def setUp(self):
dbn_1 = DynamicBayesianNetwork()
dbn_1.add_edges_from(
[(('Z', 0), ('X', 0)), (('Z', 0), ('Y', 0)), (('Z', 0), ('Z', 1))])
cpd_start_z_1 = TabularCPD(('Z', 0), 2, [[0.8, 0.2]])
cpd_x_1 = TabularCPD(
('X', 0), 2, [[0.9, 0.6], [0.1, 0.4]], [('Z', 0)], [2])
cpd_y_1 = TabularCPD(
('Y', 0), 2, [[0.7, 0.2], [0.3, 0.8]], [('Z', 0)], [2])
cpd_trans_z_1 = TabularCPD(
('Z', 1), 2, [[0.9, 0.1], [0.1, 0.9]], [('Z', 0)], [2])
dbn_1.add_cpds(cpd_start_z_1, cpd_trans_z_1, cpd_x_1, cpd_y_1)
dbn_1.initialize_initial_state()
self.dbn_inference_1 = DBNInference(dbn_1)
dbn_2 = DynamicBayesianNetwork()
dbn_2.add_edges_from([(('Z', 0), ('X', 0)), (('X', 0), ('Y', 0)),
(('Z', 0), ('Z', 1))])
cpd_start_z_2 = TabularCPD(('Z', 0), 2, [[0.5, 0.5]])
cpd_x_2 = TabularCPD(
('X', 0), 2, [[0.6, 0.9], [0.4, 0.1]], [('Z', 0)], [2])
cpd_y_2 = TabularCPD(
('Y', 0), 2, [[0.2, 0.3], [0.8, 0.7]], [('X', 0)], [2])
cpd_z_2 = TabularCPD(
('Z', 1), 2, [[0.4, 0.7], [0.6, 0.3]], [('Z', 0)], [2])
dbn_2.add_cpds(cpd_x_2, cpd_y_2, cpd_z_2, cpd_start_z_2)
dbn_2.initialize_initial_state()
self.dbn_inference_2 = DBNInference(dbn_2)
def buildDBN():
# Construct a DBN object
dbn = DBN()
#!!!!!!!!!!!!!!! VERY IMPORTANT !!!!!!!!!!!!!!!
# MAKE SURE to NAME THE RANDOM VARIABLE "LOCATION AS "L" AND "SENSOR" OBSERVATION AS "O"
########-----YOUR CODE STARTS HERE-----########
dbn.add_edges_from([(('L', 0), ('O', 0)), (('L', 0), ('L', 1))])
l_cpd = TabularCPD(('L', 0), 4, [[0], [0], [1], [0]])
o_cpd = TabularCPD(('O', 0),
4, [
[0.7, 0.1, 0.1, 0.1],
[0.1, 0.7, 0.1, 0.1],
[0.1, 0.1, 0.7, 0.1],
[0.1, 0.1, 0.1, 0.7],
],
evidence=[('L', 0)],
evidence_card=[4])
# l_i_cpd = TabularCPD(('L', 1), 4, [[.5, .5, 0, 0],
# [0, .5, 0, .5],
# [.5, 0, .5, 0],
# [0, 0, .5, .5],],
# evidence=[('L',0)],
# evidence_card=[4])
l_i_cpd = TabularCPD(('L', 1),
4, [
[.5, 0, .5, 0],
[.5, .5, 0, 0],
[0, 0, .5, .5],
[0, .5, 0, .5],
],
evidence=[('L', 0)],
evidence_card=[4])
# print(o_i_cpd)
# print(l_cpd)
# print(o_cpd)
# print(l_i_cpd)
dbn.add_cpds(l_cpd, o_cpd, l_i_cpd)
########-----YOUR CODE ENDS HERE-----########
# Do NOT forget to initialize before doing any inference! Otherwise, errors will be introduced.
dbn.initialize_initial_state()
# Create an inference object
dbn_inf = DBNInference(dbn)
########-----YOUR MAY TEST YOUR CODE BELOW -----########
########-----ADDITIONAL CODE STARTS HERE-----########
#print(dbn_inf.query([('L',3)])[('L',3)].values)
# print(dbn_inf.query([('L',1)])[('L',1)].values)
#print(dbn_inf.query([('L',1)], {('O', 1):2})[('L',1)].values)
########-----YOUR CODE ENDS HERE-----########
return dbn_inf
class DynamicBayesianNetwork(Process):
defaults = {
'nodes': [],
'edges': [],
'conditional_probabilities': {
'node_id': []
}
}
def __init__(self, parameters=None):
super().__init__(parameters)
# set up the network based on the parameters
self.model = DBN()
self.model.add_nodes_from(self.parameters['nodes'])
self.model.add_edges_from(self.parameters['edges'])
print(f'EDGES: {sorted(self.model.edges())}')
import ipdb
ipdb.set_trace()
# TODO -- add 'evidence' -- get from network?
cpds = (TabularCPD(variable=node_id,
variable_card=len(values),
values=values,
evidence=[]) for node_id, values in
self.parameters['conditional_probabilities'])
self.model.add_cpds(cpds)
# make an inference instance for sampling the model
self.inference = BayesianModelSampling(self.model)
# get a sample
sample = self.inference.forward_sample(size=2)
def ports_schema(self):
return {}
def next_update(self, timestep, states):
return {}
def buildDBN():
# Construct a DBN object
dbn = DBN()
dbn.add_edges_from([(('L', 0), ('O', 0)),
(('L', 0), ('L', 1)),
(('L', 1), ('O', 1))])
# setup conditional probability tables for nodes in network
O0_cpd = TabularCPD(('O', 0), 4, [[0.7, 0.1, 0.1, 0.1], # A
[0.1, 0.7, 0.1, 0.1], # B
[0.1, 0.1, 0.7, 0.1], # C
[0.1, 0.1, 0.1, 0.7]], # D
evidence=[('L', 0)], evidence_card=[4])
l0_cpd = TabularCPD(('L', 0), 4, [[0], # A
[0], # B
[1], # C
[0]]) # D
l1_cpd = TabularCPD(('L', 1), 4, [[0.5, 0.0, 0.5, 0.0], # A
[0.5, 0.5, 0.0, 0.0], # B
[0.0, 0.0, 0.5, 0.5], # C
[0.0, 0.5, 0.0, 0.5]], # D
evidence=[('L', 0)], evidence_card=[4])
#add these conditional probability tables to our BayesianModel
dbn.add_cpds(l0_cpd, l1_cpd, O0_cpd)
#initialize our model for time series analysis
dbn.initialize_initial_state()
# Create an inference object to perform queries
dbn_inf = DBNInference(dbn)
#print(dbn_inf.query(variables=[('L',1)], evidence={('O',1): 2})[('L',1)])
return dbn_inf
class DbnCnnInterface(object):
def __init__(self, model_file='../DBN/network.nx'):
nx_model = nx.read_gpickle(model_file)
self.dbn = DynamicBayesianNetwork(nx_model.edges())
self.dbn.add_cpds(*nx_model.cpds)
self.dbn.initialize_initial_state()
self.dbn_infer = DBNInference(self.dbn)
def filter_q_values(self, q_values, evidence=0, method='binary'):
inferred = np.ndarray(shape=(len(q_values), ), dtype=float)
inferred.fill(0)
variables = self.dbn.get_slice_nodes(1)
ev = {node: 0 for node in self.dbn.get_slice_nodes(0)}
if evidence != 0:
self.set_evidence(ev, evidence)
q = self.dbn_infer.query(variables=variables, evidence=ev)
for variable in q.values():
action = self.get_action_id(variable.variables[0])
if method == 'binary':
inferred[action] = 1 if variable.values[1] > 0 else 0
else:
inferred[action] = variable.values[1]
return q_values * inferred
def get_action_id(self, action):
if action[0] == 'Prompt':
return 0
elif action[0] == 'Reward':
return 1
elif action[0] == 'Abort':
return 2
return 3
def set_evidence(self, evidence, id):
if id == 1:
evidence[("Prompt", 0)] = 1
class TestDynamicBayesianNetworkMethods(unittest.TestCase):
def setUp(self):
self.network = DynamicBayesianNetwork()
self.grade_cpd = TabularCPD(
('G', 0), 3, [[0.3, 0.05, 0.9, 0.5], [0.4, 0.25, 0.08, 0.3],
[0.3, 0.7, 0.2, 0.2]], [('D', 0), ('I', 0)], [2, 2])
self.d_i_cpd = TabularCPD(('D', 1), 2, [[0.6, 0.3], [0.4, 0.7]],
[('D', 0)], 2)
self.diff_cpd = TabularCPD(('D', 0), 2, [[0.6, 0.4]])
self.intel_cpd = TabularCPD(('I', 0), 2, [[0.7, 0.3]])
self.i_i_cpd = TabularCPD(('I', 1), 2, [[0.5, 0.4], [0.5, 0.6]],
[('I', 0)], 2)
self.grade_1_cpd = TabularCPD(
('G', 1), 3, [[0.3, 0.05, 0.9, 0.5], [0.4, 0.25, 0.08, 0.3],
[0.3, 0.7, 0.2, 0.2]], [('D', 1), ('I', 1)], [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 tearDown(self):
del self.network
class TestDynamicBayesianNetworkMethods(unittest.TestCase):
def setUp(self):
self.network = DynamicBayesianNetwork()
self.grade_cpd = TabularCPD(('G', 0), 3, [[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]], [('D', 0), ('I', 0)], [2, 2])
self.d_i_cpd = TabularCPD(('D', 1), 2, [[0.6, 0.3], [0.4, 0.7]], [('D', 0)], 2)
self.diff_cpd = TabularCPD(('D', 0), 2, [[0.6, 0.4]])
self.intel_cpd = TabularCPD(('I', 0), 2, [[0.7, 0.3]])
self.i_i_cpd = TabularCPD(('I', 1), 2, [[0.5, 0.4], [0.5, 0.6]], [('I', 0)], 2)
self.grade_1_cpd = TabularCPD(('G', 1), 3, [[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]], [('D', 1), ('I', 1)], [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 tearDown(self):
del self.network
# off
cpd_I1 = TabularCPD(
('I', 1), 3, [[
0.7, 0.1, 0.9, 0.6, 0.0, 0.8, 0.1, 0.0, 0.3, 0.5, 0.1, 0.7, 0.3, 0.0,
0.6, 0.1, 0.0, 0.1
],
[
0.2, 0.1, 0.1, 0.3, 0.2, 0.2, 0.3, 0.1, 0.5, 0.4, 0.1,
0.3, 0.4, 0.4, 0.4, 0.2, 0.1, 0.1
],
[
0.1, 0.8, 0.0, 0.1, 0.8, 0.0, 0.6, 0.9, 0.2, 0.1, 0.8,
0.0, 0.3, 0.6, 0.0, 0.7, 0.9, 0.8
]], [('A', 0), ('I', 0), ('R', 0)], [2, 3, 3])
dbn.add_cpds(cpd_I, cpd_A, cpd_I1, cpd_O, cpd_R)
dbn.initialize_initial_state()
print "Model created successfully: ", dbn.check_model()
# CREATES SIMULATED DATA FROM DBN MODEL
samples = list()
for i in range(1, 10000):
top_order = list(nx.topological_sort(dbn))
sample = dict()
for node in top_order:
curr_cpd = dbn.get_cpds(node)
evidence = curr_cpd.get_evidence()
], #right
evidence=[('state', 0), ('goal', 0)],
evidence_card=[100, 4])
# goal0 - left corner
# goal1 - left post
# goal2 - right post
# goal3 - right corner
goal_i_cpd = TabularCPD(
('goal', 1),
4, [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
evidence=[('goal', 0)],
evidence_card=[4])
# Add the CPD tables into the DBN
dbn.add_cpds(goal_cpd, state_cpd, action_cpd, goal_i_cpd)
# Do NOT forget to initialize before doing any inference! Otherwise, errors will be introduced.
dbn.initialize_initial_state()
# Create an inference object
dbn_inf = DBNInference(dbn)
# Perform inference on the DBN.
result = dbn_inf.query(variables=[('goal', int(time_querying))],
evidence={
('action', int(time_querying)): int(action_id)
})[('goal', int(time_querying))].values
# out = str(result[0]) + "," + str(result[1]) + "," + str(result[2]) + "," + str(result[3])
largest = 0
class TestDynamicBayesianNetworkMethods2(unittest.TestCase):
def setUp(self):
self.G = DynamicBayesianNetwork()
self.G.add_edges_from(
[(('D', 0), ('G', 0)), (('I', 0), ('G', 0)),
(('D', 0), ('D', 1)), (('I', 0), ('I', 1))])
"""
G.edges()
[(('I', 0), ('G', 0)), (('I', 0), ('I', 1)),
(('D', 1), ('G', 1)), (('D', 0), ('G', 0)),
(('D', 0), ('D', 1)), (('I', 1), ('G', 1))]
"""
def test_check_model(self):
grade_cpd = TabularCPD(('G', 0), 3, [[0.3, 0.05, 0.7, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]], [('D', 0), ('I', 0)], [2, 2])
d_i_cpd = TabularCPD(('D', 1), 2, [[0.6, 0.3], [0.4, 0.7]], [('D', 0)], 2)
diff_cpd = TabularCPD(('D', 0), 2, [[0.6, 0.4]])
intel_cpd = TabularCPD(('I', 0), 2, [[0.7, 0.3]])
i_i_cpd = TabularCPD(('I', 1), 2, [[0.5, 0.4], [0.5, 0.6]], [('I', 0)], 2)
grade_1_cpd = TabularCPD(('G', 1), 3, [[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]], [('D', 1), ('I', 1)], [2, 2])
self.G.add_cpds(grade_cpd, d_i_cpd, i_i_cpd)
self.assertTrue(self.G.check_model())
self.G.remove_cpds(grade_cpd, d_i_cpd, i_i_cpd)
self.G.add_cpds(grade_1_cpd, diff_cpd, intel_cpd)
self.assertTrue(self.G.check_model())
def test_check_model1(self):
diff_cpd = TabularCPD(('D', 0), 3, [[0.3, 0.05, 0.7, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]], [('G', 0), ('I', 0)], [2, 2])
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
grade_cpd = TabularCPD(('G', 0), 2, [[0.6, 0.3], [0.4, 0.7]], [('D', 0)], 2)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
diff_cpd = TabularCPD(('D', 0), 2, [[0.6, 0.3], [0.4, 0.7]], [('D', 1)], 2)
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
grade_cpd = TabularCPD(('G', 0), 3, [[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]], [('D', 1), ('I', 1)], [2, 2])
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
grade_cpd = TabularCPD(('G', 1), 3, [[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]], [('D', 0), ('I', 0)], [2, 2])
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
grade_cpd = TabularCPD(('G', 0), 2, [[0.6, 0.3], [0.4, 0.7]], [('D', 1)], 2)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
def test_check_model2(self):
grade_cpd = TabularCPD(('G', 0), 3, [[0.9, 0.05, 0.7, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]], [('D', 0), ('I', 0)], [2, 2])
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
d_i_cpd = TabularCPD(('D', 1), 2, [[0.1, 0.3], [0.4, 0.7]], [('D', 0)], 2)
self.G.add_cpds(d_i_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(d_i_cpd)
diff_cpd = TabularCPD(('D', 0), 2, [[0.7, 0.4]])
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
intel_cpd = TabularCPD(('I', 0), 2, [[1.7, 0.3]])
self.G.add_cpds(intel_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(intel_cpd)
i_i_cpd = TabularCPD(('I', 1), 2, [[0.9, 0.4], [0.5, 0.6]], [('I', 0)], 2)
self.G.add_cpds(i_i_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(i_i_cpd)
grade_1_cpd = TabularCPD(('G', 1), 3, [[0.3, 0.05, 0.8, 0.5],
[0.4, 0.5, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]], [('D', 1), ('I', 1)], [2, 2])
self.G.add_cpds(grade_1_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_1_cpd)
def tearDown(self):
del self.G
et_cpd = TabularCPD(('E(t)', 1),
2, [[0.9828, 0.4068], [0.017108, 0.59317]],
evidence=[('V(t)', 0)],
evidence_card=[2])
ct_cpd = TabularCPD(('C(t)', 1),
2, [[0.9388, 0.82414], [0.0611017, 0.17585]],
evidence=[('V(t)', 0)],
evidence_card=[2])
Vt_cpd = TabularCPD(('V(t)', 1),
2, [[0.96, 0.1], [0.04, 0.9]],
evidence=[('V(t)', 0)],
evidence_card=[2])
dbn.add_cpds(Vt_1_cpd, Vt_cpd, st_cpd, et_cpd, ct_cpd)
dbn.initialize_initial_state()
dbn_inf = DBNInference(dbn)
#dbn_inf.query([('X', 0)], {('Y', 0):0, ('Y', 1):1, ('Y', 2):1})[('X', 0)].values
#array([ 0.66594382, 0.33405618])
nx.draw(dbn, with_labels=True)
plt.show()
'''
dbnet = DBN()
>>> dbnet.add_edges_from([(('Z', 0), ('X', 0)), (('X', 0), ('Y', 0)),
... (('Z', 0), ('Z', 1))])
(('D', 0), ('D', 1)), (('I', 0), ('I', 1))])
grade_cpd = TabularCPD(
('G', 0),
3, [[0.3, 0.05, 0.9, 0.5], [0.4, 0.25, 0.8, 0.03], [0.3, 0.7, 0.02, 0.2]],
evidence=[('I', 0), ('D', 0)],
evidence_card=[2, 2])
print 'grade_cpd', grade_cpd
d_i_cpd = TabularCPD(('D', 1),
2, [[0.6, 0.3], [0.4, 0.7]],
evidence=[('D', 0)],
evidence_card=[2])
diff_cpd = TabularCPD(('D', 0), 2, [[0.6, 0.4]])
intel_cpd = TabularCPD(('I', 0), 2, [[0.7, 0.3]])
i_i_cpd = TabularCPD(('I', 1),
2, [[0.5, 0.4], [0.5, 0.6]],
evidence=[('I', 0)],
evidence_card=[2])
dbn.add_edges_from([(('T', 0), ('T', 1)), (('P', 0), ('T', 1)),
(('P', 1), ('T', 1)), (('T', 0), ('I', 1)),
(('T', 1), ('I', 1)), (('E', 1), ('I', 1))])
trust_cpd = LinearGaussianCPD('T', [0.2, -2, 3, 7], 9.6, ['T', 'P', 'X3'])
# interaction_cpd =
# extranous_interaction_cpd =
dbn.add_cpds(grade_cpd, d_i_cpd, diff_cpd, intel_cpd, i_i_cpd)
dbn.get_cpds()
t = dbn.get_interface_nodes(time_slice=0)
leaves = dbn.get_roots()
print t, leaves
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 TestDynamicBayesianNetworkMethods2(unittest.TestCase):
def setUp(self):
self.G = DynamicBayesianNetwork()
self.G.add_edges_from([
(("D", 0), ("G", 0)),
(("I", 0), ("G", 0)),
(("D", 0), ("D", 1)),
(("I", 0), ("I", 1)),
])
"""
G.edges()
[(('I', 0), ('G', 0)), (('I', 0), ('I', 1)),
(('D', 1), ('G', 1)), (('D', 0), ('G', 0)),
(('D', 0), ('D', 1)), (('I', 1), ('G', 1))]
"""
def test_check_model(self):
grade_cpd = TabularCPD(
("G", 0),
3,
values=[[0.3, 0.05, 0.7, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]],
evidence=[("D", 0), ("I", 0)],
evidence_card=[2, 2],
)
d_i_cpd = TabularCPD(
("D", 1),
2,
values=[[0.6, 0.3], [0.4, 0.7]],
evidence=[("D", 0)],
evidence_card=[2],
)
diff_cpd = TabularCPD(("D", 0), 2, values=[[0.6, 0.4]])
intel_cpd = TabularCPD(("I", 0), 2, values=[[0.7, 0.3]])
i_i_cpd = TabularCPD(
("I", 1),
2,
values=[[0.5, 0.4], [0.5, 0.6]],
evidence=[("I", 0)],
evidence_card=[2],
)
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],
)
self.G.add_cpds(grade_cpd, d_i_cpd, i_i_cpd)
self.assertTrue(self.G.check_model())
self.G.remove_cpds(grade_cpd, d_i_cpd, i_i_cpd)
self.G.add_cpds(grade_1_cpd, diff_cpd, intel_cpd)
self.assertTrue(self.G.check_model())
def test_check_model1(self):
diff_cpd = TabularCPD(
("D", 0),
3,
values=[[0.3, 0.05, 0.7, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]],
evidence=[("G", 0), ("I", 0)],
evidence_card=[2, 2],
)
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
grade_cpd = TabularCPD(
("G", 0),
2,
values=[[0.6, 0.3], [0.4, 0.7]],
evidence=[("D", 0)],
evidence_card=[2],
)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
diff_cpd = TabularCPD(
("D", 0),
2,
values=[[0.6, 0.3], [0.4, 0.7]],
evidence=[("D", 1)],
evidence_card=[2],
)
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
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", 1), ("I", 1)],
evidence_card=[2, 2],
)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
grade_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", 0), ("I", 0)],
evidence_card=[2, 2],
)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
grade_cpd = TabularCPD(
("G", 0),
2,
values=[[0.6, 0.3], [0.4, 0.7]],
evidence=[("D", 1)],
evidence_card=[2],
)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
def test_check_model2(self):
grade_cpd = TabularCPD(
("G", 0),
3,
values=[[0.9, 0.05, 0.7, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]],
evidence=[("D", 0), ("I", 0)],
evidence_card=[2, 2],
)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
d_i_cpd = TabularCPD(
("D", 1),
2,
values=[[0.1, 0.3], [0.4, 0.7]],
evidence=[("D", 0)],
evidence_card=[2],
)
self.G.add_cpds(d_i_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(d_i_cpd)
diff_cpd = TabularCPD(("D", 0), 2, values=[[0.7, 0.4]])
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
intel_cpd = TabularCPD(("I", 0), 2, values=[[1.7, 0.3]])
self.G.add_cpds(intel_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(intel_cpd)
i_i_cpd = TabularCPD(
("I", 1),
2,
values=[[0.9, 0.4], [0.5, 0.6]],
evidence=[("I", 0)],
evidence_card=[2],
)
self.G.add_cpds(i_i_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(i_i_cpd)
grade_1_cpd = TabularCPD(
("G", 1),
3,
values=[[0.3, 0.05, 0.8, 0.5], [0.4, 0.5, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[("D", 1), ("I", 1)],
evidence_card=[2, 2],
)
self.G.add_cpds(grade_1_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_1_cpd)
def tearDown(self):
del self.G
# Q prompt r, nr
# Q fail r, nr
# Q reward r, nr
cpd_O = TabularCPD(('O', 0), 3, [[0.15, 0.70], [0.05, 0.28], [0.80, 0.02]],
[('A', 0)], [2])
# prompt r-p, r-f, r-r, nr-p, nr-f, nr-r
# fail r-p, r-f, r-r, nr-p, nr-f, nr-r
# reward r-p, r-f, r-r, nr-p, nr-f, nr-r
cpd_R_1 = TabularCPD(
('R', 1), 3,
[[0.0, 0.0, 0.0, 0.4, 0.0, 0.0], [0.0, 0.0, 0.0, 0.6, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.0, 0.0, 0.0]], [('A', 0), ('R', 0)], [2, 3])
dbn.add_cpds(cpd_A, cpd_O, cpd_R, cpd_R_1)
dbn.initialize_initial_state()
print "Model created successfully: ", dbn.check_model()
# CREATES OR LOADS SIMULATED DATA FROM DBN MODEL
if create:
# t1 = time.time()
samples = dbn.create_samples(10000)
# t = time.time() - t1
# print t
data = pd.DataFrame(samples)
data.to_csv('../data/data.csv')
else:
data = pd.DataFrame()
