refactor tests

Author: Sayam753

pymc3/tests/test_distributions.py

@@ -2937,16 +2937,11 @@ def test_car_logp(size):
 
 
 class TestBugfixes:
-    @pytest.mark.parametrize(
-        "dist_cls,kwargs", [(MvNormal, dict(mu=0)), (MvStudentT, dict(mu=0, nu=2))]
-    )
+    @pytest.mark.parametrize("dist_cls,kwargs", [(MvNormal, dict()), (MvStudentT, dict(nu=2))])
     @pytest.mark.parametrize("dims", [1, 2, 4])
     def test_issue_3051(self, dims, dist_cls, kwargs):
-        mu = np.repeat(kwargs["mu"], dims)
-        if "nu" in kwargs:
-            d = dist_cls.dist(nu=kwargs["nu"], mu=mu, cov=np.eye(dims), size=(20))
-        else:
-            d = dist_cls.dist(mu=mu, cov=np.eye(dims), size=(20))
+        mu = np.repeat(0, dims)
+        d = dist_cls.dist(mu=mu, cov=np.eye(dims), **kwargs, size=(20))
 
         X = np.random.normal(size=(20, dims))
         actual_t = logpt(d, X)

pymc3/tests/test_distributions_random.py

@@ -843,7 +843,7 @@ class TestPoisson(BaseTestDistribution):
     tests_to_run = ["check_pymc_params_match_rv_op"]
 
 
-class TestMvNormal(BaseTestDistribution):
+class TestMvNormalCov(BaseTestDistribution):
     pymc_dist = pm.MvNormal
     pymc_dist_params = {
         "mu": np.array([1.0, 2.0]),
@@ -893,6 +893,63 @@ class TestMvNormalTau(BaseTestDistribution):
     tests_to_run = ["check_pymc_params_match_rv_op"]
 
 
+class TestMvStudentTCov(BaseTestDistribution):
+    pymc_dist = pm.MvStudentT
+    pymc_dist_params = {
+        "nu": 5,
+        "mu": np.array([1.0, 2.0]),
+        "cov": np.array([[2.0, 0.0], [0.0, 3.5]]),
+    }
+    expected_rv_op_params = {
+        "nu": 5,
+        "mu": np.array([1.0, 2.0]),
+        "cov": np.array([[2.0, 0.0], [0.0, 3.5]]),
+    }
+    sizes_to_check = [None, (1), (2, 3)]
+    sizes_expected = [(2,), (1, 2), (2, 3, 2)]
+    reference_dist_params = {
+        "df": 5,
+        "loc": np.array([1.0, 2.0]),
+        "shape": np.array([[2.0, 0.0], [0.0, 3.5]]),
+    }
+    reference_dist = seeded_scipy_distribution_builder("multivariate_t")
+    tests_to_run = [
+        "check_pymc_params_match_rv_op",
+        "check_pymc_draws_match_reference",
+        "check_rv_size",
+    ]
+
+
+class TestMvStudentTChol(BaseTestDistribution):
+    pymc_dist = pm.MvStudentT
+    pymc_dist_params = {
+        "nu": 5,
+        "mu": np.array([1.0, 2.0]),
+        "chol": np.array([[2.0, 0.0], [0.0, 3.5]]),
+    }
+    expected_rv_op_params = {
+        "nu": 5,
+        "mu": np.array([1.0, 2.0]),
+        "cov": quaddist_matrix(chol=pymc_dist_params["chol"]).eval(),
+    }
+    tests_to_run = ["check_pymc_params_match_rv_op"]
+
+
+class TestMvStudentTTau(BaseTestDistribution):
+    pymc_dist = pm.MvStudentT
+    pymc_dist_params = {
+        "nu": 5,
+        "mu": np.array([1.0, 2.0]),
+        "tau": np.array([[2.0, 0.0], [0.0, 3.5]]),
+    }
+    expected_rv_op_params = {
+        "nu": 5,
+        "mu": np.array([1.0, 2.0]),
+        "cov": quaddist_matrix(tau=pymc_dist_params["tau"]).eval(),
+    }
+    tests_to_run = ["check_pymc_params_match_rv_op"]
+
+
 class TestDirichlet(BaseTestDistribution):
     pymc_dist = pm.Dirichlet
     pymc_dist_params = {"a": np.array([1.0, 2.0])}
@@ -1402,21 +1459,6 @@ def ref_rand_evd(size, mu, evds, sigma):
                 model_args=evd_args,
             )
 
-    def test_mv_t(self):
-        def ref_rand(size, nu, Sigma, mu):
-            normal = st.multivariate_normal.rvs(cov=Sigma, size=size)
-            chi2 = st.chi2.rvs(df=nu, size=size)[..., None]
-            return mu + (normal / np.sqrt(chi2 / nu))
-
-        for n in [2, 3]:
-            pymc3_random(
-                pm.MvStudentT,
-                {"nu": Domain([5, 10, 25, 50]), "Sigma": PdMatrix(n), "mu": Vector(R, n)},
-                size=100,
-                valuedomain=Vector(R, n),
-                ref_rand=ref_rand,
-            )
-
     @pytest.mark.xfail(reason="This distribution has not been refactored for v4")
     def test_dirichlet_multinomial(self):
         def ref_rand(size, a, n):