type hint fixes for adaptive/tests/test_learners.py

adaptive/tests/test_learners.py

@@ -20,6 +20,7 @@
 from adaptive.learner import (
     AverageLearner,
     BalancingLearner,
+    BaseLearner,
     DataSaver,
     IntegratorLearner,
     Learner1D,
@@ -92,28 +93,15 @@ def uniform(a: Union[int, float], b: int) -> Callable:
 learner_function_combos = collections.defaultdict(list)
 
 
-def learn_with(
-    learner_type: Union[
-        Type[Learner2D],
-        Type[SequenceLearner],
-        Type[AverageLearner],
-        Type[Learner1D],
-        Type[LearnerND],
-    ],
-    **init_kwargs,
-) -> Callable:
+def learn_with(learner_type: Type[BaseLearner], **init_kwargs,) -> Callable:
     def _(f):
         learner_function_combos[learner_type].append((f, init_kwargs))
         return f
 
     return _
 
 
-def xfail(
-    learner: Union[Type[Learner2D], Type[LearnerND]]
-) -> Union[
-    Tuple[MarkDecorator, Type[Learner2D]], Tuple[MarkDecorator, Type[LearnerND]]
-]:
+def xfail(learner: Type[BaseLearner]) -> Tuple[MarkDecorator, Type[BaseLearner]]:
     return pytest.mark.xfail, learner
 
 
@@ -141,14 +129,7 @@ def linear_with_peak(x: Union[int, float], d: uniform(-1, 1)) -> float:
 @learn_with(Learner2D, bounds=((-1, 1), (-1, 1)))
 @learn_with(SequenceLearner, sequence=np.random.rand(1000, 2))
 def ring_of_fire(
-    xy: Union[
-        Tuple[float, float],
-        np.ndarray,
-        Tuple[int, int],
-        Tuple[float, float],
-        Tuple[float, float],
-    ],
-    d: uniform(0.2, 1),
+    xy: Union[np.ndarray, Tuple[float, float]], d: uniform(0.2, 1),
 ) -> float:
     a = 0.2
     x, y = xy
@@ -158,8 +139,7 @@ def ring_of_fire(
 @learn_with(LearnerND, bounds=((-1, 1), (-1, 1), (-1, 1)))
 @learn_with(SequenceLearner, sequence=np.random.rand(1000, 3))
 def sphere_of_fire(
-    xyz: Union[Tuple[float, float, float], Tuple[int, int, int], np.ndarray],
-    d: uniform(0.2, 1),
+    xyz: Union[Tuple[float, float, float], np.ndarray], d: uniform(0.2, 1),
 ) -> float:
     a = 0.2
     x, y, z = xyz
@@ -177,16 +157,7 @@ def gaussian(n: int) -> float:
 
 # Create a sequence of learner parameters by adding all
 # possible loss functions to an existing parameter set.
-def add_loss_to_params(
-    learner_type: Union[
-        Type[Learner2D],
-        Type[SequenceLearner],
-        Type[AverageLearner],
-        Type[Learner1D],
-        Type[LearnerND],
-    ],
-    existing_params: Dict[str, Any],
-) -> Any:
+def add_loss_to_params(learner_type, existing_params: Dict[str, Any],) -> Any:
     if learner_type not in LOSS_FUNCTIONS:
         return [existing_params]
     loss_param, loss_functions = LOSS_FUNCTIONS[learner_type]
@@ -216,12 +187,7 @@ def ask_randomly(
     learner: Union[Learner1D, LearnerND, Learner2D],
     rounds: Tuple[int, int],
     points: Tuple[int, int],
-) -> Union[
-    Tuple[List[Union[Tuple[float, float, float], Tuple[int, int, int]]], List[float]],
-    Tuple[List[Union[Tuple[float, float], Tuple[int, int]]], List[float]],
-    Tuple[List[float], List[float]],
-    Tuple[List[Union[Tuple[int, int], Tuple[float, float]]], List[float]],
-]:
+):
     n_rounds = random.randrange(*rounds)
     n_points = [random.randrange(*points) for _ in range(n_rounds)]
 
@@ -240,7 +206,7 @@ def ask_randomly(
 
 @run_with(Learner1D)
 def test_uniform_sampling1D(
-    learner_type: Type[Learner1D],
+    learner_type,
     f: Callable,
     learner_kwargs: Dict[str, Union[Tuple[int, int], Callable]],
 ) -> None:
@@ -262,7 +228,7 @@ def test_uniform_sampling1D(
 @pytest.mark.xfail
 @run_with(Learner2D, LearnerND)
 def test_uniform_sampling2D(
-    learner_type: Union[Type[Learner2D], Type[LearnerND]],
+    learner_type,
     f: Callable,
     learner_kwargs: Dict[
         str,
@@ -304,8 +270,7 @@ def test_uniform_sampling2D(
     ],
 )
 def test_learner_accepts_lists(
-    learner_type: Union[Type[Learner2D], Type[LearnerND], Type[Learner1D]],
-    bounds: Union[Tuple[int, int], List[Tuple[int, int]]],
+    learner_type, bounds: Union[Tuple[int, int], List[Tuple[int, int]]],
 ) -> None:
     def f(x):
         return [0, 1]
@@ -316,11 +281,7 @@ def f(x):
 
 @run_with(Learner1D, Learner2D, LearnerND, SequenceLearner)
 def test_adding_existing_data_is_idempotent(
-    learner_type: Union[
-        Type[SequenceLearner], Type[LearnerND], Type[Learner1D], Type[Learner2D]
-    ],
-    f: Callable,
-    learner_kwargs: Dict[str, Any],
+    learner_type, f: Callable, learner_kwargs: Dict[str, Any],
 ) -> None:
     """Adding already existing data is an idempotent operation.
 
@@ -369,15 +330,7 @@ def test_adding_existing_data_is_idempotent(
 #      but we xfail it now, as Learner2D will be deprecated anyway
 @run_with(Learner1D, xfail(Learner2D), LearnerND, AverageLearner, SequenceLearner)
 def test_adding_non_chosen_data(
-    learner_type: Union[
-        Type[Learner2D],
-        Type[SequenceLearner],
-        Type[AverageLearner],
-        Type[Learner1D],
-        Type[LearnerND],
-    ],
-    f: Callable,
-    learner_kwargs: Dict[str, Any],
+    learner_type, f: Callable, learner_kwargs: Dict[str, Any],
 ) -> None:
     """Adding data for a point that was not returned by 'ask'."""
     # XXX: learner, control and bounds are not defined
@@ -421,9 +374,7 @@ def test_adding_non_chosen_data(
 
 @run_with(Learner1D, xfail(Learner2D), xfail(LearnerND), AverageLearner)
 def test_point_adding_order_is_irrelevant(
-    learner_type: Union[
-        Type[AverageLearner], Type[LearnerND], Type[Learner1D], Type[Learner2D]
-    ],
+    learner_type,
     f: Callable,
     learner_kwargs: Dict[
         str,
@@ -478,9 +429,7 @@ def test_point_adding_order_is_irrelevant(
 # see https://github.com/python-adaptive/adaptive/issues/55
 @run_with(Learner1D, xfail(Learner2D), LearnerND, AverageLearner)
 def test_expected_loss_improvement_is_less_than_total_loss(
-    learner_type: Union[
-        Type[AverageLearner], Type[LearnerND], Type[Learner1D], Type[Learner2D]
-    ],
+    learner_type,
     f: Callable,
     learner_kwargs: Dict[
         str,
@@ -519,7 +468,7 @@ def test_expected_loss_improvement_is_less_than_total_loss(
 #      but we xfail it now, as Learner2D will be deprecated anyway
 @run_with(Learner1D, xfail(Learner2D), LearnerND)
 def test_learner_performance_is_invariant_under_scaling(
-    learner_type: Union[Type[Learner2D], Type[LearnerND], Type[Learner1D]],
+    learner_type,
     f: Callable,
     learner_kwargs: Dict[
         str,
@@ -583,15 +532,7 @@ def test_learner_performance_is_invariant_under_scaling(
     with_all_loss_functions=False,
 )
 def test_balancing_learner(
-    learner_type: Union[
-        Type[Learner2D],
-        Type[SequenceLearner],
-        Type[AverageLearner],
-        Type[Learner1D],
-        Type[LearnerND],
-    ],
-    f: Callable,
-    learner_kwargs: Dict[str, Any],
+    learner_type, f: Callable, learner_kwargs: Dict[str, Any],
 ) -> None:
     """Test if the BalancingLearner works with the different types of learners."""
     learners = [
@@ -638,17 +579,7 @@ def test_balancing_learner(
     SequenceLearner,
     with_all_loss_functions=False,
 )
-def test_saving(
-    learner_type: Union[
-        Type[Learner2D],
-        Type[SequenceLearner],
-        Type[AverageLearner],
-        Type[Learner1D],
-        Type[LearnerND],
-    ],
-    f: Callable,
-    learner_kwargs: Dict[str, Any],
-) -> None:
+def test_saving(learner_type, f: Callable, learner_kwargs: Dict[str, Any],) -> None:
     f = generate_random_parametrization(f)
     learner = learner_type(f, **learner_kwargs)
     control = learner_type(f, **learner_kwargs)
@@ -680,15 +611,7 @@ def test_saving(
     with_all_loss_functions=False,
 )
 def test_saving_of_balancing_learner(
-    learner_type: Union[
-        Type[Learner2D],
-        Type[SequenceLearner],
-        Type[AverageLearner],
-        Type[Learner1D],
-        Type[LearnerND],
-    ],
-    f: Callable,
-    learner_kwargs: Dict[str, Any],
+    learner_type, f: Callable, learner_kwargs: Dict[str, Any],
 ) -> None:
     f = generate_random_parametrization(f)
     learner = BalancingLearner([learner_type(f, **learner_kwargs)])
@@ -727,9 +650,7 @@ def fname(learner):
     with_all_loss_functions=False,
 )
 def test_saving_with_datasaver(
-    learner_type: Union[
-        Type[Learner2D], Type[AverageLearner], Type[LearnerND], Type[Learner1D]
-    ],
+    learner_type,
     f: Callable,
     learner_kwargs: Dict[
         str,
@@ -770,7 +691,7 @@ def test_saving_with_datasaver(
 @pytest.mark.xfail
 @run_with(Learner1D, Learner2D, LearnerND)
 def test_convergence_for_arbitrary_ordering(
-    learner_type: Union[Type[Learner2D], Type[LearnerND], Type[Learner1D]],
+    learner_type,
     f: Callable,
     learner_kwargs: Dict[
         str,
@@ -794,17 +715,7 @@ def test_convergence_for_arbitrary_ordering(
 @pytest.mark.xfail
 @run_with(Learner1D, Learner2D, LearnerND)
 def test_learner_subdomain(
-    learner_type: Union[Type[Learner2D], Type[LearnerND], Type[Learner1D]],
-    f: Callable,
-    learner_kwargs: Dict[
-        str,
-        Union[
-            Tuple[Tuple[int, int], Tuple[int, int]],
-            Callable,
-            Tuple[int, int],
-            Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]],
-        ],
-    ],
+    learner_type, f: Callable, learner_kwargs,
 ):
     """Learners that never receive data outside of a subdomain should
        perform 'similarly' to learners defined on that subdomain only."""