Refactor test_regression to cover innotations = True

AlexAndorra · AlexAndorra · commit b94c9a3b58a3 · 2025-07-14T12:44:09.000-04:00
diff --git a/tests/statespace/models/structural/components/test_regression.py b/tests/statespace/models/structural/components/test_regression.py
@@ -1,6 +1,7 @@
 import numpy as np
 import pandas as pd
 import pymc as pm
+import pytest
 
 from numpy.testing import assert_allclose
 from pytensor import config
@@ -15,121 +16,222 @@
 RTOL = 0 if config.floatX.endswith("64") else 1e-6
 
 
-def test_exogenous_component(rng):
-    data = rng.normal(size=(100, 2)).astype(config.floatX)
-    mod = st.RegressionComponent(state_names=["feature_1", "feature_2"], name="exog")
+@pytest.fixture
+def regression_data(rng):
+    """Generate test data for regression components (2 exogenous variables)."""
+    return rng.normal(size=(100, 2)).astype(config.floatX)
 
-    params = {"beta_exog": np.array([1.0, 2.0], dtype=config.floatX)}
-    exog_data = {"data_exog": data}
-    x, y = simulate_from_numpy_model(mod, rng, params, exog_data)
 
-    # Check that the generated data is just a linear regression
-    assert_allclose(y, data @ params["beta_exog"], atol=ATOL, rtol=RTOL)
+@pytest.fixture
+def multiple_regression_data(rng):
+    """Generate test data for multiple regression components."""
+    return {
+        "data_1": rng.normal(size=(100, 2)).astype(config.floatX),
+        "data_2": rng.normal(size=(100, 1)).astype(config.floatX),
+    }
 
-    mod = mod.build(verbose=False)
-    _assert_basic_coords_correct(mod)
-    assert mod.coords["state_exog"] == ["feature_1", "feature_2"]
 
-
-def test_adding_exogenous_component(rng):
-    data = rng.normal(size=(100, 2)).astype(config.floatX)
-    reg = st.RegressionComponent(state_names=["a", "b"], name="exog")
-    ll = st.LevelTrendComponent(name="level")
-
-    seasonal = st.FrequencySeasonality(name="annual", season_length=12, n=4)
-    mod = reg + ll + seasonal
-
-    assert mod.ssm["design"].eval({"data_exog": data}).shape == (100, 1, 2 + 2 + 8)
-    assert_allclose(mod.ssm["design", 5, 0, :2].eval({"data_exog": data}), data[5])
-
-
-def test_regression_with_multiple_observed_states(rng):
-    from scipy.linalg import block_diag
-
-    data = rng.normal(size=(100, 2)).astype(config.floatX)
-    mod = st.RegressionComponent(
-        state_names=["feature_1", "feature_2"],
-        name="exog",
-        observed_state_names=["data_1", "data_2"],
-    )
-
-    params = {"beta_exog": np.array([[1.0, 2.0], [3.0, 4.0]], dtype=config.floatX)}
-    exog_data = {"data_exog": data}
-    x, y = simulate_from_numpy_model(mod, rng, params, exog_data)
-
-    assert x.shape == (100, 4)  # 2 features, 2 states
-    assert y.shape == (100, 2)
-
-    # Check that the generated data are two independent linear regressions
-    assert_allclose(y[:, 0], data @ params["beta_exog"][0], atol=ATOL, rtol=RTOL)
-    assert_allclose(y[:, 1], data @ params["beta_exog"][1], atol=ATOL, rtol=RTOL)
-
-    mod = mod.build(verbose=False)
-    assert mod.coords["state_exog"] == [
-        "feature_1",
-        "feature_2",
-    ]
-
-    Z = mod.ssm["design"].eval({"data_exog": data})
-    vec_block_diag = np.vectorize(block_diag, signature="(n,m),(o,p)->(q,r)")
-    assert Z.shape == (100, 2, 4)
-    assert np.allclose(Z, vec_block_diag(data[:, None, :], data[:, None, :]))
-
-
-def test_add_regression_components_with_multiple_observed_states(rng):
-    from scipy.linalg import block_diag
-
-    data_1 = rng.normal(size=(100, 2)).astype(config.floatX)
-    data_2 = rng.normal(size=(100, 1)).astype(config.floatX)
-
-    reg1 = st.RegressionComponent(
-        state_names=["a", "b"], name="exog1", observed_state_names=["data_1", "data_2"]
-    )
-    reg2 = st.RegressionComponent(state_names=["c"], name="exog2", observed_state_names=["data_3"])
-
-    mod = (reg1 + reg2).build(verbose=False)
-    assert mod.coords["state_exog1"] == ["a", "b"]
-    assert mod.coords["state_exog2"] == ["c"]
-
-    Z = mod.ssm["design"].eval({"data_exog1": data_1, "data_exog2": data_2})
-    vec_block_diag = np.vectorize(block_diag, signature="(n,m),(o,p)->(q,r)")
-    assert Z.shape == (100, 3, 5)
-    assert np.allclose(
-        Z,
-        vec_block_diag(vec_block_diag(data_1[:, None, :], data_1[:, None, :]), data_2[:, None, :]),
-    )
-
-    x0 = mod.ssm["initial_state"].eval(
-        {
-            "beta_exog1": np.array([[1.0, 2.0], [3.0, 4.0]], dtype=config.floatX),
-            "beta_exog2": np.array([5.0], dtype=config.floatX),
-        }
-    )
-    np.testing.assert_allclose(x0, np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=config.floatX))
-
-
-def test_filter_scans_time_varying_design_matrix(rng):
+@pytest.fixture
+def time_series_data(rng):
+    """Generate time series data for PyMC integration tests."""
     time_idx = pd.date_range(start="2000-01-01", freq="D", periods=100)
     data = pd.DataFrame(rng.normal(size=(100, 2)), columns=["a", "b"], index=time_idx)
-
     y = pd.DataFrame(rng.normal(size=(100, 1)), columns=["data"], index=time_idx)
-
-    reg = st.RegressionComponent(state_names=["a", "b"], name="exog")
-    mod = reg.build(verbose=False)
-
-    with pm.Model(coords=mod.coords) as m:
-        data_exog = pm.Data("data_exog", data.values)
-
-        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=["state_exog"])
-
-        mod.build_statespace_graph(y)
-        x0, P0, c, d, T, Z, R, H, Q = mod.unpack_statespace()
-        pm.Deterministic("Z", Z)
-
-        prior = pm.sample_prior_predictive(draws=10)
-
-    prior_Z = prior.prior.Z.values
-    assert prior_Z.shape == (1, 10, 100, 1, 2)
-    assert_allclose(prior_Z[0, :, :, 0, :], data.values[None].repeat(10, axis=0))
+    return data, y
+
+
+class TestRegressionComponent:
+    """Test basic regression component functionality."""
+
+    @pytest.mark.parametrize("innovations", [False, True])
+    def test_exogenous_component(self, rng, regression_data, innovations):
+        """Test basic regression component with and without innovations."""
+        mod = st.RegressionComponent(
+            state_names=["feature_1", "feature_2"], name="exog", innovations=innovations
+        )
+
+        params = {"beta_exog": np.array([1.0, 2.0], dtype=config.floatX)}
+        if innovations:
+            params["sigma_beta_exog"] = np.array([0.1, 0.2], dtype=config.floatX)
+
+        exog_data = {"data_exog": regression_data}
+        x, y = simulate_from_numpy_model(mod, rng, params, exog_data)
+
+        if not innovations:
+            # Check that the generated data is just a linear regression
+            assert_allclose(y, regression_data @ params["beta_exog"], atol=ATOL, rtol=RTOL)
+        else:
+            # With innovations, the coefficients should vary over time
+            # The initial state should match the beta parameters
+            assert_allclose(x[0], params["beta_exog"], atol=ATOL, rtol=RTOL)
+
+        mod = mod.build(verbose=False)
+        _assert_basic_coords_correct(mod)
+        assert mod.coords["state_exog"] == ["feature_1", "feature_2"]
+
+        if innovations:
+            # Check that sigma_beta parameter is included
+            assert "sigma_beta_exog" in mod.param_names
+
+    @pytest.mark.parametrize("innovations", [False, True])
+    def test_adding_exogenous_component(self, rng, regression_data, innovations):
+        """Test adding regression component to other components."""
+        reg = st.RegressionComponent(state_names=["a", "b"], name="exog", innovations=innovations)
+        ll = st.LevelTrendComponent(name="level")
+        seasonal = st.FrequencySeasonality(name="annual", season_length=12, n=4)
+        mod = reg + ll + seasonal
+
+        assert mod.ssm["design"].eval({"data_exog": regression_data}).shape == (100, 1, 2 + 2 + 8)
+        assert_allclose(
+            mod.ssm["design", 5, 0, :2].eval({"data_exog": regression_data}), regression_data[5]
+        )
+
+        if innovations:
+            # Check that sigma_beta parameter is included in the combined model
+            assert "sigma_beta_exog" in mod.param_names
+
+
+class TestMultivariateRegression:
+    """Test multivariate regression functionality."""
+
+    @pytest.mark.parametrize("innovations", [False, True])
+    def test_regression_with_multiple_observed_states(self, rng, regression_data, innovations):
+        """Test multivariate regression with and without innovations."""
+        from scipy.linalg import block_diag
+
+        mod = st.RegressionComponent(
+            state_names=["feature_1", "feature_2"],
+            name="exog",
+            observed_state_names=["data_1", "data_2"],
+            innovations=innovations,
+        )
+
+        params = {"beta_exog": np.array([[1.0, 2.0], [3.0, 4.0]], dtype=config.floatX)}
+        if innovations:
+            params["sigma_beta_exog"] = np.array([[0.1, 0.2], [0.3, 0.4]], dtype=config.floatX)
+
+        exog_data = {"data_exog": regression_data}
+        x, y = simulate_from_numpy_model(mod, rng, params, exog_data)
+
+        assert x.shape == (100, 4)  # 2 features, 2 states
+        assert y.shape == (100, 2)
+
+        if not innovations:
+            # Check that the generated data are two independent linear regressions
+            assert_allclose(y[:, 0], regression_data @ params["beta_exog"][0], atol=ATOL, rtol=RTOL)
+            assert_allclose(y[:, 1], regression_data @ params["beta_exog"][1], atol=ATOL, rtol=RTOL)
+        else:
+            # Check that initial states match the beta parameters
+            assert_allclose(x[0, :2], params["beta_exog"][0], atol=ATOL, rtol=RTOL)
+            assert_allclose(x[0, 2:], params["beta_exog"][1], atol=ATOL, rtol=RTOL)
+
+        mod = mod.build(verbose=False)
+        assert mod.coords["state_exog"] == ["feature_1", "feature_2"]
+
+        Z = mod.ssm["design"].eval({"data_exog": regression_data})
+        vec_block_diag = np.vectorize(block_diag, signature="(n,m),(o,p)->(q,r)")
+        assert Z.shape == (100, 2, 4)
+        assert np.allclose(
+            Z,
+            vec_block_diag(regression_data[:, None, :], regression_data[:, None, :]),
+        )
+
+        if innovations:
+            # Check that sigma_beta parameter is included
+            assert "sigma_beta_exog" in mod.param_names
+
+
+class TestMultipleRegressionComponents:
+    """Test multiple regression components functionality."""
+
+    @pytest.mark.parametrize("innovations", [False, True])
+    def test_add_regression_components_with_multiple_observed_states(
+        self, rng, multiple_regression_data, innovations
+    ):
+        """Test adding multiple regression components with and without innovations."""
+        from scipy.linalg import block_diag
+
+        reg1 = st.RegressionComponent(
+            state_names=["a", "b"],
+            name="exog1",
+            observed_state_names=["data_1", "data_2"],
+            innovations=innovations,
+        )
+        reg2 = st.RegressionComponent(
+            state_names=["c"],
+            name="exog2",
+            observed_state_names=["data_3"],
+            innovations=innovations,
+        )
+
+        mod = (reg1 + reg2).build(verbose=False)
+        assert mod.coords["state_exog1"] == ["a", "b"]
+        assert mod.coords["state_exog2"] == ["c"]
+
+        Z = mod.ssm["design"].eval(
+            {
+                "data_exog1": multiple_regression_data["data_1"],
+                "data_exog2": multiple_regression_data["data_2"],
+            }
+        )
+        vec_block_diag = np.vectorize(block_diag, signature="(n,m),(o,p)->(q,r)")
+        assert Z.shape == (100, 3, 5)
+        assert np.allclose(
+            Z,
+            vec_block_diag(
+                vec_block_diag(
+                    multiple_regression_data["data_1"][:, None, :],
+                    multiple_regression_data["data_1"][:, None, :],
+                ),
+                multiple_regression_data["data_2"][:, None, :],
+            ),
+        )
+
+        x0 = mod.ssm["initial_state"].eval(
+            {
+                "beta_exog1": np.array([[1.0, 2.0], [3.0, 4.0]], dtype=config.floatX),
+                "beta_exog2": np.array([5.0], dtype=config.floatX),
+            }
+        )
+        np.testing.assert_allclose(x0, np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=config.floatX))
+
+        if innovations:
+            # Check that sigma_beta parameters are included
+            assert "sigma_beta_exog1" in mod.param_names
+            assert "sigma_beta_exog2" in mod.param_names
+
+
+class TestPyMCIntegration:
+    """Test PyMC integration functionality."""
+
+    @pytest.mark.parametrize("innovations", [False, True])
+    def test_filter_scans_time_varying_design_matrix(self, rng, time_series_data, innovations):
+        """Test PyMC integration with and without innovations."""
+        data, y = time_series_data
+
+        reg = st.RegressionComponent(state_names=["a", "b"], name="exog", innovations=innovations)
+        mod = reg.build(verbose=False)
+
+        with pm.Model(coords=mod.coords) as m:
+            data_exog = pm.Data("data_exog", data.values)
+
+            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=["state_exog"])
+
+            if innovations:
+                sigma_beta_exog = pm.Exponential("sigma_beta_exog", 1, dims=["state_exog"])
+
+            mod.build_statespace_graph(y)
+            x0, P0, c, d, T, Z, R, H, Q = mod.unpack_statespace()
+            pm.Deterministic("Z", Z)
+
+            prior = pm.sample_prior_predictive(draws=10)
+
+        prior_Z = prior.prior.Z.values
+        assert prior_Z.shape == (1, 10, 100, 1, 2)
+        assert_allclose(prior_Z[0, :, :, 0, :], data.values[None].repeat(10, axis=0))
+
+        if innovations:
+            # Check that sigma_beta parameter is included in the prior
+            assert "sigma_beta_exog" in prior.prior.data_vars
