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Diffstat (limited to 'tests/test_belief_propagation.py')
| -rw-r--r-- | tests/test_belief_propagation.py | 255 |
1 files changed, 255 insertions, 0 deletions
diff --git a/tests/test_belief_propagation.py b/tests/test_belief_propagation.py new file mode 100644 index 0000000..5c5a612 --- /dev/null +++ b/tests/test_belief_propagation.py @@ -0,0 +1,255 @@ +import numpy as np +import pytest +from pytest import approx + +from beliefs.inference.belief_propagation import BeliefPropagation, ConflictingEvidenceError +from beliefs.models.belief_update_node_model import ( + BeliefUpdateNodeModel, + BernoulliOrNode +) + + +@pytest.fixture(scope='module') +def edges_four_nodes(): + """Edges define a polytree with 4 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 + + +@pytest.fixture(scope='module') +def simple_edges(): + """Edges define a polytree with 15 nodes.""" + edges = [('1', '3'), ('2', '3'), ('3', '5'), ('4', '5'), ('5', '10'), + ('5', '9'), ('6', '8'), ('7', '8'), ('8', '9'), ('9', '11'), + ('9', 'x'), ('14', 'x'), ('x', '12'), ('x', '13')] + return edges + + +@pytest.fixture(scope='module') +def many_parents_edges(): + """Node 62 has 18 parents and no children.""" + edges = [('96', '62'), ('80', '62'), ('98', '62'), + ('100', '62'), ('86', '62'), ('102', '62'), ('104', '62'), + ('64', '62'), ('106', '62'), ('108', '62'), ('110', '62'), + ('112', '62'), ('114', '62'), ('116', '62'), ('118', '62'), + ('122', '62'), ('70', '62'), ('94', '62')] + return edges + + +@pytest.fixture(scope='function') +def four_node_model(edges_four_nodes): + return BeliefUpdateNodeModel.init_from_edges(edges_four_nodes, BernoulliOrNode) + + +@pytest.fixture(scope='function') +def simple_model(simple_edges): + return BeliefUpdateNodeModel.init_from_edges(simple_edges, BernoulliOrNode) + + +@pytest.fixture(scope='function') +def many_parents_model(many_parents_edges): + return BeliefUpdateNodeModel.init_from_edges(many_parents_edges, BernoulliOrNode) + + +@pytest.fixture(scope='function') +def one_node_model(): + a_node = BernoulliOrNode(label_id='x', children=[], parents=[]) + return BeliefUpdateNodeModel(nodes_dict={'x': a_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.""" + return {label_id: belief[1] for label_id, belief in query_result.items()} + + +def compare_dictionaries(expected, observed): + for key, expected_value in expected.items(): + observed_value = observed.get(key) + if observed_value is None: + raise KeyError("Expected key {} not in observed.") + assert observed_value == approx(expected_value), \ + "Expected {} but got {}".format(expected_value, observed_value) + + +#============================================================================================== +# Tests of single Bernoulli node model + +def test_no_evidence_one_node_model(one_node_model): + expected = {'x': 0.5} + infer = BeliefPropagation(one_node_model) + query_result = infer.query(evidence={}) + result = get_label_mapped_to_positive_belief(query_result) + compare_dictionaries(expected, result) + + +def test_virtual_evidence_one_node_model(one_node_model): + """Curator thinks YES is 10x more likely than NO based on virtual evidence.""" + expected = {'x': 5/(0.5+5)} + infer = BeliefPropagation(one_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) + + +def test_MAYBE_default_evidence_one_node_model(one_node_model): + expected = {'x': 0.5} + infer = BeliefPropagation(one_node_model) + query_result = infer.query(evidence={'x': np.array([0.5, 0.5])}) + result = get_label_mapped_to_positive_belief(query_result) + compare_dictionaries(expected, result) + + +def test_YES_evidence_one_node_model(one_node_model): + expected = {'x': 1} + infer = BeliefPropagation(one_node_model) + query_result = infer.query(evidence={'x': np.array([0, 1])}) + result = get_label_mapped_to_positive_belief(query_result) + compare_dictionaries(expected, result) + + +def test_NO_evidence_one_node_model(one_node_model): + expected = {'x': 0} + infer = BeliefPropagation(one_node_model) + query_result = infer.query(evidence={'x': np.array([1, 0])}) + result = get_label_mapped_to_positive_belief(query_result) + compare_dictionaries(expected, result) + + +#============================================================================================== +# Tests of 4-node, 4-edge model + +def test_no_evidence_four_node_model(four_node_model): + expected = {'x': 1-0.5**2} + infer = BeliefPropagation(four_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): + """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) + 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 simple BernoulliOr polytree model + +def test_no_evidence_simple_model(simple_model): + expected = {'x': 0.984375, '14': 0.5, '7': 0.5, '2': 0.5, '3': + 0.75, '13': 0.984375, '6': 0.5, '4': 0.5, '8': 0.75, '10': 0.875, + '1': 0.5, '9': 0.96875, '12': 0.984375, '5': 0.875, '11': 0.96875} + infer = BeliefPropagation(simple_model) + query_result = infer.query(evidence={}) + result = get_label_mapped_to_positive_belief(query_result) + compare_dictionaries(expected, result) + + +def test_belief_propagation_no_modify_model_inplace(simple_model): + assert not simple_model.all_nodes_are_fully_initialized + infer = BeliefPropagation(simple_model, inplace=False) + _ = infer.query(evidence={}) + # after belief propagation, model node values should be unchanged + assert not simple_model.all_nodes_are_fully_initialized + + +def test_belief_propagation_modify_model_inplace(simple_model): + assert not simple_model.all_nodes_are_fully_initialized + expected = {'x': 0.984375, '14': 0.5, '7': 0.5, '2': 0.5, '3': + 0.75, '13': 0.984375, '6': 0.5, '4': 0.5, '8': 0.75, '10': 0.875, + '1': 0.5, '9': 0.96875, '12': 0.984375, '5': 0.875, '11': 0.96875} + infer = BeliefPropagation(simple_model, inplace=True) + _ = infer.query(evidence={}) + + assert simple_model.all_nodes_are_fully_initialized + beliefs_from_model = {node_id: node.belief[1] for + node_id, node in simple_model.nodes_dict.items()} + compare_dictionaries(expected, beliefs_from_model) + + +def test_positive_evidence_node_13(simple_model): + expected = {'6': 0.50793650793650791, '3': 0.76190476190476186, + '9': 0.98412698412698407, '8': 0.76190476190476186, + 'x': 1.0, '4': 0.50793650793650791, '11': 0.98412698412698407, + '1': 0.50793650793650791, '5': 0.88888888888888884, + '2': 0.50793650793650791, '12': 1.0, + '14': 0.50793650793650791, '13': 1, + '10': 0.88888888888888884, '7': 0.50793650793650791} + infer = BeliefPropagation(simple_model) + query_result = infer.query(evidence={'13': np.array([0, 1])}) + result = get_label_mapped_to_positive_belief(query_result) + compare_dictionaries(expected, result) + + +def test_positive_evidence_node_5(simple_model): + expected = {'1': 0.5714285714285714, '5': 1, '3': + 0.8571428571428571, '10': 1.0, '8': 0.75, '2': 0.5714285714285714, + '4': 0.5714285714285714, '6': 0.5, '7': 0.5, '14': 0.5, '12': 1.0, + '13': 1.0, '11': 1.0, '9': 1.0, 'x': 1.0} + infer = BeliefPropagation(simple_model) + query_result = infer.query(evidence={'5': np.array([0, 1])}) + result = get_label_mapped_to_positive_belief(query_result) + compare_dictionaries(expected, result) + + +def test_positive_evidence_node_5_negative_evidence_node_14(simple_model): + expected = {'6': 0.5, '7': 0.5, '9': 1.0, '3': 0.8571428571428571, + '1': 0.57142857142857151, '12': 1.0, 'x': 1.0, '11': 1.0, '14': + 0.0, '2': 0.57142857142857151, '4': 0.5714285714285714, '5': 1.0, + '10': 1.0, '13': 1.0, '8': 0.75} + infer = BeliefPropagation(simple_model) + query_result = infer.query(evidence={'5': np.array([0, 1]), '14': np.array([1, 0])}) + result = get_label_mapped_to_positive_belief(query_result) + compare_dictionaries(expected, result) + + +def test_conflicting_evidence(simple_model): + infer = BeliefPropagation(simple_model) + with pytest.raises(ConflictingEvidenceError) as err: + query_result = infer.query(evidence={'x': np.array([1, 0]), '5': np.array([0, 1])}) + assert "Can't run belief propagation with conflicting evidence" in str(err) + + +#============================================================================================== +# Tests of model with 18 parents sharing a single child + +def test_no_evidence_many_parents_model(many_parents_model): + expected = {'64': 0.5, '86': 0.5, '62': 0.99999618530273438, + '116': 0.5, '100': 0.5, '108': 0.5, '122': 0.5, '114': 0.5, '98': + 0.5, '106': 0.5, '94': 0.5, '80': 0.5, '102': 0.5, '70': 0.5, + '118': 0.5, '96': 0.5, '104': 0.5, '110': 0.5, '112': 0.5} + infer = BeliefPropagation(many_parents_model) + query_result = infer.query(evidence={}) + result = get_label_mapped_to_positive_belief(query_result) + compare_dictionaries(expected, result) + + +def test_positive_evidence_node_112(many_parents_model): + """If a single parent (112) is True, then (62) has to be True.""" + expected = {'64': 0.5, '86': 0.5, '62': 1.0, '116': 0.5, '100': + 0.5, '108': 0.5, '122': 0.5, '114': 0.5, '98': 0.5, + '106': 0.5, '94': 0.5, '80': 0.5, '102': 0.5, '70': + 0.5, '118': 0.5, '96': 0.5, '104': 0.5, '110': 0.5, + '112': 1.0} + infer = BeliefPropagation(many_parents_model) + query_result = infer.query(evidence={'112': np.array([0, 1])}) + result = get_label_mapped_to_positive_belief(query_result) + compare_dictionaries(expected, result) + + +def test_negative_evidence_node_62(many_parents_model): + """If node 62 is False, then all of its parents must be False.""" + expected = {'64': 0, '86': 0, '62': 0, '116': 0, '100': 0, '108': + 0, '122': 0, '114': 0, '98': 0, '106': 0, '94': 0, + '80': 0, '102': 0, '70': 0, '118': 0, '96': 0, '104': + 0, '110': 0, '112': 0} + infer = BeliefPropagation(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) |