refactor: changed test case atol to 0.2

Author: Michal-Novomestsky

tests/inference/INLA/test_inla.py

@@ -26,6 +26,40 @@ def rng():
     return np.random.default_rng(seed)
 
 
+@pytest.mark.filterwarnings(r"ignore:INLA is currently experimental")
+def test_AR1(rng):
+    T = 10
+    x = np.zeros((T,))
+
+    # true stationarity:
+    true_theta = 0.95
+    # true standard deviation of the innovation:
+    true_sigma = 2.0
+    # true process mean:
+    true_center = 0.0
+
+    for t in range(1, T):
+        x[t] = true_theta * x[t - 1] + rng.normal(loc=true_center, scale=true_sigma)
+
+    y_obs = rng.poisson(np.exp(x))
+
+    with pm.Model() as ar1_inla:
+        theta = pm.Normal("theta", 0, 1.0)
+        tau = pm.Exponential("tau", 0.5)
+
+        x = pm.AR(
+            "x", rho=theta, tau=tau, steps=T - 1, init_dist=pm.Normal.dist(0, 100, shape=(T,))
+        )
+
+        y = pm.Poisson("y", mu=pm.math.exp(x), observed=y_obs)
+
+        # Use INLA
+        idata = pmx.fit(method="INLA", x=x, Q=tau, return_latent_posteriors=False)
+
+    theta_inla = idata.posterior.theta.mean(axis=(0, 1))
+    np.testing.assert_allclose(np.array([true_theta]), theta_inla, atol=0.2)
+
+
 @pytest.mark.filterwarnings(r"ignore:INLA is currently experimental")
 def test_3_layer_normal(rng):
     """
@@ -61,36 +95,3 @@ def test_3_layer_normal(rng):
         posterior_mean_true = (mu_mu + mu_true) / 2
         posterior_mean_inla = idata.posterior.mu.mean(axis=(0, 1))
         np.testing.assert_allclose(posterior_mean_true, posterior_mean_inla, atol=0.1)
-
-
-@pytest.mark.filterwarnings(r"ignore:INLA is currently experimental")
-def test_AR1(rng):
-    T = 10
-    x = np.zeros((T,))
-
-    # true stationarity:
-    true_theta = 0.95
-    # true standard deviation of the innovation:
-    true_sigma = 2.0
-    # true process mean:
-    true_center = 0.0
-
-    for t in range(1, T):
-        x[t] = true_theta * x[t - 1] + rng.normal(loc=true_center, scale=true_sigma)
-
-    y_obs = rng.poisson(np.exp(x))
-
-    with pm.Model() as ar1:
-        theta = pm.Normal("theta", 0.0, 1.0)
-        tau = pm.Exponential("tau", 0.5)
-
-        x = pm.AR(
-            "x", rho=theta, tau=tau, steps=T - 1, init_dist=pm.Normal.dist(0, 100, shape=(T,))
-        )
-
-        y = pm.Poisson("y", mu=pm.math.exp(x), observed=y_obs)
-
-        idata = pm.sample()
-
-    theta_inla = idata.posterior.theta.mean(axis=(0, 1))
-    np.testing.assert_allclose(np.array([true_theta]), theta_inla, atol=0.1)