fix two moons test

Author: LarsKue

tests/test_networks/conftest.py

@@ -119,10 +119,10 @@ def generative_inference_network(request):
 
 
 @pytest.fixture(scope="function")
-def lst_net(summary_dim):
-    from bayesflow.networks import LSTNet
+def time_series_network(summary_dim):
+    from bayesflow.networks import TimeSeriesNetwork
 
-    return LSTNet(summary_dim=summary_dim)
+    return TimeSeriesNetwork(summary_dim=summary_dim)
 
 
 @pytest.fixture(scope="function")
@@ -139,7 +139,7 @@ def deep_set(summary_dim):
     return DeepSet(summary_dim=summary_dim)
 
 
-@pytest.fixture(params=[None, "lst_net", "set_transformer", "deep_set"], scope="function")
+@pytest.fixture(params=[None, "time_series_network", "set_transformer", "deep_set"], scope="function")
 def summary_network(request, summary_dim):
     if request.param is None:
         return None

tests/test_simulators/test_simulators.py

@@ -6,13 +6,11 @@ def test_two_moons(simulator, batch_size):
     samples = simulator.sample((batch_size,))
 
     assert isinstance(samples, dict)
-    assert list(samples.keys()) == ["r", "alpha", "theta", "x"]
+    assert list(samples.keys()) == ["parameters", "observables"]
     assert all(isinstance(value, np.ndarray) for value in samples.values())
 
-    assert samples["r"].shape == (batch_size, 1)
-    assert samples["alpha"].shape == (batch_size, 1)
-    assert samples["theta"].shape == (batch_size, 2)
-    assert samples["x"].shape == (batch_size, 2)
+    assert samples["parameters"].shape == (batch_size, 2)
+    assert samples["observables"].shape == (batch_size, 2)
 
 
 def test_sample(simulator, batch_size):