Preprocess prior

arviz/tests/external_tests/test_data_beanmachine.py

@@ -22,7 +22,7 @@ class TestDataBeanMachine:
     @pytest.fixture(scope="class")
     def data(self, eight_schools_params, draws, chains):
         class Data:
-            model, obj = load_cached_models(
+            model, prior, obj = load_cached_models(
                 eight_schools_params,
                 draws,
                 chains,
@@ -71,14 +71,7 @@ def test_inference_data_has_log_likelihood_and_observed_data(self, data):
 
     def test_inference_data_no_posterior(self, data):
         model = data.model
-        prior = bm.GlobalNoUTurnSampler().infer(
-            queries=[model.mu(), model.tau(), model.eta()],
-            observations={},
-            num_samples=100,
-            num_adaptive_samples=100,
-            num_chains=2,
-        )
         # only prior
-        inference_data = from_beanmachine(prior)
+        inference_data = from_beanmachine(data.prior)
         assert not model.obs() in inference_data.posterior
         assert "observed_data" not in inference_data

arviz/tests/helpers.py

@@ -513,14 +513,23 @@ def obs(self):
             return dist.Normal(self.theta(), torch.from_numpy(data["sigma"]).float())
 
     model = EightSchools()
+
+    prior = bm.GlobalNoUTurnSampler().infer(
+        queries=[model.mu(), model.tau(), model.eta()],
+        observations={},
+        num_samples=draws,
+        num_adaptive_samples=500,
+        num_chains=chains,
+    )
+
     posterior = bm.GlobalNoUTurnSampler().infer(
         queries=[model.mu(), model.tau(), model.eta()],
         observations={model.obs(): torch.from_numpy(data["y"]).float()},
         num_samples=draws,
         num_adaptive_samples=500,
         num_chains=chains,
     )
-    return model, posterior
+    return model, prior, posterior
 
 
 def library_handle(library):