removed asserts, added tests on sizes

pymc/distributions/multivariate.py

@@ -2259,12 +2259,6 @@ class ICAR(Continuous):
     constraint by finding the sum of the vector $\\phi$ and penalizing based on its
     distance from zero.
 
-    ========  ==========================================
-    Support   :math:`x \\in \\mathbb{R}^k`
-    Mean      :math:`0`
-    Variance  :math:`T^{-1}` ?
-    ========  ==========================================
-
     Parameters
     ----------
     W : ndarray of int
@@ -2365,14 +2359,10 @@ def dist(cls, W, sigma=1, zero_sum_strength=0.001, **kwargs):
         # check on sigma
 
         sigma = pt.as_tensor_variable(floatX(sigma))
-        sigma = Assert("sigma > 0")(sigma, pt.gt(sigma, 0))
 
         # check on centering_strength
 
         zero_sum_strength = pt.as_tensor_variable(floatX(zero_sum_strength))
-        zero_sum_strength = Assert("centering_strength > 0")(
-            zero_sum_strength, pt.gt(zero_sum_strength, 0)
-        )
 
         return super().dist([W, node1, node2, N, sigma, zero_sum_strength], **kwargs)
 

tests/distributions/test_multivariate.py

@@ -2093,7 +2093,15 @@ class TestICAR(BaseTestDistributionRandom):
         "sigma": 2,
         "zero_sum_strength": 0.001,
     }
-    checks_to_run = ["check_pymc_params_match_rv_op"]
+    checks_to_run = ["check_pymc_params_match_rv_op", "check_rv_inferred_size"]
+
+    def check_rv_inferred_size(self):
+        sizes_to_check = [None, (), 1, (1,), 5, (4, 5), (2, 4, 2)]
+        sizes_expected = [(3,), (3,), (1, 3), (1, 3), (5, 3), (4, 5, 3), (2, 4, 2, 3)]
+        for size, expected in zip(sizes_to_check, sizes_expected):
+            pymc_rv = self.pymc_dist.dist(**self.pymc_dist_params, size=size)
+            expected_symbolic = tuple(pymc_rv.shape.eval())
+            assert expected_symbolic == expected
 
     def test_icar_logp(self):
         W = np.array([[0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0]])