tests for belief propagation with AND and mixed AND and OR nodes

2 files changed, 113 insertions, 11 deletions

diff --git a/beliefs/factors/bernoulli_or_cpd.py b/beliefs/factors/bernoulli_or_cpd.py
index bfb3a95..162e156 100644
--- a/beliefs/factors/bernoulli_or_cpd.py
+++ b/beliefs/factors/bernoulli_or_cpd.py
@@ -25,7 +25,7 @@ class BernoulliOrCPD(TabularCPD):
 
     @property
     def values(self):
-        if not any(self._values):
+        if len(self._values) == 0:
             self._values = self._build_kwise_values_array(len(self.variables))
             self._values = self._values.reshape(self.cardinality)
         return self._values
diff --git a/tests/test_belief_propagation.py b/tests/test_belief_propagation.py
index 5c5a612..7a77311 100644
--- a/tests/test_belief_propagation.py
+++ b/tests/test_belief_propagation.py
@@ -5,13 +5,14 @@ from pytest import approx
 from beliefs.inference.belief_propagation import BeliefPropagation, ConflictingEvidenceError
 from beliefs.models.belief_update_node_model import (
     BeliefUpdateNodeModel,
-    BernoulliOrNode
+    BernoulliOrNode,
+    BernoulliAndNode
 )
 
 
 @pytest.fixture(scope='module')
-def edges_four_nodes():
-    """Edges define a polytree with 4 nodes (connected in an X-shape with the
+def edges_five_nodes():
+    """Edges define a polytree with 5 nodes (connected in an X-shape with the
     node, 'x', at the center of the X."""
     edges = [('u', 'x'), ('v', 'x'), ('x', 'y'), ('x', 'z')]
     return edges
@@ -38,8 +39,8 @@ def many_parents_edges():
 
 
 @pytest.fixture(scope='function')
-def four_node_model(edges_four_nodes):
-    return BeliefUpdateNodeModel.init_from_edges(edges_four_nodes, BernoulliOrNode)
+def five_node_model(edges_five_nodes):
+    return BeliefUpdateNodeModel.init_from_edges(edges_five_nodes, BernoulliOrNode)
 
 
 @pytest.fixture(scope='function')
@@ -53,11 +54,41 @@ def many_parents_model(many_parents_edges):
 
 
 @pytest.fixture(scope='function')
+def many_parents_and_model(many_parents_edges):
+    return BeliefUpdateNodeModel.init_from_edges(many_parents_edges, BernoulliAndNode)
+
+
+@pytest.fixture(scope='function')
 def one_node_model():
     a_node = BernoulliOrNode(label_id='x', children=[], parents=[])
     return BeliefUpdateNodeModel(nodes_dict={'x': a_node})
 
 
+@pytest.fixture(scope='function')
+def five_node_and_model(edges_five_nodes):
+    return BeliefUpdateNodeModel.init_from_edges(edges_five_nodes, BernoulliAndNode)
+
+
+@pytest.fixture(scope='function')
+def mixed_cpd_model(edges_five_nodes):
+    """
+    X-shaped 5 node model plus one more node, 'w', with edge from 'w' to 'z'.
+    'z' is an AND node while all other nodes are OR nodes.
+    """
+    u_node = BernoulliOrNode(label_id='u', children=['x'], parents=[])
+    v_node = BernoulliOrNode(label_id='v', children=['x'], parents=[])
+    x_node = BernoulliOrNode(label_id='x', children=['y', 'z'], parents=['u', 'v'])
+    y_node = BernoulliOrNode(label_id='y', children=[], parents=['x'])
+    z_node = BernoulliAndNode(label_id='z', children=[], parents=['x', 'w'])
+    w_node = BernoulliOrNode(label_id='w', children=['z'], parents=[])
+    return BeliefUpdateNodeModel(nodes_dict={'u': u_node,
+                                             'v': v_node,
+                                             'x': x_node,
+                                             'y': y_node,
+                                             'z': z_node,
+                                             'w': w_node})
+
+
 def get_label_mapped_to_positive_belief(query_result):
     """Return a dictionary mapping each label_id to the probability of
     the label being True."""
@@ -118,27 +149,90 @@ def test_NO_evidence_one_node_model(one_node_model):
 
 
 #==============================================================================================
-# Tests of 4-node, 4-edge model
+# Tests of 5-node, 4-edge model
 
-def test_no_evidence_four_node_model(four_node_model):
+def test_no_evidence_five_node_model(five_node_model):
     expected = {'x': 1-0.5**2}
-    infer = BeliefPropagation(four_node_model)
+    infer = BeliefPropagation(five_node_model)
     query_result = infer.query(evidence={})
     result = get_label_mapped_to_positive_belief(query_result)
     compare_dictionaries(expected, result)
 
 
-def test_virtual_evidence_for_node_x_four_node_model(four_node_model):
+def test_virtual_evidence_for_node_x_five_node_model(five_node_model):
     """Virtual evidence for node x."""
     expected = {'x': 0.967741935483871, 'y': 0.967741935483871, 'z': 0.967741935483871,
                 'u': 0.6451612903225806, 'v': 0.6451612903225806}
-    infer = BeliefPropagation(four_node_model)
+    infer = BeliefPropagation(five_node_model)
     query_result = infer.query(evidence={'x': np.array([1, 10])})
     result = get_label_mapped_to_positive_belief(query_result)
     compare_dictionaries(expected, result)
 
 
 #==============================================================================================
+# Tests of 5-node, 4-edge model with AND cpds
+
+def test_no_evidence_five_node_and_model(five_node_and_model):
+    expected = {'x': 0.5**2}
+    infer = BeliefPropagation(five_node_and_model)
+    query_result = infer.query(evidence={})
+    result = get_label_mapped_to_positive_belief(query_result)
+    compare_dictionaries(expected, result)
+
+
+def test_one_parent_false_five_node_and_model(five_node_and_model):
+    expected = {'x': 0}
+    infer = BeliefPropagation(five_node_and_model)
+    query_result = infer.query(evidence={'u': np.array([1,0])})
+    result = get_label_mapped_to_positive_belief(query_result)
+    compare_dictionaries(expected, result)
+
+
+def test_one_parent_true_five_node_and_model(five_node_and_model):
+    expected = {'x': 0.5}
+    infer = BeliefPropagation(five_node_and_model)
+    query_result = infer.query(evidence={'u': np.array([0,1])})
+    result = get_label_mapped_to_positive_belief(query_result)
+    compare_dictionaries(expected, result)
+
+
+def test_both_parents_true_five_node_and_model(five_node_and_model):
+    expected = {'x': 1, 'y': 1, 'z': 1}
+    infer = BeliefPropagation(five_node_and_model)
+    query_result = infer.query(evidence={'u': np.array([0,1]), 'v': np.array([0,1])})
+    result = get_label_mapped_to_positive_belief(query_result)
+    compare_dictionaries(expected, result)
+
+
+#==============================================================================================
+# Tests of mixed cpd model (all CPDs are OR, except for one AND node with 2 parents)
+
+
+def test_no_evidence_mixed_cpd_model(mixed_cpd_model):
+    expected = {'x': 1-0.5**2, 'z': 0.5*(1-0.5**2)}
+    infer = BeliefPropagation(mixed_cpd_model)
+    query_result = infer.query(evidence={})
+    result = get_label_mapped_to_positive_belief(query_result)
+    compare_dictionaries(expected, result)
+
+
+def test_x_false_w_true_mixed_cpd_model(mixed_cpd_model):
+    expected = {'u': 0, 'v': 0, 'y': 0, 'z': 0}
+    infer = BeliefPropagation(mixed_cpd_model)
+    query_result = infer.query(evidence={'x': np.array([1,0]), 'w': np.array([0,1])})
+    result = get_label_mapped_to_positive_belief(query_result)
+    compare_dictionaries(expected, result)
+
+
+def test_x_true_w_true_mixed_cpd_model(mixed_cpd_model):
+    expected = {'y': 1, 'z': 1}
+    infer = BeliefPropagation(mixed_cpd_model)
+    query_result = infer.query(evidence={'x': np.array([0,1]), 'w': np.array([0,1])})
+    result = get_label_mapped_to_positive_belief(query_result)
+    compare_dictionaries(expected, result)
+
+
+#==============================================================================================
 # Tests of simple BernoulliOr polytree model
 
 def test_no_evidence_simple_model(simple_model):
@@ -253,3 +347,11 @@ def test_negative_evidence_node_62(many_parents_model):
     query_result = infer.query(evidence={'62': np.array([1, 0])})
     result = get_label_mapped_to_positive_belief(query_result)
     compare_dictionaries(expected, result)
+
+
+def test_conflicting_evidence_and_model(many_parents_and_model):
+    """If one of the parents of node 62 is False, then node 62 has to be False."""
+    infer = BeliefPropagation(many_parents_and_model)
+    with pytest.raises(ConflictingEvidenceError) as err:
+        query_result = infer.query(evidence={'62': np.array([0, 1]), '112': np.array([1, 0])})
+    assert "Can't run belief propagation with conflicting evidence" in str(err)