1), loss_per_interval=curvature_loss)
 
-npoints_goal = lambda l: l.npoints >= 100
+npoints_goal = 100
 # adaptive.runner.simple is a non parallel blocking runner.
 adaptive.runner.simple(learner_h, goal=npoints_goal)
 adaptive.runner.simple(learner_1, goal=npoints_goal)

docs/source/tutorial/tutorial.LearnerND.md

@@ -123,7 +123,7 @@ b = [(-1, -1, -1), (-1, 1, -1), (-1, -1, 1), (-1, 1, 1), (1, 1, -1), (1, -1, -1)
 hull = scipy.spatial.ConvexHull(b)
 
 learner = adaptive.LearnerND(f, hull)
-adaptive.BlockingRunner(learner, goal=lambda l: l.npoints > 2000)
+adaptive.BlockingRunner(learner, goal=2000)
 
 learner.plot_isosurface(-0.5)
 ```

docs/source/tutorial/tutorial.SKOptLearner.md

@@ -47,7 +47,7 @@ learner = adaptive.SKOptLearner(
     acq_func="gp_hedge",
     acq_optimizer="lbfgs",
 )
-runner = adaptive.Runner(learner, ntasks=1, goal=lambda l: l.npoints > 40)
+runner = adaptive.Runner(learner, ntasks=1, goal=40)
 ```
 
 ```{code-cell} ipython3

docs/source/tutorial/tutorial.advanced-topics.md

@@ -90,7 +90,7 @@ def slow_f(x):
 
 
 learner = adaptive.Learner1D(slow_f, bounds=[0, 1])
-runner = adaptive.Runner(learner, goal=lambda l: l.npoints > 100)
+runner = adaptive.Runner(learner, goal=100)
 runner.start_periodic_saving(
     save_kwargs=dict(fname="data/periodic_example.p"), interval=6
 )