From 06626854ca893b44c128ca333fb5623591134746 Mon Sep 17 00:00:00 2001 From: Cathy Yeh Date: Fri, 8 Dec 2017 16:01:37 -0800 Subject: tests for belief propagation with AND and mixed AND and OR nodes --- beliefs/factors/bernoulli_or_cpd.py | 2 +- tests/test_belief_propagation.py | 122 +++++++++++++++++++++++++++++++++--- 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') @@ -52,12 +53,42 @@ def many_parents_model(many_parents_edges): return BeliefUpdateNodeModel.init_from_edges(many_parents_edges, BernoulliOrNode) +@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.""" @@ -118,26 +149,89 @@ 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 @@ -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) -- cgit v1.2.3