class ImportExportTest(unittest.TestCase):
def setUp(self):
# Create BayesNet
self.bn = BayesNet();
# Create Nodes
weather0 = DiscreteNode("Weather0", ["Sun", "Rain"])
weather = DiscreteNode("Weather", ["Sun", "Rain"])
ice_cream_eaten = DiscreteNode("Ice Cream Eaten", [True, False])
# Add nodes
self.bn.add_node(weather0)
self.bn.add_node(weather)
self.bn.add_node(ice_cream_eaten)
# Add edges
self.bn.add_edge(weather, ice_cream_eaten)
self.bn.add_edge(weather0, weather);
# Set probabilities
cpt_weather0 = numpy.array([.6, .4])
weather0.set_probability_table(cpt_weather0, [weather0])
cpt_weather = numpy.array([[.7, .5],
[.3, .5]])
weather.set_probability_table(cpt_weather, [weather0, weather])
ice_cream_eaten.set_probability(.9, [(ice_cream_eaten, True), (weather, "Sun")])
ice_cream_eaten.set_probability(.1, [(ice_cream_eaten, False), (weather, "Sun")])
ice_cream_eaten.set_probability(.2, [(ice_cream_eaten, True), (weather, "Rain")])
ice_cream_eaten.set_probability(.8, [(ice_cream_eaten, False), (weather, "Rain")])
def test_import_export(self):
# write BN
xmlbif = XMLBIF(self.bn, "Test Net")
xmlbif.write("test_out.xmlbif")
# read BN
bn2 = XMLBIF.read("test_out.xmlbif")
for node1 in self.bn.get_nodes():
name_found = False
cpd_equal = False
value_range_equal = False
str_equal = False
pos_equal = False
for node2 in bn2.get_nodes():
# Test node names
if node1.name == node2.name:
name_found = True
cpd_equal = node1.get_cpd == node2.get_cpd
value_range_equal = node1.get_value_range() == node2.get_value_range()
str_equal = str(node1) == str(node2)
pos_equal = node1.pos == node2.pos
self.assertTrue(name_found)
self.assertTrue(cpd_equal)
self.assertTrue(value_range_equal)
self.assertTrue(str_equal)
self.assertTrue(pos_equal)
# remove file
os.remove("test_out.xmlbif")
class NodeAddAndRemoveTestCase(unittest.TestCase):
def setUp(self):
self.bn = BayesNet()
def tearDown(self):
self.bn = None
def test_clear_and_len(self):
self.assertFalse(0 == len(self.bn))
self.assertFalse(0 == self.bn.number_of_nodes())
self.bn.clear()
self.assertEqual(0, len(self.bn))
self.assertEqual(0, self.bn.number_of_nodes())
def test_add_node(self):
self.bn.clear()
n = DiscreteNode("Some Node", [True, False])
self.bn.add_node(n)
self.assertEqual(n, self.bn.get_node("Some Node"))
self.assertTrue(n in self.bn.get_nodes(["Some Node"]))
node_with_same_name = DiscreteNode("Some Node", [True, False])
self.assertRaises(Exception, self.bn.add_node, node_with_same_name)
def test_remove_node(self):
self.bn.clear()
n = DiscreteNode("Some Node to remove", [True, False])
self.bn.add_node(n)
self.bn.remove_node(n)
self.assertFalse(n in self.bn.get_nodes([]))
def test_add_edge(self):
self.bn.clear()
n1 = DiscreteNode("1", [True, False])
n2 = DiscreteNode("2", [True, False])
self.bn.add_node(n1)
self.bn.add_node(n2)
self.bn.add_edge(n1, n2)
self.assertTrue(n1 in self.bn.get_parents(n2))
self.assertTrue(n2 in self.bn.get_children(n1))
def test_remove_edge(self):
self.bn.clear()
n1 = DiscreteNode("1", [True, False])
n2 = DiscreteNode("2", [True, False])
self.bn.add_node(n1)
self.bn.add_node(n2)
self.bn.add_edge(n1, n2)
self.assertEqual([n1], self.bn.get_parents(n2))
self.bn.remove_edge(n1, n2)
self.assertEqual([], self.bn.get_parents(n2))
def test_is_valid(self):
self.bn.clear()
n1 = DiscreteNode("1", [True, False])
n2 = DiscreteNode("2", [True, False])
self.bn.add_node(n1)
self.bn.add_node(n2)
self.bn.add_edge(n1, n2)
self.assertTrue(self.bn.is_valid())
self.bn.add_edge(n1, n1)
self.assertFalse(self.bn.is_valid())
self.bn.remove_edge(n1, n1)
self.assertTrue(self.bn.is_valid())
n3 = DiscreteNode("3", [True, False])
n4 = DiscreteNode("4", [True, False])
self.bn.add_node(n3)
self.bn.add_node(n4)
self.assertTrue(self.bn.is_valid())
self.bn.add_edge(n2, n3)
self.assertTrue(self.bn.is_valid())
self.bn.add_edge(n3, n4)
self.assertTrue(self.bn.is_valid())
self.bn.add_edge(n4, n1)
self.assertFalse(self.bn.is_valid())
class DynamicBayesNet(BayesNet):
''' This is the implementation of a dynamic Bayesian network (also called
temporal Bayesian network).
Definition: DBN is a pair (B0, TwoTBN), where B0 is a BN over X(0),
representing the initial distribution over states, and TwoTBN is a
2-TBN for the process.
See Koller, Friedman - "Probabilistic Graphical Models" (p. 204)
Properties: Markov property, stationary, directed, discrete,
acyclic (within a slice)
'''
def __init__(self):
super(DynamicBayesNet, self).__init__()
self._B0 = BayesNet()
self._twoTBN = TwoTBN()
@property
def B0(self):
''' Get the Bayesian network representing the initial distribution.'''
return self._B0
@B0.setter
def B0(self, value):
''' Set the Bayesian network representing the initial distribution.'''
if isinstance(value, BayesNet):
if not value.is_valid():
raise Exception("BayesNet is not valid.")
self._B0 = value
else:
raise Exception("Can only set 'BayesNet' and its subclasses as " +
"B0 of a DBN.")
@property
def twoTBN(self):
''' Get the 2-time-slice Bayesian network.'''
return self._twoTBN
@twoTBN.setter
def twoTBN(self, value):
''' Set the 2-time-slice Bayesian network.'''
if isinstance(value, TwoTBN):
if not value.is_valid():
raise Exception("BayesNet is not valid.")
self._twoTBN = value
else:
raise Exception("Can only set 'TwoTBN' and its subclasses as " +
"twoTBN of a DBN.")
def is_valid(self):
'''Check if graph structure is valid. And if there is a same-named
inital node in towTBN for every node in BO.
Returns true if graph is directed and acyclic, false otherwiese'''
for node in self._B0.get_nodes():
if not self._twoTBN.has_initial_node_by_name(node.name):
print("Node with name " + str(node.name) +
" not found in TwoTBN!")
return False;
return super(DynamicBayesNet, self).is_valid()
