when pvals don't set, return marginal dist

bamt/networks/base.py

@@ -863,7 +863,7 @@ def plot(self, output: str):
 
         return network.show(f"visualization_result/" + output)
 
-    def get_dist(self, node_name: str, pvals: dict):
+    def get_dist(self, node_name: str, pvals: Optional[dict] = None):
         """
         Get a distribution from node with known parent values (conditional distribution).
 
@@ -877,8 +877,7 @@ def get_dist(self, node_name: str, pvals: dict):
 
         parents = node.cont_parents + node.disc_parents
         if not parents:
-            logger_network.error("No parents")
-            return
+            return self.distributions[node_name]
 
         pvals = [pvals[parent] for parent in parents]
 

tests/test_nodes.py

@@ -9,7 +9,7 @@
 logging.getLogger("nodes").setLevel(logging.CRITICAL)
 
 
-class DistributionAssertion(unittest.TestCase):
+class MyTest(unittest.TestCase):
     unittest.skip("This is an assertion.")
 
     def assertDist(self, dist, node_type):
@@ -22,7 +22,7 @@ def assertDistMixture(self, dist):
         return self.assertEqual(len(dist), 3, msg=f"Error on {dist}")
 
 
-class TestBaseNode(unittest.TestCase):
+class TestBaseNode(MyTest):
     def setUp(self):
         np.random.seed(510)
 
@@ -137,19 +137,26 @@ def test_get_dist_mixture(self):
 
         for i in range(-2, 0, 2):
             dist = hybrid_bn.get_dist(mixture_gauss.name, pvals={"Node0": i})
-            DistributionAssertion().assertDistMixture(dist)
+            self.assertDistMixture(dist)
 
         for i in range(-2, 0, 2):
             for j in self.data[cond_mixture_gauss.disc_parents[0]].unique().tolist():
                 dist = hybrid_bn.get_dist(
                     cond_mixture_gauss.name, pvals={"Node0": float(i), "Node4": j}
                 )
-                DistributionAssertion().assertDistMixture(dist)
+                self.assertDistMixture(dist)
 
     def test_get_dist(self):
         for node in self.bn.nodes:
             if not node.cont_parents + node.disc_parents:
+                dist = self.bn.get_dist(node.name)
+                if "mean" in dist.keys():
+                    self.assertTrue(isinstance(dist["mean"], float))
+                    self.assertTrue(isinstance(dist["variance"], float))
+                else:
+                    self.assertAlmostEqual(sum(dist["cprob"]), 1)
                 continue
+
             if len(node.cont_parents + node.disc_parents) == 1:
                 if node.disc_parents:
                     pvals = self.data[node.disc_parents[0]].unique().tolist()
@@ -160,19 +167,15 @@ def test_get_dist(self):
 
                 for pval in pvals:
                     dist = self.bn.get_dist(node.name, {parent: pval})
-                    DistributionAssertion().assertDist(
-                        dist, self.info["types"][node.name]
-                    )
+                    self.assertDist(dist, self.info["types"][node.name])
             else:
                 for i in self.data[node.disc_parents[0]].unique().tolist():
                     for j in range(-5, 5, 1):
                         dist = self.bn.get_dist(
                             node.name,
                             {node.cont_parents[0]: float(j), node.disc_parents[0]: i},
                         )
-                        DistributionAssertion().assertDist(
-                            dist, self.info["types"][node.name]
-                        )
+                        self.assertDist(dist, self.info["types"][node.name])
 
     # ???
     def test_choose_serialization(self):