add type annotations for adaptive/tests/test_learner1d.py

Author: basnijholt

adaptive/tests/test_learner1d.py

@@ -1,4 +1,5 @@
 import random
+from typing import Tuple, Union
 
 import numpy as np
 
@@ -7,7 +8,7 @@
 from adaptive.runner import simple
 
 
-def test_pending_loss_intervals():
+def test_pending_loss_intervals() -> None:
     # https://github.com/python-adaptive/adaptive/issues/40
     learner = Learner1D(lambda x: x, (0, 4))
 
@@ -21,7 +22,7 @@ def test_pending_loss_intervals():
     assert set(learner.losses_combined.keys()) == {(0, 1), (1, 2), (2, 3.5), (3.5, 4.0)}
 
 
-def test_loss_interpolation_for_unasked_point():
+def test_loss_interpolation_for_unasked_point() -> None:
     # https://github.com/python-adaptive/adaptive/issues/40
     learner = Learner1D(lambda x: x, (0, 4))
 
@@ -53,7 +54,7 @@ def test_loss_interpolation_for_unasked_point():
     }
 
 
-def test_first_iteration():
+def test_first_iteration() -> None:
     """Edge cases where we ask for a few points at the start."""
     learner = Learner1D(lambda x: None, (-1, 1))
     points, loss_improvements = learner.ask(2)
@@ -87,7 +88,7 @@ def test_first_iteration():
     assert points == [1]
 
 
-def test_loss_interpolation():
+def test_loss_interpolation() -> None:
     learner = Learner1D(lambda _: 0, bounds=(-1, 1))
 
     learner.tell(-1, 0)
@@ -104,7 +105,9 @@ def test_loss_interpolation():
         assert abs(expected_loss - loss) < 1e-15, (expected_loss, loss)
 
 
-def _run_on_discontinuity(x_0, bounds):
+def _run_on_discontinuity(
+    x_0: Union[int, float], bounds: Union[Tuple[int, int], Tuple[float, float]]
+) -> Learner1D:
     def f(x):
         return -1 if x < x_0 else +1
 
@@ -116,7 +119,7 @@ def f(x):
     return learner
 
 
-def test_termination_on_discontinuities():
+def test_termination_on_discontinuities() -> None:
 
     learner = _run_on_discontinuity(0, (-1, 1))
     smallest_interval = min(abs(a - b) for a, b in learner.losses.keys())
@@ -131,7 +134,7 @@ def test_termination_on_discontinuities():
     assert smallest_interval >= 0.5e3 * np.finfo(float).eps
 
 
-def test_order_adding_points():
+def test_order_adding_points() -> None:
     # and https://github.com/python-adaptive/adaptive/issues/41
     learner = Learner1D(lambda x: x, (0, 1))
     learner.tell_many([1, 0, 0.5], [0, 0, 0])
@@ -140,7 +143,7 @@ def test_order_adding_points():
     learner.ask(1)
 
 
-def test_adding_existing_point_passes_silently():
+def test_adding_existing_point_passes_silently() -> None:
     # See https://github.com/python-adaptive/adaptive/issues/42
     learner = Learner1D(lambda x: x, (0, 4))
     learner.tell(0, 0)
@@ -149,7 +152,7 @@ def test_adding_existing_point_passes_silently():
     learner.tell(1, 100)
 
 
-def test_loss_at_machine_precision_interval_is_zero():
+def test_loss_at_machine_precision_interval_is_zero() -> None:
     """The loss of an interval smaller than _dx_eps
     should be set to zero."""
 
@@ -166,11 +169,11 @@ def goal(l):
     assert learner.npoints != 1000
 
 
-def small_deviations(x):
+def small_deviations(x: float) -> Union[int, float]:
     return 0 if x <= 1 else 1 + 10 ** (-random.randint(12, 14))
 
 
-def test_small_deviations():
+def test_small_deviations() -> None:
     """This tests whether the Learner1D can handle small deviations.
     See https://gitlab.kwant-project.org/qt/adaptive/merge_requests/73 and
     https://github.com/python-adaptive/adaptive/issues/78."""
@@ -205,7 +208,7 @@ def test_small_deviations():
             break
 
 
-def test_uniform_sampling1D_v2():
+def test_uniform_sampling1D_v2() -> None:
     def check(known, expect):
         def f(x):
             return x
@@ -227,7 +230,7 @@ def f(x):
     check([-1, -0.5], {-0.75, 0.25, 1})
 
 
-def test_add_data_unordered():
+def test_add_data_unordered() -> None:
     # see https://github.com/python-adaptive/adaptive/issues/44
     learner = Learner1D(lambda x: x, bounds=(-1, 1))
     xs = [-1, 1, 0]
@@ -239,14 +242,14 @@ def test_add_data_unordered():
     learner.ask(3)
 
 
-def test_ask_does_not_return_known_points_when_returning_bounds():
+def test_ask_does_not_return_known_points_when_returning_bounds() -> None:
     learner = Learner1D(lambda x: None, (-1, 1))
     learner.tell(0, 0)
     points, _ = learner.ask(3)
     assert 0 not in points
 
 
-def test_tell_many():
+def test_tell_many() -> None:
     def f(x, offset=0.123214):
         a = 0.01
         return (
@@ -355,7 +358,7 @@ def _random_run(learner, learner2, scale_doubling=True):
     test_equal(learner, learner2)
 
 
-def test_curvature_loss():
+def test_curvature_loss() -> None:
     def f(x):
         return np.tanh(20 * x)
 
@@ -366,7 +369,7 @@ def f(x):
     assert learner.npoints > 100
 
 
-def test_curvature_loss_vectors():
+def test_curvature_loss_vectors() -> None:
     def f(x):
         return np.tanh(20 * x), np.tanh(20 * (x - 0.4))
 
@@ -377,7 +380,7 @@ def f(x):
     assert learner.npoints > 100
 
 
-def test_NaN_loss():
+def test_NaN_loss() -> None:
     # see https://github.com/python-adaptive/adaptive/issues/145
     def f(x):
         a = 0.01