From 10f5c49ea6767f54d59f88eb4064bb4959d14c6b Mon Sep 17 00:00:00 2001
From: Cathy Yeh <cathy@driver.xyz>
Date: Tue, 12 Dec 2017 21:28:26 -0800
Subject: implement explicit factor methods for compute_pi_agg and
 compute_lambda_msg_to_parent in Node

---
 tests/test_belief_propagation.py | 64 +++++++++++++++++++++++++++++++++++++++-
 1 file changed, 63 insertions(+), 1 deletion(-)

(limited to 'tests/test_belief_propagation.py')

diff --git a/tests/test_belief_propagation.py b/tests/test_belief_propagation.py
index 7a77311..1b8c0ac 100644
--- a/tests/test_belief_propagation.py
+++ b/tests/test_belief_propagation.py
@@ -3,10 +3,12 @@ import pytest
 from pytest import approx
 
 from beliefs.inference.belief_propagation import BeliefPropagation, ConflictingEvidenceError
+from beliefs.factors.cpd import TabularCPD
 from beliefs.models.belief_update_node_model import (
     BeliefUpdateNodeModel,
     BernoulliOrNode,
-    BernoulliAndNode
+    BernoulliAndNode,
+    Node
 )
 
 
@@ -89,6 +91,41 @@ def mixed_cpd_model(edges_five_nodes):
                                              'w': w_node})
 
 
+@pytest.fixture(scope='function')
+def custom_cpd_model():
+    """
+    Y-shaped model, with parents ,'u' and 'v' as Or-nodes, 'x' a node with
+    cardinality 3 and custom CPD, 'y' a node with cardinality 2 and custom CPD.
+    """
+    custom_cpd_x = TabularCPD(variable='x',
+                              variable_card=3,
+                              parents=['u', 'v'],
+                              parents_card=[2, 2],
+                              values=[[0.2, 0, 0.3, 0.1],
+                                      [0.4, 1, 0.7, 0.2],
+                                      [0.4, 0, 0, 0.7]],
+                              state_names={'x': ['lo', 'med', 'hi'],
+                                           'u': ['False', 'True'],
+                                           'v': ['False', 'True']})
+    custom_cpd_y = TabularCPD(variable='y',
+                              variable_card=2,
+                              parents=['x'],
+                              parents_card=[3],
+                              values=[[0.3, 0.1, 0],
+                                      [0.7, 0.9, 1]],
+                              state_names={'x': ['lo', 'med', 'hi'],
+                                           'y': ['False', 'True']})
+
+    u_node = BernoulliOrNode(label_id='u', children=['x'], parents=[])
+    v_node = BernoulliOrNode(label_id='v', children=['x'], parents=[])
+    x_node = Node(children=['y'], cpd=custom_cpd_x)
+    y_node = Node(children=[], cpd=custom_cpd_y)
+    return BeliefUpdateNodeModel(nodes_dict={'u': u_node,
+                                             'v': v_node,
+                                             'x': x_node,
+                                             'y': y_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."""
@@ -355,3 +392,28 @@ def test_conflicting_evidence_and_model(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)
+
+
+#==============================================================================================
+# Model with two custom cpds
+
+
+def test_no_evidence_custom_cpd_model(custom_cpd_model):
+    expected = {'x': np.array([0.15, 0.575, 0.275]),
+                'v': np.array([0.5, 0.5]),
+                'u': np.array([0.5, 0.5]),
+                'y': np.array([0.1025, 0.8975])}
+    infer = BeliefPropagation(custom_cpd_model)
+    query_result = infer.query(evidence={})
+    compare_dictionaries(expected, query_result)
+
+
+def test_evidence_custom_cpd_model(custom_cpd_model):
+    """Custom node is observed to be in 'med' state."""
+    expected = {'x': np.array([0., 1., 0.]),
+                'u': np.array([0.60869565, 0.39130435]),
+                'v': np.array([0.47826087, 0.52173913]),
+                'y': np.array([0.1, 0.9])}
+    infer = BeliefPropagation(custom_cpd_model)
+    query_result = infer.query(evidence={'x': np.array([0, 1, 0])})
+    compare_dictionaries(expected, query_result)
-- 
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