Fix MvStudentT deprecation: cov -> scale

Author: Armavica

pymc/distributions/multivariate.py

@@ -380,7 +380,7 @@ class MvStudentT(Continuous):
     def dist(cls, nu, Sigma=None, mu=None, scale=None, tau=None, chol=None, lower=True, **kwargs):
         if kwargs.get("cov") is not None:
             warnings.warn(
-                "Use the scale argument to specify the scale matrix."
+                "Use the scale argument to specify the scale matrix. "
                 "cov will be removed in future versions.",
                 FutureWarning,
             )

pymc/gp/gp.py

@@ -307,7 +307,7 @@ def _build_prior(self, name, X, reparameterize=True, jitter=JITTER_DEFAULT, **kw
             v = pm.StudentT(name + "_rotated_", mu=0.0, sigma=1.0, nu=self.nu, size=size, **kwargs)
             f = pm.Deterministic(name, mu + cholesky(cov).dot(v), dims=kwargs.get("dims", None))
         else:
-            f = pm.MvStudentT(name, nu=self.nu, mu=mu, cov=cov, **kwargs)
+            f = pm.MvStudentT(name, nu=self.nu, mu=mu, scale=cov, **kwargs)
         return f
 
     def prior(self, name, X, reparameterize=True, jitter=JITTER_DEFAULT, **kwargs):
@@ -376,7 +376,7 @@ def conditional(self, name, Xnew, jitter=JITTER_DEFAULT, **kwargs):
         X = self.X
         f = self.f
         nu2, mu, cov = self._build_conditional(Xnew, X, f, jitter)
-        return pm.MvStudentT(name, nu=nu2, mu=mu, cov=cov, **kwargs)
+        return pm.MvStudentT(name, nu=nu2, mu=mu, scale=cov, **kwargs)
 
 
 @conditioned_vars(["X", "y", "sigma"])

pymc/tests/distributions/test_multivariate.py

@@ -1049,7 +1049,7 @@ def test_car_moment(self, mu, size, expected):
     )
     def test_mvstudentt_moment(self, nu, mu, cov, size, expected):
         with pm.Model() as model:
-            x = pm.MvStudentT("x", nu=nu, mu=mu, cov=cov, size=size)
+            x = pm.MvStudentT("x", nu=nu, mu=mu, scale=cov, size=size)
 
         # MvStudentT logp is only impemented for up to 2D variables
         assert_moment_is_expected(model, expected, check_finite_logp=x.ndim < 3)
@@ -1369,28 +1369,28 @@ def test_issue_3706(self):
 
 
 class TestMvStudentTCov(BaseTestDistributionRandom):
-    def mvstudentt_rng_fn(self, size, nu, mu, cov, rng):
-        mv_samples = rng.multivariate_normal(np.zeros_like(mu), cov, size=size)
+    def mvstudentt_rng_fn(self, size, nu, mu, scale, rng):
+        mv_samples = rng.multivariate_normal(np.zeros_like(mu), scale, size=size)
         chi2_samples = rng.chisquare(nu, size=size)
         return (mv_samples / np.sqrt(chi2_samples[:, None] / nu)) + mu
 
     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]]),
+        "scale": 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]]),
+        "scale": 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 = {
         "nu": 5,
         "mu": np.array([1.0, 2.0]),
-        "cov": np.array([[2.0, 0.0], [0.0, 3.5]]),
+        "scale": np.array([[2.0, 0.0], [0.0, 3.5]]),
     }
     reference_dist = lambda self: ft.partial(self.mvstudentt_rng_fn, rng=self.get_random_state())
     checks_to_run = [
@@ -1409,29 +1409,29 @@ def check_errors(self):
                     "mvstudentt",
                     nu=np.array([1, 2]),
                     mu=np.ones(2),
-                    cov=np.full((2, 2), np.ones(2)),
+                    scale=np.full((2, 2), np.ones(2)),
                 )
 
     def check_mu_broadcast_helper(self):
         """Test that mu is broadcasted to the shape of cov"""
-        x = pm.MvStudentT.dist(nu=4, mu=1, cov=np.eye(3))
+        x = pm.MvStudentT.dist(nu=4, mu=1, scale=np.eye(3))
         mu = x.owner.inputs[4]
         assert mu.eval().shape == (3,)
 
-        x = pm.MvStudentT.dist(nu=4, mu=np.ones(1), cov=np.eye(3))
+        x = pm.MvStudentT.dist(nu=4, mu=np.ones(1), scale=np.eye(3))
         mu = x.owner.inputs[4]
         assert mu.eval().shape == (3,)
 
-        x = pm.MvStudentT.dist(nu=4, mu=np.ones((1, 1)), cov=np.eye(3))
+        x = pm.MvStudentT.dist(nu=4, mu=np.ones((1, 1)), scale=np.eye(3))
         mu = x.owner.inputs[4]
         assert mu.eval().shape == (1, 3)
 
-        x = pm.MvStudentT.dist(nu=4, mu=np.ones((10, 1)), cov=np.eye(3))
+        x = pm.MvStudentT.dist(nu=4, mu=np.ones((10, 1)), scale=np.eye(3))
         mu = x.owner.inputs[4]
         assert mu.eval().shape == (10, 3)
 
         # Cov is artificually limited to being 2D
-        # x = pm.MvStudentT.dist(nu=4, mu=np.ones((10, 1)), cov=np.full((2, 3, 3), np.eye(3)))
+        # x = pm.MvStudentT.dist(nu=4, mu=np.ones((10, 1)), scale=np.full((2, 3, 3), np.eye(3)))
         # mu = x.owner.inputs[4]
         # assert mu.eval().shape == (10, 2, 3)
 
@@ -1446,7 +1446,7 @@ class TestMvStudentTChol(BaseTestDistributionRandom):
     expected_rv_op_params = {
         "nu": 5,
         "mu": np.array([1.0, 2.0]),
-        "cov": quaddist_matrix(chol=pymc_dist_params["chol"]).eval(),
+        "scale": quaddist_matrix(chol=pymc_dist_params["chol"]).eval(),
     }
     checks_to_run = ["check_pymc_params_match_rv_op"]