add aesara assert

pymc3/distributions/multivariate.py

@@ -24,6 +24,7 @@
 import numpy as np
 import scipy
 
+from aesara.assert_op import Assert
 from aesara.graph.basic import Apply
 from aesara.graph.op import Op
 from aesara.sparse.basic import sp_sum
@@ -1939,12 +1940,12 @@ def make_node(self, rng, size, dtype, mu, W, alpha, tau):
             raise ValueError("W must be a matrix (ndim=2).")
 
         sparse = isinstance(W, aesara.sparse.SparseVariable)
+        msg = "W must be a symmetric adjacency matrix."
         if sparse:
-            if not at.isclose(aesara.sparse.basic.sp_sum(W - W.T), 0):
-                raise ValueError("W must be a symmetric adjacency matrix.")
+            abs_diff = aesara.sparse.basic.mul(aesara.sparse.basic.sgn(W - W.T), W - W.T)
+            W = Assert(msg)(W, at.isclose(aesara.sparse.basic.sp_sum(abs_diff), 0))
         else:
-            if not at.allclose(W, W.T):
-                raise ValueError("W must be a symmetric adjacency matrix.")
+            W = Assert(msg)(W, at.allclose(W, W.T))
 
         tau = at.as_tensor_variable(floatX(tau))
         alpha = at.as_tensor_variable(floatX(alpha))

pymc3/tests/test_distributions.py

@@ -3093,6 +3093,7 @@ def test_car_symmetry_check(sparse):
     tau = 2
     alpha = 0.5
     mu = np.zeros(4)
+    xs = np.random.randn(*mu.shape)
 
     # non-symmetric matrix
     W = np.array(
@@ -3102,8 +3103,9 @@ def test_car_symmetry_check(sparse):
     if sparse:
         W = aesara.sparse.csr_from_dense(W)
 
-    with pytest.raises(ValueError):
-        car_dist = CAR.dist(mu, W, alpha, tau)
+    car_dist = CAR.dist(mu, W, alpha, tau)
+    with pytest.raises(AssertionError):
+        logp(car_dist, xs).eval()
 
 
 class TestBugfixes: