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fix unconditional sampling in ContinuousApproximator #548

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Merged
merged 1 commit into from
Jul 28, 2025
Merged

fix unconditional sampling in ContinuousApproximator #548

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2 changes: 1 addition & 1 deletion bayesflow/approximators/continuous_approximator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -537,7 +537,7 @@ def _sample(
)
batch_shape = keras.ops.shape(inference_conditions)[:-1]
else:
batch_shape = keras.ops.shape(inference_conditions)[1:-1]
batch_shape = (num_samples,)

return self.inference_network.sample(
batch_shape, conditions=inference_conditions, **filter_kwargs(kwargs, self.inference_network.sample)
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17 changes: 16 additions & 1 deletion tests/test_approximators/conftest.py
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Expand Up @@ -68,6 +68,9 @@ def point_inference_network_with_multiple_parametric_scores():
def point_approximator_with_single_parametric_score(adapter, point_inference_network, summary_network):
from bayesflow import PointApproximator

if "-> 'inference_conditions'" not in str(adapter) and "-> 'summary_conditions'" not in str(adapter):
pytest.skip("point approximator does not support unconditional estimation")

return PointApproximator(
adapter=adapter,
inference_network=point_inference_network,
Expand All @@ -81,6 +84,9 @@ def point_approximator_with_multiple_parametric_scores(
):
from bayesflow import PointApproximator

if "-> 'inference_conditions'" not in str(adapter) and "-> 'summary_conditions'" not in str(adapter):
pytest.skip("point approximator does not support unconditional estimation")

return PointApproximator(
adapter=adapter,
inference_network=point_inference_network_with_multiple_parametric_scores,
Expand Down Expand Up @@ -128,7 +134,16 @@ def adapter_with_sample_weight():
)


@pytest.fixture(params=["adapter_without_sample_weight", "adapter_with_sample_weight"])
@pytest.fixture()
def adapter_unconditional():
from bayesflow import ContinuousApproximator

return ContinuousApproximator.build_adapter(
inference_variables=["mean", "std"],
)


@pytest.fixture(params=["adapter_unconditional", "adapter_without_sample_weight", "adapter_with_sample_weight"])
def adapter(request):
return request.getfixturevalue(request.param)

Expand Down