Disable use of Arviz in pymc3.tests.test_data_container

pymc-devs · Mar 22, 2021 · 6d8c136 · 6d8c136
1 parent 2b4fd10
commit 6d8c136
Showing 1 changed file with 53 additions and 8 deletions.
diff --git a/pymc3/tests/test_data_container.py b/pymc3/tests/test_data_container.py
@@ -70,7 +70,13 @@ def test_sample_posterior_predictive_after_set_data(self):
             y = pm.Data("y", [1.0, 2.0, 3.0])
             beta = pm.Normal("beta", 0, 10.0)
             pm.Normal("obs", beta * x, np.sqrt(1e-2), observed=y)
-            trace = pm.sample(1000, tune=1000, chains=1)
+            trace = pm.sample(
+                1000,
+                tune=1000,
+                chains=1,
+                return_inferencedata=False,
+                compute_convergence_checks=False,
+            )
         # Predict on new data.
         with model:
             x_test = [5, 6, 9]
@@ -86,13 +92,27 @@ def test_sample_after_set_data(self):
             y = pm.Data("y", [1.0, 2.0, 3.0])
             beta = pm.Normal("beta", 0, 10.0)
             pm.Normal("obs", beta * x, np.sqrt(1e-2), observed=y)
-            pm.sample(1000, init=None, tune=1000, chains=1)
+            pm.sample(
+                1000,
+                init=None,
+                tune=1000,
+                chains=1,
+                return_inferencedata=False,
+                compute_convergence_checks=False,
+            )
         # Predict on new data.
         new_x = [5.0, 6.0, 9.0]
         new_y = [5.0, 6.0, 9.0]
         with model:
             pm.set_data(new_data={"x": new_x, "y": new_y})
-            new_trace = pm.sample(1000, init=None, tune=1000, chains=1)
+            new_trace = pm.sample(
+                1000,
+                init=None,
+                tune=1000,
+                chains=1,
+                return_inferencedata=False,
+                compute_convergence_checks=False,
+            )
             pp_trace = pm.sample_posterior_predictive(new_trace, 1000)
 
         assert pp_trace["obs"].shape == (1000, 3)
@@ -110,7 +130,14 @@ def test_shared_data_as_index(self):
             pm.Normal("obs", alpha[index], np.sqrt(1e-2), observed=y)
 
             prior_trace = pm.sample_prior_predictive(1000, var_names=["alpha"])
-            trace = pm.sample(1000, init=None, tune=1000, chains=1)
+            trace = pm.sample(
+                1000,
+                init=None,
+                tune=1000,
+                chains=1,
+                return_inferencedata=False,
+                compute_convergence_checks=False,
+            )
 
         # Predict on new data
         new_index = np.array([0, 1, 2])
@@ -132,14 +159,18 @@ def test_shared_data_as_rv_input(self):
         with pm.Model() as m:
             x = pm.Data("x", [1.0, 2.0, 3.0])
             _ = pm.Normal("y", mu=x, size=3)
-            trace = pm.sample(chains=1)
+            trace = pm.sample(
+                chains=1, return_inferencedata=False, compute_convergence_checks=False
+            )
 
         np.testing.assert_allclose(np.array([1.0, 2.0, 3.0]), x.get_value(), atol=1e-1)
         np.testing.assert_allclose(np.array([1.0, 2.0, 3.0]), trace["y"].mean(0), atol=1e-1)
 
         with m:
             pm.set_data({"x": np.array([2.0, 4.0, 6.0])})
-            trace = pm.sample(chains=1)
+            trace = pm.sample(
+                chains=1, return_inferencedata=False, compute_convergence_checks=False
+            )
 
         np.testing.assert_allclose(np.array([2.0, 4.0, 6.0]), x.get_value(), atol=1e-1)
         np.testing.assert_allclose(np.array([2.0, 4.0, 6.0]), trace["y"].mean(0), atol=1e-1)
@@ -175,7 +206,14 @@ def test_set_data_to_non_data_container_variables(self):
             y = np.array([1.0, 2.0, 3.0])
             beta = pm.Normal("beta", 0, 10.0)
             pm.Normal("obs", beta * x, np.sqrt(1e-2), observed=y)
-            pm.sample(1000, init=None, tune=1000, chains=1)
+            pm.sample(
+                1000,
+                init=None,
+                tune=1000,
+                chains=1,
+                return_inferencedata=False,
+                compute_convergence_checks=False,
+            )
         with pytest.raises(TypeError) as error:
             pm.set_data({"beta": [1.1, 2.2, 3.3]}, model=model)
         error.match("defined as `pymc3.Data` inside the model")
@@ -188,7 +226,14 @@ def test_model_to_graphviz_for_model_with_data_container(self):
             beta = pm.Normal("beta", 0, 10.0)
             obs_sigma = floatX(np.sqrt(1e-2))
             pm.Normal("obs", beta * x, obs_sigma, observed=y)
-            pm.sample(1000, init=None, tune=1000, chains=1)
+            pm.sample(
+                1000,
+                init=None,
+                tune=1000,
+                chains=1,
+                return_inferencedata=False,
+                compute_convergence_checks=False,
+            )
 
         for formatting in {"latex", "latex_with_params"}:
             with pytest.raises(ValueError, match="Unsupported formatting"):