def get_graph(fname, tar_list=None):
reader = BIFReader(fname)
nodes = reader.get_variables()
candidates = set(nodes)
print(nodes)
edges = reader.get_edges()
n_nodes = len(nodes)
node2id = {node:id for id, node in enumerate(nodes)}
for i, tar in enumerate(tar_list):
# select one as the target
target = tar or np.random.choice(list(candidates))
print('target: ', target)
candidates -= {target}
node2id[target], node2id[nodes[-(i+1)]] = node2id[nodes[-(i+1)]], node2id[target]
print(node2id)
# build adjmat
graph = np.zeros([n_nodes, n_nodes])
for edge in edges:
p = node2id[edge[0]]
n = node2id[edge[1]]
graph[n, p] = 1
print(graph)
return graph
class TestBIFReader(unittest.TestCase):
def setUp(self):
self.reader = BIFReader(string="""
// Bayesian Network in the Interchange Format
// Produced by BayesianNetworks package in JavaBayes
// Output created Sun Nov 02 17:49:49 GMT+00:00 1997
// Bayesian network
network "Dog-Problem" { //5 variables and 5 probability distributions
property "credal-set constant-density-bounded 1.1" ;
}
variable "light-on" { //2 values
type discrete[2] { "true" "false" };
property "position = (218, 195)" ;
}
variable "bowel-problem" { //2 values
type discrete[2] { "true" "false" };
property "position = (335, 99)" ;
}
variable "dog-out" { //2 values
type discrete[2] { "true" "false" };
property "position = (300, 195)" ;
}
variable "hear-bark" { //2 values
type discrete[2] { "true" "false" };
property "position = (296, 268)" ;
}
variable "family-out" { //2 values
type discrete[2] { "true" "false" };
property "position = (257, 99)" ;
}
probability ( "light-on" "family-out" ) { //2 variable(s) and 4 values
table 0.6 0.05 0.4 0.95 ;
}
probability ( "bowel-problem" ) { //1 variable(s) and 2 values
table 0.01 0.99 ;
}
probability ( "dog-out" "bowel-problem" "family-out" ) { //3 variable(s) and 8 values
table 0.99 0.97 0.9 0.3 0.01 0.03 0.1 0.7 ;
}
probability ( "hear-bark" "dog-out" ) { //2 variable(s) and 4 values
table 0.7 0.01 0.3 0.99 ;
}
probability ( "family-out" ) { //1 variable(s) and 2 values
table 0.15 0.85 ;
}
""")
def test_network_name(self):
name_expected = 'Dog-Problem'
self.assertEqual(self.reader.network_name, name_expected)
def test_get_variables(self):
var_expected = [
'light-on', 'bowel-problem', 'dog-out', 'hear-bark', 'family-out'
]
self.assertListEqual(self.reader.get_variables(), var_expected)
def test_states(self):
states_expected = {
'bowel-problem': ['true', 'false'],
'dog-out': ['true', 'false'],
'family-out': ['true', 'false'],
'hear-bark': ['true', 'false'],
'light-on': ['true', 'false']
}
states = self.reader.get_states()
for variable in states_expected:
self.assertListEqual(states_expected[variable], states[variable])
def test_get_property(self):
property_expected = {
'bowel-problem': ['position = (335, 99)'],
'dog-out': ['position = (300, 195)'],
'family-out': ['position = (257, 99)'],
'hear-bark': ['position = (296, 268)'],
'light-on': ['position = (218, 195)']
}
prop = self.reader.get_property()
for variable in property_expected:
self.assertListEqual(property_expected[variable], prop[variable])
def test_get_cpd(self):
cpd_expected = {
'bowel-problem': np.array([[0.01], [0.99]]),
'dog-out': np.array([[0.99, 0.97, 0.9, 0.3],
[0.01, 0.03, 0.1, 0.7]]),
'family-out': np.array([[0.15], [0.85]]),
'hear-bark': np.array([[0.7, 0.01], [0.3, 0.99]]),
'light-on': np.array([[0.6, 0.05], [0.4, 0.95]])
}
cpd = self.reader.variable_cpds
for variable in cpd_expected:
np_test.assert_array_equal(cpd_expected[variable], cpd[variable])
def test_get_parents(self):
parents_expected = {
'bowel-problem': [],
'dog-out': ['bowel-problem', 'family-out'],
'family-out': [],
'hear-bark': ['dog-out'],
'light-on': ['family-out']
}
parents = self.reader.get_parents()
for variable in parents_expected:
self.assertListEqual(parents_expected[variable], parents[variable])
def test_get_edges(self):
edges_expected = [['family-out', 'dog-out'],
['bowel-problem', 'dog-out'],
['family-out', 'light-on'], ['dog-out', 'hear-bark']]
self.assertListEqual(sorted(self.reader.variable_edges),
sorted(edges_expected))
def test_get_model(self):
edges_expected = [('family-out', 'dog-out'),
('bowel-problem', 'dog-out'),
('family-out', 'light-on'), ('dog-out', 'hear-bark')]
nodes_expected = [
'bowel-problem', 'hear-bark', 'light-on', 'dog-out', 'family-out'
]
edge_expected = {
'bowel-problem': {
'dog-out': {}
},
'dog-out': {
'hear-bark': {}
},
'family-out': {
'dog-out': {},
'light-on': {}
},
'hear-bark': {},
'light-on': {}
}
node_expected = {
'bowel-problem': {
'position': '(335, 99)'
},
'dog-out': {
'position': '(300, 195)'
},
'family-out': {
'position': '(257, 99)'
},
'hear-bark': {
'position': '(296, 268)'
},
'light-on': {
'position': '(218, 195)'
}
}
cpds_expected = [
np.array([[0.01], [0.99]]),
np.array([[0.99, 0.97, 0.9, 0.3], [0.01, 0.03, 0.1, 0.7]]),
np.array([[0.15], [0.85]]),
np.array([[0.7, 0.01], [0.3, 0.99]]),
np.array([[0.6, 0.05], [0.4, 0.95]])
]
model = self.reader.get_model()
for cpd_index in range(0, len(cpds_expected)):
np_test.assert_array_equal(model.get_cpds()[cpd_index].get_cpd(),
cpds_expected[cpd_index])
self.assertDictEqual(model.node, node_expected)
self.assertDictEqual(model.edge, edge_expected)
self.assertListEqual(sorted(model.nodes()), sorted(nodes_expected))
self.assertListEqual(sorted(model.edges()), sorted(edges_expected))
def tearDown(self):
del self.reader
class TestBIFReader(unittest.TestCase):
def setUp(self):
self.reader = BIFReader(string="""
// Bayesian Network in the Interchange Format
// Produced by BayesianNetworks package in JavaBayes
// Output created Sun Nov 02 17:49:49 GMT+00:00 1997
// Bayesian network
network "Dog-Problem" { //5 variables and 5 probability distributions
property "credal-set constant-density-bounded 1.1" ;
}
variable "light-on" { //2 values
type discrete[2] { "true" "false" };
property "position = (218, 195)" ;
}
variable "bowel-problem" { //2 values
type discrete[2] { "true" "false" };
property "position = (335, 99)" ;
}
variable "dog-out" { //2 values
type discrete[2] { "true" "false" };
property "position = (300, 195)" ;
}
variable "hear-bark" { //2 values
type discrete[2] { "true" "false" };
property "position = (296, 268)" ;
}
variable "family-out" { //2 values
type discrete[2] { "true" "false" };
property "position = (257, 99)" ;
}
probability ( "light-on" "family-out" ) { //2 variable(s) and 4 values
(true) 0.6 0.4 ;
(false) 0.05 0.95 ;
}
probability ( "bowel-problem" ) { //1 variable(s) and 2 values
table 0.01 0.99 ;
}
probability ( "dog-out" "bowel-problem" "family-out" ) { //3 variable(s) and 8 values
table 0.99 0.97 0.9 0.3 0.01 0.03 0.1 0.7 ;
}
probability ( "hear-bark" "dog-out" ) { //2 variable(s) and 4 values
table 0.7 0.01 0.3 0.99 ;
}
probability ( "family-out" ) { //1 variable(s) and 2 values
table 0.15 0.85 ;
}
""")
def test_network_name(self):
name_expected = 'Dog-Problem'
self.assertEqual(self.reader.network_name, name_expected)
def test_get_variables(self):
var_expected = ['light-on', 'bowel-problem', 'dog-out',
'hear-bark', 'family-out']
self.assertListEqual(self.reader.get_variables(), var_expected)
def test_states(self):
states_expected = {'bowel-problem': ['true', 'false'],
'dog-out': ['true', 'false'],
'family-out': ['true', 'false'],
'hear-bark': ['true', 'false'],
'light-on': ['true', 'false']}
states = self.reader.get_states()
for variable in states_expected:
self.assertListEqual(states_expected[variable], states[variable])
def test_get_property(self):
property_expected = {'bowel-problem': ['position = (335, 99)'],
'dog-out': ['position = (300, 195)'],
'family-out': ['position = (257, 99)'],
'hear-bark': ['position = (296, 268)'],
'light-on': ['position = (218, 195)']}
prop = self.reader.get_property()
for variable in property_expected:
self.assertListEqual(property_expected[variable],
prop[variable])
def test_get_values(self):
cpd_expected = {'bowel-problem': np.array([[0.01],
[0.99]]),
'dog-out': np.array([[0.99, 0.97, 0.9, 0.3],
[0.01, 0.03, 0.1, 0.7]]),
'family-out': np.array([[0.15],
[0.85]]),
'hear-bark': np.array([[0.7, 0.01],
[0.3, 0.99]]),
'light-on': np.array([[0.6, 0.05],
[0.4, 0.95]])}
cpd = self.reader.variable_cpds
for variable in cpd_expected:
np_test.assert_array_equal(cpd_expected[variable],
cpd[variable])
def test_get_parents(self):
parents_expected = {'bowel-problem': [],
'dog-out': ['bowel-problem', 'family-out'],
'family-out': [],
'hear-bark': ['dog-out'],
'light-on': ['family-out']}
parents = self.reader.get_parents()
for variable in parents_expected:
self.assertListEqual(parents_expected[variable],
parents[variable])
def test_get_edges(self):
edges_expected = [['family-out', 'dog-out'],
['bowel-problem', 'dog-out'],
['family-out', 'light-on'],
['dog-out', 'hear-bark']]
self.assertListEqual(sorted(self.reader.variable_edges),
sorted(edges_expected))
def test_get_model(self):
edges_expected = [('family-out', 'dog-out'),
('bowel-problem', 'dog-out'),
('family-out', 'light-on'),
('dog-out', 'hear-bark')]
nodes_expected = ['bowel-problem', 'hear-bark', 'light-on',
'dog-out', 'family-out']
edge_expected = {'bowel-problem': {'dog-out': {'weight': None}},
'dog-out': {'hear-bark': {'weight': None}},
'family-out': {'dog-out': {'weight': None},
'light-on': {'weight': None}},
'hear-bark': {},
'light-on': {}}
node_expected = {'bowel-problem': {'weight': None,
'position': '(335, 99)'},
'dog-out': {'weight': None,
'position': '(300, 195)'},
'family-out': {'weight': None,
'position': '(257, 99)'},
'hear-bark': {'weight': None,
'position': '(296, 268)'},
'light-on': {'weight': None,
'position': '(218, 195)'}}
cpds_expected = [np.array([[0.01],
[0.99]]),
np.array([[0.99, 0.97, 0.9, 0.3],
[0.01, 0.03, 0.1, 0.7]]),
np.array([[0.15],
[0.85]]),
np.array([[0.7, 0.01],
[0.3, 0.99]]),
np.array([[0.6, 0.05],
[0.4, 0.95]])]
model = self.reader.get_model()
for cpd_index in range(0, len(cpds_expected)):
np_test.assert_array_equal(model.get_cpds()[cpd_index].get_values(),
cpds_expected[cpd_index])
self.assertDictEqual(model.node, node_expected)
self.assertDictEqual(model.edge, edge_expected)
self.assertListEqual(sorted(model.nodes()), sorted(nodes_expected))
self.assertListEqual(sorted(model.edges()), sorted(edges_expected))
def tearDown(self):
del self.reader
