More test updates

Author: jessegrabowski

tests/statespace/filters/test_distributions.py

@@ -52,8 +52,8 @@ def pymc_model(data):
         data = pm.Data("data", data.values)
         P0_diag = pm.Exponential("P0_diag", 1, shape=(2,))
         P0 = pm.Deterministic("P0", pt.diag(P0_diag))
-        initial_trend = pm.Normal("initial_trend", shape=(2,))
-        sigma_trend = pm.Exponential("sigma_trend", 1, shape=(2,))
+        initial_trend = pm.Normal("level_trend_initial", shape=(2,))
+        sigma_trend = pm.Exponential("level_trend_sigma", 1, shape=(2,))
 
     return mod
 
@@ -69,8 +69,8 @@ def pymc_model_2(data):
     with pm.Model(coords=coords) as mod:
         P0_diag = pm.Exponential("P0_diag", 1, shape=(2,))
         P0 = pm.Deterministic("P0", pt.diag(P0_diag))
-        initial_trend = pm.Normal("initial_trend", shape=(2,))
-        sigma_trend = pm.Exponential("sigma_trend", 1, shape=(2,))
+        initial_trend = pm.Normal("level_trend_initial", shape=(2,))
+        sigma_trend = pm.Exponential("level_trend_sigma", 1, shape=(2,))
         sigma_me = pm.Exponential("sigma_error", 1)
 
     return mod
@@ -207,8 +207,8 @@ def test_lgss_with_time_varying_inputs(output_name, rng):
         exog_data = pm.Data("data_exog", X)
         P0_diag = pm.Exponential("P0_diag", 1, shape=(mod.k_states,))
         P0 = pm.Deterministic("P0", pt.diag(P0_diag))
-        initial_trend = pm.Normal("initial_trend", shape=(2,))
-        sigma_trend = pm.Exponential("sigma_trend", 1, shape=(2,))
+        initial_trend = pm.Normal("level_trend_initial", shape=(2,))
+        sigma_trend = pm.Exponential("level_trend_sigma", 1, shape=(2,))
         beta_exog = pm.Normal("beta_exog", shape=(3,))
 
         mod._insert_random_variables()

tests/statespace/models/structural/components/test_level_trend.py

@@ -45,7 +45,7 @@ def test_level_trend_multiple_observed_construction():
         "level[data_3]",
         "trend[data_3]",
     ]
-    assert mod.shock_names == ["level_shock[data_1]", "level_shock[data_2]", "level_shock[data_3]"]
+    assert mod.shock_names == ["level[data_1]", "level[data_2]", "level[data_3]"]
 
     Z, T, R = pytensor.function(
         [], [mod.ssm["design"], mod.ssm["transition"], mod.ssm["selection"]], mode="FAST_COMPILE"

tests/statespace/models/structural/test_core.py

@@ -22,8 +22,8 @@ def test_add_components():
     mod = ll + se
 
     ll_params = {
-        "initial_trend": np.zeros(2, dtype=floatX),
-        "sigma_trend": np.ones(2, dtype=floatX),
+        "level_trend_initial": np.zeros(2, dtype=floatX),
+        "level_trend_sigma": np.ones(2, dtype=floatX),
     }
     se_params = {
         "seasonal_coefs": np.ones(11, dtype=floatX),
@@ -93,8 +93,8 @@ def test_extract_components_from_idata(rng):
         x0 = pm.Normal("x0", dims=["state"])
         P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"])
         beta_exog = pm.Normal("beta_exog", dims=["exog_state"])
-        initial_trend = pm.Normal("initial_trend", dims=["trend_state"])
-        sigma_trend = pm.Exponential("sigma_trend", 1, dims=["trend_shock"])
+        initial_trend = pm.Normal("level_trend_initial", dims=["level_trend_state"])
+        sigma_trend = pm.Exponential("level_trend_sigma", 1, dims=["level_trend_shock"])
         seasonal_coefs = pm.Normal("seasonal", dims=["seasonal_state"])
         sigma_obs = pm.Exponential("sigma_obs", 1)