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rerun pre-commit on all files
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-8
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4 files changed

+6
-8
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adaptive/tests/test_cquad.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -242,7 +242,7 @@ def test_approximating_intervals():
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@pytest.mark.xfail
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def test_removed_choose_mutiple_points_at_once():
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"""Given that a high-precision interval that was split into 2 low-precision ones,
245-
we should use the high-precision interval.
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we should use the high-precision interval.
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"""
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learner = IntegratorLearner(np.exp, bounds=(0, 1), tol=1e-15)
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n = ns[-1] + 2 * (ns[0] - 2) # first + two children (33+6=39)

adaptive/tests/test_learners.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -626,6 +626,6 @@ def test_convergence_for_arbitrary_ordering(learner_type, f, learner_kwargs):
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@run_with(Learner1D, Learner2D, LearnerND)
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def test_learner_subdomain(learner_type, f, learner_kwargs):
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"""Learners that never receive data outside of a subdomain should
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perform 'similarly' to learners defined on that subdomain only."""
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perform 'similarly' to learners defined on that subdomain only."""
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# XXX: not sure how to implement this. How do we measure "performance"?
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raise NotImplementedError()

adaptive/tests/test_pickling.py

Lines changed: 1 addition & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -86,9 +86,7 @@ def balancing_learner(f, learner_type, learner_kwargs):
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]
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@pytest.mark.parametrize(
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"learner_type, learner_kwargs, serializer, f", learners,
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)
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@pytest.mark.parametrize("learner_type, learner_kwargs, serializer, f", learners)
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def test_serialization_for(learner_type, learner_kwargs, serializer, f):
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"""Test serializing a learner using different serializers."""
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adaptive/tests/test_skopt_learner.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -12,7 +12,7 @@
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@pytest.mark.skipif(not with_scikit_optimize, reason="scikit-optimize is not installed")
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def test_skopt_learner_runs():
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"""The SKOptLearner provides very few guarantees about its
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behaviour, so we only test the most basic usage
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behaviour, so we only test the most basic usage
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"""
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def g(x, noise_level=0.1):
@@ -28,8 +28,8 @@ def g(x, noise_level=0.1):
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@pytest.mark.skipif(not with_scikit_optimize, reason="scikit-optimize is not installed")
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def test_skopt_learner_4D_runs():
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"""The SKOptLearner provides very few guarantees about its
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behaviour, so we only test the most basic usage
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In this case we test also for 4D domain
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behaviour, so we only test the most basic usage
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In this case we test also for 4D domain
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"""
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def g(x, noise_level=0.1):

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