class TestDynamicBayesianNetworkCreation(unittest.TestCase):
def setUp(self):
self.network = DynamicBayesianNetwork()
def test_add_single_node(self):
self.network.add_node('a')
self.assertListEqual(self.network.nodes(), ['a'])
def test_add_multiple_nodes(self):
self.network.add_nodes_from(['a', 'b', 'c'])
self.assertListEqual(sorted(self.network.nodes()), ['a', 'b', 'c'])
def test_add_single_edge_with_timeslice(self):
self.network.add_edge(('a', 0), ('b', 0))
self.assertListEqual(sorted(self.network.edges()), [(('a', 0), ('b', 0)), (('a', 1), ('b', 1))])
self.assertListEqual(sorted(self.network.nodes()), ['a', 'b'])
def test_add_edge_with_different_number_timeslice(self):
self.network.add_edge(('a', 2), ('b', 2))
self.assertListEqual(sorted(self.network.edges()), [(('a', 0), ('b', 0)), (('a', 1), ('b', 1))])
def test_add_edge_going_backward(self):
self.assertRaises(NotImplementedError, self.network.add_edge, ('a', 1), ('b', 0))
def test_add_edge_with_farther_timeslice(self):
self.assertRaises(ValueError, self.network.add_edge, ('a', 2), ('b', 4))
def test_add_edge_with_self_loop(self):
self.assertRaises(ValueError, self.network.add_edge, ('a', 0), ('a', 0))
def test_add_edge_with_varying_length(self):
self.assertRaises(ValueError, self.network.add_edge, ('a', 1, 1), ('b', 2))
self.assertRaises(ValueError, self.network.add_edge, ('b', 2), ('a', 2, 3))
def test_add_edge_with_closed_path(self):
self.assertRaises(ValueError, self.network.add_edges_from,
[(('a', 0), ('b', 0)), (('b', 0), ('c', 0)), (('c', 0), ('a', 0))])
def test_add_single_edge_without_timeslice(self):
self.assertRaises(ValueError, self.network.add_edge, 'a', 'b')
def test_add_single_edge_with_incorrect_timeslice(self):
self.assertRaises(ValueError, self.network.add_edge, ('a', 'b'), ('b', 'c'))
def test_add_multiple_edges(self):
self.network.add_edges_from([(('a', 0), ('b', 0)), (('a', 0), ('a', 1)), (('b', 0), ('b', 1))])
self.assertListEqual(sorted(self.network.edges()),
[(('a', 0), ('a', 1)), (('a', 0), ('b', 0)), (('a', 1), ('b', 1)), (('b', 0), ('b', 1))])
def tearDown(self):
del self.network
class TestDynamicBayesianNetworkCreation(unittest.TestCase):
def setUp(self):
self.network = DynamicBayesianNetwork()
def test_add_single_node(self):
self.network.add_node('a')
self.assertListEqual(self.network.nodes(), ['a'])
def test_add_multiple_nodes(self):
self.network.add_nodes_from(['a', 'b', 'c'])
self.assertListEqual(sorted(self.network.nodes()), ['a', 'b', 'c'])
def test_add_single_edge_with_timeslice(self):
self.network.add_edge(('a', 0), ('b', 0))
self.assertListEqual(sorted(self.network.edges()), [(('a', 0), ('b', 0)), (('a', 1), ('b', 1))])
self.assertListEqual(sorted(self.network.nodes()), ['a', 'b'])
def test_add_edge_with_different_number_timeslice(self):
self.network.add_edge(('a', 2), ('b', 2))
self.assertListEqual(sorted(self.network.edges()), [(('a', 0), ('b', 0)), (('a', 1), ('b', 1))])
def test_add_edge_going_backward(self):
self.assertRaises(NotImplementedError, self.network.add_edge, ('a', 1), ('b', 0))
def test_add_edge_with_farther_timeslice(self):
self.assertRaises(ValueError, self.network.add_edge, ('a', 2), ('b', 4))
def test_add_edge_with_self_loop(self):
self.assertRaises(ValueError, self.network.add_edge, ('a', 0), ('a', 0))
def test_add_edge_with_varying_length(self):
self.assertRaises(ValueError, self.network.add_edge, ('a', 1, 1), ('b', 2))
self.assertRaises(ValueError, self.network.add_edge, ('b', 2), ('a', 2, 3))
def test_add_edge_with_closed_path(self):
self.assertRaises(ValueError, self.network.add_edges_from,
[(('a', 0), ('b', 0)), (('b', 0), ('c', 0)), (('c', 0), ('a', 0))])
def test_add_single_edge_without_timeslice(self):
self.assertRaises(ValueError, self.network.add_edge, 'a', 'b')
def test_add_single_edge_with_incorrect_timeslice(self):
self.assertRaises(ValueError, self.network.add_edge, ('a', 'b'), ('b', 'c'))
def test_add_multiple_edges(self):
self.network.add_edges_from([(('a', 0), ('b', 0)), (('a', 0), ('a', 1)), (('b', 0), ('b', 1))])
self.assertListEqual(sorted(self.network.edges()),
[(('a', 0), ('a', 1)), (('a', 0), ('b', 0)), (('a', 1), ('b', 1)), (('b', 0), ('b', 1))])
def tearDown(self):
del self.network
class TestDynamicBayesianNetworkMethods(unittest.TestCase):
def setUp(self):
self.network = DynamicBayesianNetwork()
self.grade_cpd = TabularCPD(
("G", 0),
3,
values=[[0.3, 0.05, 0.8, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[("D", 0), ("I", 0)],
evidence_card=[2, 2],
)
self.d_i_cpd = TabularCPD(
("D", 1),
2,
values=[[0.6, 0.3], [0.4, 0.7]],
evidence=[("D", 0)],
evidence_card=[2],
)
self.diff_cpd = TabularCPD(("D", 0), 2, values=[[0.6, 0.4]])
self.intel_cpd = TabularCPD(("I", 0), 2, values=[[0.7, 0.3]])
self.i_i_cpd = TabularCPD(
("I", 1),
2,
values=[[0.5, 0.4], [0.5, 0.6]],
evidence=[("I", 0)],
evidence_card=[2],
)
self.grade_1_cpd = TabularCPD(
("G", 1),
3,
values=[[0.3, 0.05, 0.8, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[("D", 1), ("I", 1)],
evidence_card=[2, 2],
)
def test_get_intra_and_inter_edges(self):
self.network.add_edges_from([(("a", 0), ("b", 0)),
(("a", 0), ("a", 1)),
(("b", 0), ("b", 1))])
self.assertListEqual(sorted(self.network.get_intra_edges()),
[(("a", 0), ("b", 0))])
self.assertListEqual(sorted(self.network.get_intra_edges(1)),
[(("a", 1), ("b", 1))])
self.assertRaises(ValueError, self.network.get_intra_edges, -1)
self.assertRaises(ValueError, self.network.get_intra_edges, "-")
self.assertListEqual(
sorted(self.network.get_inter_edges()),
[(("a", 0), ("a", 1)), (("b", 0), ("b", 1))],
)
def test_get_interface_nodes(self):
self.network.add_edges_from([
(("D", 0), ("G", 0)),
(("I", 0), ("G", 0)),
(("D", 0), ("D", 1)),
(("I", 0), ("I", 1)),
])
self.assertListEqual(sorted(self.network.get_interface_nodes()),
[("D", 0), ("I", 0)])
self.assertRaises(ValueError, self.network.get_interface_nodes, -1)
self.assertRaises(ValueError, self.network.get_interface_nodes, "-")
def test_get_slice_nodes(self):
self.network.add_edges_from([
(("D", 0), ("G", 0)),
(("I", 0), ("G", 0)),
(("D", 0), ("D", 1)),
(("I", 0), ("I", 1)),
])
self.assertListEqual(sorted(self.network.get_slice_nodes()),
[("D", 0), ("G", 0), ("I", 0)])
self.assertListEqual(sorted(self.network.get_slice_nodes(1)),
[("D", 1), ("G", 1), ("I", 1)])
self.assertRaises(ValueError, self.network.get_slice_nodes, -1)
self.assertRaises(ValueError, self.network.get_slice_nodes, "-")
def test_add_single_cpds(self):
self.network.add_edges_from([(("D", 0), ("G", 0)),
(("I", 0), ("G", 0))])
self.network.add_cpds(self.grade_cpd)
self.assertListEqual(self.network.get_cpds(), [self.grade_cpd])
def test_get_cpds(self):
self.network.add_edges_from([
(("D", 0), ("G", 0)),
(("I", 0), ("G", 0)),
(("D", 0), ("D", 1)),
(("I", 0), ("I", 1)),
])
self.network.add_cpds(self.grade_cpd, self.d_i_cpd, self.diff_cpd,
self.intel_cpd, self.i_i_cpd)
self.network.initialize_initial_state()
self.assertEqual(
set(self.network.get_cpds()),
set([self.diff_cpd, self.intel_cpd, self.grade_cpd]),
)
self.assertEqual(
{cpd.variable
for cpd in self.network.get_cpds(time_slice=1)},
{("D", 1), ("I", 1), ("G", 1)},
)
def test_add_multiple_cpds(self):
self.network.add_edges_from([
(("D", 0), ("G", 0)),
(("I", 0), ("G", 0)),
(("D", 0), ("D", 1)),
(("I", 0), ("I", 1)),
])
self.network.add_cpds(self.grade_cpd, self.d_i_cpd, self.diff_cpd,
self.intel_cpd, self.i_i_cpd)
self.assertEqual(self.network.get_cpds(("G", 0)).variable, ("G", 0))
self.assertEqual(self.network.get_cpds(("D", 1)).variable, ("D", 1))
self.assertEqual(self.network.get_cpds(("D", 0)).variable, ("D", 0))
self.assertEqual(self.network.get_cpds(("I", 0)).variable, ("I", 0))
self.assertEqual(self.network.get_cpds(("I", 1)).variable, ("I", 1))
def test_initialize_initial_state(self):
self.network.add_nodes_from(["D", "G", "I", "S", "L"])
self.network.add_edges_from([
(("D", 0), ("G", 0)),
(("I", 0), ("G", 0)),
(("D", 0), ("D", 1)),
(("I", 0), ("I", 1)),
])
self.network.add_cpds(self.grade_cpd, self.d_i_cpd, self.diff_cpd,
self.intel_cpd, self.i_i_cpd)
self.network.initialize_initial_state()
self.assertEqual(len(self.network.cpds), 6)
self.assertEqual(self.network.get_cpds(("G", 1)).variable, ("G", 1))
def test_moralize(self):
self.network.add_edges_from(([(("D", 0), ("G", 0)),
(("I", 0), ("G", 0))]))
moral_graph = self.network.moralize()
self.assertListEqual(
hf.recursive_sorted(moral_graph.edges()),
[
[("D", 0), ("G", 0)],
[("D", 0), ("I", 0)],
[("D", 1), ("G", 1)],
[("D", 1), ("I", 1)],
[("G", 0), ("I", 0)],
[("G", 1), ("I", 1)],
],
)
def test_copy(self):
self.network.add_edges_from([
(("D", 0), ("G", 0)),
(("I", 0), ("G", 0)),
(("D", 0), ("D", 1)),
(("I", 0), ("I", 1)),
])
cpd = TabularCPD(
("G", 0),
3,
values=[[0.3, 0.05, 0.8, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[("D", 0), ("I", 0)],
evidence_card=[2, 2],
)
self.network.add_cpds(cpd)
copy = self.network.copy()
self.assertIsInstance(copy, DynamicBayesianNetwork)
self.assertListEqual(sorted(self.network._nodes()),
sorted(copy._nodes()))
self.assertListEqual(sorted(self.network.edges()),
sorted(copy.edges()))
self.assertListEqual(self.network.get_cpds(), copy.get_cpds())
self.assertListEqual(sorted(self.network.get_intra_edges()),
sorted(copy.get_intra_edges()))
self.assertListEqual(sorted(self.network.get_inter_edges()),
sorted(copy.get_inter_edges()))
self.assertListEqual(sorted(self.network.get_slice_nodes()),
sorted(copy.get_slice_nodes()))
copy.cpds[0].values = np.array([[0.4, 0.05, 0.3, 0.5],
[0.3, 0.25, 0.5, 0.3],
[0.3, 0.7, 0.2, 0.2]])
self.assertNotEqual(self.network.get_cpds(), copy.get_cpds())
self.network.add_cpds(self.i_i_cpd, self.d_i_cpd)
copy.add_cpds(self.diff_cpd, self.intel_cpd)
self.network.add_node("A")
copy.add_node("Z")
self.network.add_edge(("A", 0), ("D", 0))
copy.add_edge(("Z", 0), ("D", 0))
self.assertNotEqual(sorted(self.network._nodes()),
sorted(copy._nodes()))
self.assertNotEqual(sorted(self.network.edges()), sorted(copy.edges()))
self.assertNotEqual(self.network.get_cpds(), copy.get_cpds())
self.assertNotEqual(sorted(self.network.get_intra_edges()),
sorted(copy.get_intra_edges()))
self.assertListEqual(sorted(self.network.get_inter_edges()),
sorted(copy.get_inter_edges()))
self.assertNotEqual(sorted(self.network.get_slice_nodes()),
sorted(copy.get_slice_nodes()))
self.network.add_edge(("A", 0), ("D", 1))
copy.add_edge(("Z", 0), ("D", 1))
self.assertNotEqual(sorted(self.network.get_inter_edges()),
sorted(copy.get_inter_edges()))
def tearDown(self):
del self.network
class TestDynamicBayesianNetworkMethods(unittest.TestCase):
def setUp(self):
self.network = DynamicBayesianNetwork()
self.grade_cpd = TabularCPD(('G', 0), 3, values=[[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[('D', 0), ('I', 0)], evidence_card=[2, 2])
self.d_i_cpd = TabularCPD(('D', 1), 2, values=[[0.6, 0.3], [0.4, 0.7]],
evidence=[('D', 0)], evidence_card=2)
self.diff_cpd = TabularCPD(('D', 0), 2, values=[[0.6, 0.4]])
self.intel_cpd = TabularCPD(('I', 0), 2, values=[[0.7, 0.3]])
self.i_i_cpd = TabularCPD(('I', 1), 2, values=[[0.5, 0.4], [0.5, 0.6]],
evidence=[('I', 0)], evidence_card=[2])
self.grade_1_cpd = TabularCPD(('G', 1), 3, values=[[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[('D', 1), ('I', 1)], evidence_card=[2, 2])
def test_get_intra_and_inter_edges(self):
self.network.add_edges_from([(('a', 0), ('b', 0)), (('a', 0), ('a', 1)), (('b', 0), ('b', 1))])
self.assertListEqual(sorted(self.network.get_intra_edges()), [(('a', 0), ('b', 0))])
self.assertListEqual(sorted(self.network.get_intra_edges(1)), [(('a', 1), ('b', 1))])
self.assertRaises(ValueError, self.network.get_intra_edges, -1)
self.assertRaises(ValueError, self.network.get_intra_edges, '-')
self.assertListEqual(sorted(self.network.get_inter_edges()), [(('a', 0), ('a', 1)), (('b', 0), ('b', 1))])
def test_get_interface_nodes(self):
self.network.add_edges_from(
[(('D', 0), ('G', 0)), (('I', 0), ('G', 0)), (('D', 0), ('D', 1)), (('I', 0), ('I', 1))])
self.assertListEqual(sorted(self.network.get_interface_nodes()), [('D', 0), ('I', 0)])
self.assertRaises(ValueError, self.network.get_interface_nodes, -1)
self.assertRaises(ValueError, self.network.get_interface_nodes, '-')
def test_get_slice_nodes(self):
self.network.add_edges_from(
[(('D', 0), ('G', 0)), (('I', 0), ('G', 0)), (('D', 0), ('D', 1)), (('I', 0), ('I', 1))])
self.assertListEqual(sorted(self.network.get_slice_nodes()), [('D', 0), ('G', 0), ('I', 0)])
self.assertListEqual(sorted(self.network.get_slice_nodes(1)), [('D', 1), ('G', 1), ('I', 1)])
self.assertRaises(ValueError, self.network.get_slice_nodes, -1)
self.assertRaises(ValueError, self.network.get_slice_nodes, '-')
def test_add_single_cpds(self):
self.network.add_edges_from([(('D', 0), ('G', 0)), (('I', 0), ('G', 0))])
self.network.add_cpds(self.grade_cpd)
self.assertListEqual(self.network.get_cpds(), [self.grade_cpd])
def test_get_cpds(self):
self.network.add_edges_from(
[(('D', 0), ('G', 0)), (('I', 0), ('G', 0)), (('D', 0), ('D', 1)), (('I', 0), ('I', 1))])
self.network.add_cpds(self.grade_cpd, self.d_i_cpd, self.diff_cpd, self.intel_cpd, self.i_i_cpd)
self.network.initialize_initial_state()
self.assertEqual(set(self.network.get_cpds()), set([self.diff_cpd, self.intel_cpd, self.grade_cpd]))
self.assertEqual(self.network.get_cpds(time_slice=1)[0].variable, ('G', 1))
def test_add_multiple_cpds(self):
self.network.add_edges_from(
[(('D', 0), ('G', 0)), (('I', 0), ('G', 0)), (('D', 0), ('D', 1)), (('I', 0), ('I', 1))])
self.network.add_cpds(self.grade_cpd, self.d_i_cpd, self.diff_cpd, self.intel_cpd, self.i_i_cpd)
self.assertEqual(self.network.get_cpds(('G', 0)).variable, ('G', 0))
self.assertEqual(self.network.get_cpds(('D', 1)).variable, ('D', 1))
self.assertEqual(self.network.get_cpds(('D', 0)).variable, ('D', 0))
self.assertEqual(self.network.get_cpds(('I', 0)).variable, ('I', 0))
self.assertEqual(self.network.get_cpds(('I', 1)).variable, ('I', 1))
def test_initialize_initial_state(self):
self.network.add_nodes_from(['D', 'G', 'I', 'S', 'L'])
self.network.add_edges_from(
[(('D', 0), ('G', 0)), (('I', 0), ('G', 0)), (('D', 0), ('D', 1)), (('I', 0), ('I', 1))])
self.network.add_cpds(self.grade_cpd, self.d_i_cpd, self.diff_cpd, self.intel_cpd, self.i_i_cpd)
self.network.initialize_initial_state()
self.assertEqual(len(self.network.cpds), 6)
self.assertEqual(self.network.get_cpds(('G', 1)).variable, ('G', 1))
def test_moralize(self):
self.network.add_edges_from(([(('D', 0), ('G', 0)), (('I', 0), ('G', 0))]))
moral_graph = self.network.moralize()
self.assertListEqual(hf.recursive_sorted(moral_graph.edges()),
[[('D', 0), ('G', 0)], [('D', 0), ('I', 0)],
[('D', 1), ('G', 1)], [('D', 1), ('I', 1)],
[('G', 0), ('I', 0)], [('G', 1), ('I', 1)]])
def test_copy(self):
self.network.add_edges_from(
[(('D', 0), ('G', 0)), (('I', 0), ('G', 0)), (('D', 0), ('D', 1)), (('I', 0), ('I', 1))])
cpd = TabularCPD(('G', 0), 3, values=[[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[('D', 0), ('I', 0)], evidence_card=[2, 2])
self.network.add_cpds(cpd)
copy = self.network.copy()
self.assertIsInstance(copy, DynamicBayesianNetwork)
self.assertListEqual(sorted(self.network.nodes()), sorted(copy.nodes()))
self.assertListEqual(sorted(self.network.edges()), sorted(copy.edges()))
self.assertListEqual(self.network.get_cpds(), copy.get_cpds())
self.assertListEqual(sorted(self.network.get_intra_edges()), sorted(copy.get_intra_edges()))
self.assertListEqual(sorted(self.network.get_inter_edges()), sorted(copy.get_inter_edges()))
self.assertListEqual(sorted(self.network.get_slice_nodes()), sorted(copy.get_slice_nodes()))
copy.cpds[0].values = np.array([[0.4, 0.05, 0.3, 0.5], [0.3, 0.25, 0.5, 0.3], [0.3, 0.7, 0.2, 0.2]])
self.assertNotEqual(self.network.get_cpds(), copy.get_cpds())
self.network.add_cpds(self.i_i_cpd, self.d_i_cpd)
copy.add_cpds(self.diff_cpd, self.intel_cpd)
self.network.add_node('A')
copy.add_node('Z')
self.network.add_edge(('A', 0), ('D', 0))
copy.add_edge(('Z', 0), ('D', 0))
self.assertNotEqual(sorted(self.network.nodes()), sorted(copy.nodes()))
self.assertNotEqual(sorted(self.network.edges()), sorted(copy.edges()))
self.assertNotEqual(self.network.get_cpds(), copy.get_cpds())
self.assertNotEqual(sorted(self.network.get_intra_edges()), sorted(copy.get_intra_edges()))
self.assertListEqual(sorted(self.network.get_inter_edges()), sorted(copy.get_inter_edges()))
self.assertNotEqual(sorted(self.network.get_slice_nodes()), sorted(copy.get_slice_nodes()))
self.network.add_edge(('A', 0), ('D', 1))
copy.add_edge(('Z', 0), ('D', 1))
self.assertNotEqual(sorted(self.network.get_inter_edges()), sorted(copy.get_inter_edges()))
def tearDown(self):
del self.network
for var in variables:
nvar = var.replace('(', '').replace(')', '').replace('\'', '').replace(' ', '').split(',')
nvars.append((nvar[0], int(nvar[1])))
data.columns = nvars
# LEARNS MODEL FROM REAL DATA
hc = HillClimbSearchDBN(data, scoring_method=BicScore(data))
print 'Learning model'
# GIVE STRUCTURE LEARNING ALGORITHM A HINT OF THE MODEL
nodes = hc.state_names.keys()
start = DynamicBayesianNetwork()
nodes = set(X[0] for X in nodes)
start.add_nodes_from_ts(nodes, [0, 1])
start.add_edge(('A', 1), ('O', 1))
model = hc.estimate(start=start, tabu_length=5, max_indegree=2)
# model = hc.estimate(tabu_length=5, max_indegree=2)
print 'Learning parameters'
model.fit(data)
# model.fit(data, estimator=BayesianEstimator)
model.initialize_initial_state()
print "Model learned successfully: ", model.check_model()
print model.edges()
for cpd in model.get_cpds():
print cpd
# DRAWS RESULTING NETWORK
nx.drawing.nx_pydot.write_dot(model, "../output/network.dot")
'').split(',')
nvars.append((nvar[0], int(nvar[1])))
data.columns = nvars
# LEARNS MODEL FROM REAL DATA
hc = HillClimbSearchDBN(data, scoring_method=BicScore(data))
print 'Learning model'
# GIVE STRUCTURE LEARNING ALGORITHM A HINT OF THE MODEL
nodes = hc.state_names.keys()
start = DynamicBayesianNetwork()
nodes = set(X[0] for X in nodes)
start.add_nodes_from_ts(nodes, [0, 1])
# start.add_edge(('A', 0), ('A', 1))
# start.add_edge(('R', 0), ('R', 1))
start.add_edge(('P', 0), ('P', 1))
model = hc.estimate(start=start, tabu_length=10, max_indegree=2)
# model = hc.estimate(tabu_length=5, max_indegree=2)
print 'Learning parameters'
model.fit(data)
# model.fit(data, estimator=BayesianEstimator)
model.initialize_initial_state()
print "Model learned successfully: ", model.check_model()
print model.edges()
for cpd in model.get_cpds():
print cpd
# DRAWS RESULTING NETWORK
nx.drawing.nx_pydot.write_dot(model, "../output/network.dot")
