remove multiple batch sizes, remove multiple python version tests, remove update-workflows branch from workflow style tests, add __init__ and conftest to test_point_approximators (#443)

Author: LarsKue

.github/workflows/style.yaml

@@ -6,12 +6,10 @@ on:
     branches:
       - main
       - dev
-      - update-workflows
   push:
     branches:
       - main
       - dev
-      - update-workflows
 
 jobs:
   check-code-style:

.github/workflows/tests.yaml

@@ -24,7 +24,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.10"]  # we usually only need to test the oldest python version
         backend: ["jax", "tensorflow", "torch"]
 
     runs-on: ${{ matrix.os }}

tests/conftest.py

@@ -41,7 +41,7 @@ def pytest_make_parametrize_id(config, val, argname):
     return f"{argname}={repr(val)}"
 
 
-@pytest.fixture(params=[2, 3], scope="session")
+@pytest.fixture(params=[2], scope="session")
 def batch_size(request):
     return request.param
 
@@ -94,33 +94,3 @@ def random_set(batch_size, set_size, feature_size):
 @pytest.fixture(params=[2, 3], scope="session")
 def set_size(request):
     return request.param
-
-
-@pytest.fixture(params=["two_moons"], scope="session")
-def simulator(request):
-    return request.getfixturevalue(request.param)
-
-
-@pytest.fixture(scope="session")
-def training_dataset(simulator, batch_size):
-    from bayesflow.datasets import OfflineDataset
-
-    num_batches = 128
-    samples = simulator.sample((num_batches * batch_size,))
-    return OfflineDataset(samples, batch_size=batch_size)
-
-
-@pytest.fixture(scope="session")
-def two_moons(batch_size):
-    from bayesflow.simulators import TwoMoonsSimulator
-
-    return TwoMoonsSimulator()
-
-
-@pytest.fixture(scope="session")
-def validation_dataset(simulator, batch_size):
-    from bayesflow.datasets import OfflineDataset
-
-    num_batches = 16
-    samples = simulator.sample((num_batches * batch_size,))
-    return OfflineDataset(samples, batch_size=batch_size)

tests/test_approximators/test_point_approximators/__init__.py

@@ -3,7 +3,7 @@
 import keras
 
 
-@pytest.fixture(params=[2, 3])
+@pytest.fixture(params=[2])
 def batch_size(request):
     return request.param
 

tests/test_approximators/test_point_approximators/conftest.py

@@ -82,8 +82,3 @@ def quantiles(request):
 @pytest.fixture()
 def unordered(batch_size, num_quantiles, num_variables):
     return keras.random.normal((batch_size, num_quantiles, num_variables))
-
-
-# @pytest.fixture()
-# def random_matrix_batch(batch_size, num_variables):
-#     return keras.random.normal((batch_size, num_variables, num_variables))

tests/test_distributions/conftest.py

@@ -103,7 +103,7 @@ def test_save_and_load(tmp_path, summary_network, random_set):
 @pytest.mark.parametrize("stage", ["training", "validation"])
 def test_compute_metrics(stage, summary_network, random_set):
     if summary_network is None:
-        pytest.skip()
+        pytest.skip("Nothing to do, because there is no summary network.")
 
     summary_network.build(keras.ops.shape(random_set))
 

tests/test_links/conftest.py

@@ -13,12 +13,12 @@ def check_combination_simulator_adapter(simulator, adapter):
         with pytest.raises(KeyError):
             adapter(simulator.sample(1))
         # Don't use this fixture combination for further tests.
-        pytest.skip()
+        pytest.skip(reason="Do not use this fixture combination for further tests")  # TODO: better reason
     elif simulator_with_sample_weight and not adapter_with_sample_weight:
         # When a weight key is present, but the adapter does not configure it
         # to be used as sample weight, no error is raised currently.
         # Don't use this fixture combination for further tests.
-        pytest.skip()
+        pytest.skip(reason="Do not use this fixture combination for further tests")  # TODO: better reason
 
 
 def check_approximator_multivariate_normal_score(approximator):
@@ -28,4 +28,4 @@ def check_approximator_multivariate_normal_score(approximator):
     if isinstance(approximator, PointApproximator):
         for score in approximator.inference_network.scores.values():
             if isinstance(score, MultivariateNormalScore):
-                pytest.skip()
+                pytest.skip(reason="MultivariateNormalScore is unstable")