def test_csv_serde():
"""
Tests CSV serde.
:return: None.
"""
try:
lhs = BbnUtil.get_huang_graph()
Bbn.to_csv(lhs, 'huang.csv')
rhs = Bbn.from_csv('huang.csv')
assert len(lhs.get_nodes()) == len(rhs.get_nodes())
assert len(lhs.get_edges()) == len(rhs.get_edges())
lhs_nodes = set([str(node) for node in lhs.get_nodes()])
rhs_nodes = set([str(node) for node in rhs.get_nodes()])
for n in lhs_nodes:
assert n in rhs_nodes
lhs_edges = set([str(edge) for edge in lhs.get_edges()])
rhs_edges = set([str(edge) for edge in rhs.get_edges()])
for e in lhs_edges:
assert e in rhs_edges
except:
assert False
finally:
import os
try:
os.remove('huang.csv')
except:
pass
def test_potential_initializer():
"""
Tests potential initialization.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
def test_potential_initializer():
"""
Tests potential initialization.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
e_potentials = {
'0=on': 0.50000,
'0=off': 0.50000,
'0=on,1=on': 0.50000,
'0=on,1=off': 0.50000,
'0=off,1=on': 0.40000,
'0=off,1=off': 0.60000,
'0=on,2=on': 0.70000,
'0=on,2=off': 0.30000,
'0=off,2=on': 0.20000,
'0=off,2=off': 0.80000,
'1=on,3=on': 0.90000,
'1=on,3=off': 0.10000,
'1=off,3=on': 0.50000,
'1=off,3=off': 0.50000,
'2=on,4=on': 0.30000,
'2=on,4=off': 0.70000,
'2=off,4=on': 0.60000,
'2=off,4=off': 0.40000,
'3=on,4=on,5=on': 0.01000,
'3=on,4=on,5=off': 0.99000,
'3=on,4=off,5=on': 0.01000,
'3=on,4=off,5=off': 0.99000,
'3=off,4=on,5=on': 0.01000,
'3=off,4=on,5=off': 0.99000,
'3=off,4=off,5=on': 0.99000,
'3=off,4=off,5=off': 0.01000,
'2=on,6=on': 0.80000,
'2=on,6=off': 0.20000,
'2=off,6=on': 0.10000,
'2=off,6=off': 0.90000,
'4=on,6=on,7=on': 0.05000,
'4=on,6=on,7=off': 0.95000,
'4=on,6=off,7=on': 0.95000,
'4=on,6=off,7=off': 0.05000,
'4=off,6=on,7=on': 0.95000,
'4=off,6=on,7=off': 0.05000,
'4=off,6=off,7=on': 0.95000,
'4=off,6=off,7=off': 0.05000
}
o_potentials = '\n'.join([str(node.potential) for node in bbn.get_nodes()]).split('\n')
o_potentials = [p.split('|') for p in o_potentials]
o_potentials = {tokens[0]: float(tokens[1]) for tokens in o_potentials}
assert len(e_potentials) == len(o_potentials)
for k, lhs in o_potentials.items():
assert k in e_potentials
rhs = e_potentials[k]
assert lhs == rhs
def test_toplogical_sort_huang():
"""
Tests toplogical sorting of Huang graph.
"""
bbn = BbnUtil.get_huang_graph()
sampler = LogicSampler(bbn)
assert_almost_equal([0, 1, 2, 3, 4, 5, 6, 7], sampler.nodes)
def test_transformer():
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
ug = Moralizer.moralize(bbn)
cliques = Triangulator.triangulate(ug)
join_tree = Transformer.transform(cliques)
def test_moralizer():
"""
Tests moralization.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
ug = Moralizer.moralize(bbn)
def test_from_dict():
"""
Tests creating BBN from dictionary (deserialized from JSON).
:return: None.
"""
e_bbn = BbnUtil.get_huang_graph()
o_bbn = Bbn.from_dict(Bbn.to_dict(e_bbn))
assert len(e_bbn.get_nodes()) == len(o_bbn.get_nodes())
assert len(e_bbn.get_edges()) == len(o_bbn.get_edges())
def test_triangulator():
"""
Tests triangulation.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
ug = Moralizer.moralize(bbn)
cliques = Triangulator.triangulate(ug)
def test_potential_initializer():
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
# assert later
# for node in bbn.get_nodes():
# potential = node.potential
# print('{} {}'.format(node.id, node.variable.name))
# print(potential)
#
# assert 1 == 2
def test_moralizer():
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
ug = Moralizer.moralize(bbn)
# assert later
# for node in ug.get_nodes():
# print(node)
# for edge in ug.get_edges():
# print(edge)
#
# assert 1 == 2
def test_initializer():
"""
Tests initialization.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
ug = Moralizer.moralize(bbn)
cliques = Triangulator.triangulate(ug)
join_tree = Transformer.transform(cliques)
Initializer.initialize(join_tree)
def test_inference_controller():
bbn = BbnUtil.get_huang_graph()
join_tree = InferenceController.apply(bbn)
print('INIT')
print_potentials(join_tree)
ev = EvidenceBuilder()\
.with_node(join_tree.get_bbn_node(0))\
.with_evidence('on', 1.0)\
.build()
join_tree.set_observation(ev)
print('FIRST')
print_potentials(join_tree)
def test_huang_inference_with_multiple_evidence():
"""
Tests inference on the Huang graph with a multiple evidence.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
join_tree = InferenceController.apply(bbn)
expected = {
'a': [0.5, 0.5],
'b': [0.45, 0.55],
'c': [0.45, 0.55],
'd': [0.68, 0.32],
'e': [0.465, 0.535],
'f': [0.176, 0.824],
'g': [0.415, 0.585],
'h': [0.823, 0.177]
}
__validate_posterior__(expected, join_tree)
ev1 = EvidenceBuilder() \
.with_node(join_tree.get_bbn_node_by_name('a')) \
.with_evidence('on', 1.0) \
.build()
ev2 = EvidenceBuilder() \
.with_node(join_tree.get_bbn_node_by_name('f')) \
.with_evidence('on', 1.0) \
.build()
join_tree.unobserve_all()
join_tree.update_evidences([ev1, ev2])
expected = {
'a': [1.0, 0.0],
'b': [0.184, 0.816],
'c': [0.798, 0.202],
'd': [0.0370, 0.963],
'e': [0.0206, 0.979],
'f': [1.0, 0.0],
'g': [0.658, 0.342],
'h': [0.941, 0.0588]
}
__validate_posterior__(expected, join_tree)
def test_triangulator():
"""
Tests triangulation.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
ug = Moralizer.moralize(bbn)
cliques = Triangulator.triangulate(ug)
e_cliques = set(
['(d,e,f)', '(e,g,h)', '(c,e,g)', '(a,b,c)', '(b,c,d)', '(c,d,e)'])
o_cliques = [str(c) for c in cliques]
assert len(e_cliques) == len(o_cliques)
for c in e_cliques:
assert c in o_cliques
def test_moralizer():
"""
Tests moralization.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
ug = Moralizer.moralize(bbn)
e_edges = set([
'0--1', '0--2', '1--3', '2--4', '3--5', '4--5', '2--6', '4--7', '6--7',
'3--4', '4--6'
])
o_edges = set([str(edge) for edge in ug.get_edges()])
assert len(e_edges) == len(o_edges)
for e in e_edges:
assert e in o_edges
def test_huang_inference_with_single_evidence():
"""
Tests inference on the Huang graph with a single evidence.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
join_tree = InferenceController.apply(bbn)
expected = {
'a': [0.5, 0.5],
'b': [0.45, 0.55],
'c': [0.45, 0.55],
'd': [0.68, 0.32],
'e': [0.465, 0.535],
'f': [0.176, 0.824],
'g': [0.415, 0.585],
'h': [0.823, 0.177]
}
__validate_posterior__(expected, join_tree)
ev = EvidenceBuilder() \
.with_node(join_tree.get_bbn_node_by_name('a')) \
.with_evidence('on', 1.0) \
.build()
join_tree.unobserve_all()
join_tree.set_observation(ev)
expected = {
'a': [1.0, 0.0],
'b': [0.5, 0.5],
'c': [0.7, 0.3],
'd': [0.7, 0.3],
'e': [0.39, 0.61],
'f': [0.18934, 0.81066],
'g': [0.59, 0.41],
'h': [0.7826, 0.2174]
}
__validate_posterior__(expected, join_tree)
def test_simple_inference():
"""
Tests inference on the Huang graph.
:return: None.
"""
bbn = BbnUtil.get_simple()
join_tree = InferenceController.apply(bbn)
expected = {
'a': [0.5, 0.5],
'b': [0.45, 0.55],
'c': [0.45, 0.55],
'd': [0.68, 0.32],
'e': [0.465, 0.535],
'f': [0.176, 0.824]
}
__validate_posterior__(expected, join_tree)
__print_potentials__(join_tree)
def test_transformer():
"""
Tests transformer.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
ug = Moralizer.moralize(bbn)
cliques = Triangulator.triangulate(ug)
join_tree = Transformer.transform(cliques)
e_nodes = set([
'(d,e,f)', '(e,g,h)', '(c,e,g)', '(a,b,c)', '(b,c,d)', '(c,d,e)',
'|(a,b,c) -- b,c -- (b,c,d)|', '|(b,c,d) -- c,d -- (c,d,e)|',
'|(c,e,g) -- c,e -- (c,d,e)|', '|(d,e,f) -- d,e -- (c,d,e)|',
'|(e,g,h) -- e,g -- (c,e,g)|'
])
e_edges = set([
'(a,b,c)--|(a,b,c) -- b,c -- (b,c,d)|--(b,c,d)',
'(b,c,d)--|(b,c,d) -- c,d -- (c,d,e)|--(c,d,e)',
'(c,e,g)--|(c,e,g) -- c,e -- (c,d,e)|--(c,d,e)',
'(d,e,f)--|(d,e,f) -- d,e -- (c,d,e)|--(c,d,e)',
'(e,g,h)--|(e,g,h) -- e,g -- (c,e,g)|--(c,e,g)'
])
o_nodes = [str(n) for n in join_tree.get_nodes()]
o_edges = [str(e) for e in join_tree.get_edges()]
assert len(e_nodes) == len(o_nodes)
for n in e_nodes:
assert n in o_nodes
assert len(e_edges) == len(o_edges)
for e in e_edges:
assert e in o_edges
def test_initializer():
"""
Tests initialization.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
ug = Moralizer.moralize(bbn)
cliques = Triangulator.triangulate(ug)
join_tree = Transformer.transform(cliques)
Initializer.initialize(join_tree)
e_potentials = {
'3=on,4=on,5=on': 0.01000,
'3=on,4=on,5=off': 0.99000,
'3=on,4=off,5=on': 0.01000,
'3=on,4=off,5=off': 0.99000,
'3=off,4=on,5=on': 0.01000,
'3=off,4=on,5=off': 0.99000,
'3=off,4=off,5=on': 0.99000,
'3=off,4=off,5=off': 0.01000,
'4=on,6=on,7=on': 0.05000,
'4=on,6=on,7=off': 0.95000,
'4=on,6=off,7=on': 0.95000,
'4=on,6=off,7=off': 0.05000,
'4=off,6=on,7=on': 0.95000,
'4=off,6=on,7=off': 0.05000,
'4=off,6=off,7=on': 0.95000,
'4=off,6=off,7=off': 0.05000,
'2=on,4=on,6=on': 0.80000,
'2=on,4=on,6=off': 0.20000,
'2=on,4=off,6=on': 0.80000,
'2=on,4=off,6=off': 0.20000,
'2=off,4=on,6=on': 0.10000,
'2=off,4=on,6=off': 0.90000,
'2=off,4=off,6=on': 0.10000,
'2=off,4=off,6=off': 0.90000,
'0=on,1=on,2=on': 0.17500,
'0=off,1=on,2=on': 0.04000,
'0=on,1=on,2=off': 0.07500,
'0=off,1=on,2=off': 0.16000,
'0=on,1=off,2=on': 0.17500,
'0=off,1=off,2=on': 0.06000,
'0=on,1=off,2=off': 0.07500,
'0=off,1=off,2=off': 0.24000,
'1=on,2=on,3=on': 0.90000,
'1=off,2=on,3=on': 0.50000,
'1=on,2=on,3=off': 0.10000,
'1=off,2=on,3=off': 0.50000,
'1=on,2=off,3=on': 0.90000,
'1=off,2=off,3=on': 0.50000,
'1=on,2=off,3=off': 0.10000,
'1=off,2=off,3=off': 0.50000,
'2=on,3=on,4=on': 0.30000,
'2=off,3=on,4=on': 0.60000,
'2=on,3=on,4=off': 0.70000,
'2=off,3=on,4=off': 0.40000,
'2=on,3=off,4=on': 0.30000,
'2=off,3=off,4=on': 0.60000,
'2=on,3=off,4=off': 0.70000,
'2=off,3=off,4=off': 0.40000,
'1=on,2=on': 1.00000,
'1=on,2=off': 1.00000,
'1=off,2=on': 1.00000,
'1=off,2=off': 1.00000,
'2=on,3=on': 1.00000,
'2=on,3=off': 1.00000,
'2=off,3=on': 1.00000,
'2=off,3=off': 1.00000,
'2=on,4=on': 1.00000,
'2=on,4=off': 1.00000,
'2=off,4=on': 1.00000,
'2=off,4=off': 1.00000,
'3=on,4=on': 1.00000,
'3=on,4=off': 1.00000,
'3=off,4=on': 1.00000,
'3=off,4=off': 1.00000,
'4=on,6=on': 1.00000,
'4=on,6=off': 1.00000,
'4=off,6=on': 1.00000,
'4=off,6=off': 1.00000
}
o_potentials = '\n'.join([str(v) for _, v in join_tree.potentials.items()]).split('\n')
o_potentials = [p.split('|') for p in o_potentials]
o_potentials = {tokens[0]: float(tokens[1]) for tokens in o_potentials}
assert len(e_potentials) == len(o_potentials)
for k, lhs in o_potentials.items():
assert k in e_potentials
rhs = e_potentials[k]
assert lhs == rhs
import numpy as np from pybbn.generator.bbngenerator import generate_bbn_to_file from pybbn.graph.dag import BbnUtil, Bbn np.random.seed(37) generate_bbn_to_file(900, 'singly-bbn.csv', 'singly', 10) generate_bbn_to_file(900, 'multi-bbn.csv', 'multi', 10) bbn = BbnUtil.get_huang_graph() Bbn.to_csv(bbn, 'huang.csv')
def test_propagator():
"""
Tests propagation.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
PotentialInitializer.init(bbn)
ug = Moralizer.moralize(bbn)
cliques = Triangulator.triangulate(ug)
join_tree = Transformer.transform(cliques)
Initializer.initialize(join_tree)
Propagator.propagate(join_tree)
e_potentials = {
'3=on,4=on,5=on': 0.00315,
'3=on,4=on,5=off': 0.31155,
'3=on,4=off,5=on': 0.00365,
'3=on,4=off,5=off': 0.36165,
'3=off,4=on,5=on': 0.00150,
'3=off,4=on,5=off': 0.14880,
'3=off,4=off,5=on': 0.16800,
'3=off,4=off,5=off': 0.00170,
'4=on,6=on,7=on': 0.00705,
'4=on,6=on,7=off': 0.13395,
'4=on,6=off,7=on': 0.30780,
'4=on,6=off,7=off': 0.01620,
'4=off,6=on,7=on': 0.26030,
'4=off,6=on,7=off': 0.01370,
'4=off,6=off,7=on': 0.24795,
'4=off,6=off,7=off': 0.01305,
'2=on,4=on,6=on': 0.10800,
'2=on,4=on,6=off': 0.02700,
'2=on,4=off,6=on': 0.25200,
'2=on,4=off,6=off': 0.06300,
'2=off,4=on,6=on': 0.03300,
'2=off,4=on,6=off': 0.29700,
'2=off,4=off,6=on': 0.02200,
'2=off,4=off,6=off': 0.19800,
'0=on,1=on,2=on': 0.17500,
'0=off,1=on,2=on': 0.04000,
'0=on,1=on,2=off': 0.07500,
'0=off,1=on,2=off': 0.16000,
'0=on,1=off,2=on': 0.17500,
'0=off,1=off,2=on': 0.06000,
'0=on,1=off,2=off': 0.07500,
'0=off,1=off,2=off': 0.24000,
'1=on,2=on,3=on': 0.19350,
'1=off,2=on,3=on': 0.11750,
'1=on,2=on,3=off': 0.02150,
'1=off,2=on,3=off': 0.11750,
'1=on,2=off,3=on': 0.21150,
'1=off,2=off,3=on': 0.15750,
'1=on,2=off,3=off': 0.02350,
'1=off,2=off,3=off': 0.15750,
'2=on,3=on,4=on': 0.09330,
'2=off,3=on,4=on': 0.22140,
'2=on,3=on,4=off': 0.21770,
'2=off,3=on,4=off': 0.14760,
'2=on,3=off,4=on': 0.04170,
'2=off,3=off,4=on': 0.10860,
'2=on,3=off,4=off': 0.09730,
'2=off,3=off,4=off': 0.07240,
'1=on,2=on': 0.21500,
'1=on,2=off': 0.23500,
'1=off,2=on': 0.23500,
'1=off,2=off': 0.31500,
'2=on,3=on': 0.31100,
'2=on,3=off': 0.13900,
'2=off,3=on': 0.36900,
'2=off,3=off': 0.18100,
'2=on,4=on': 0.13500,
'2=on,4=off': 0.31500,
'2=off,4=on': 0.33000,
'2=off,4=off': 0.22000,
'3=on,4=on': 0.31470,
'3=on,4=off': 0.36530,
'3=off,4=on': 0.15030,
'3=off,4=off': 0.16970,
'4=on,6=on': 0.14100,
'4=on,6=off': 0.32400,
'4=off,6=on': 0.27400,
'4=off,6=off': 0.26100
}
o_potentials = '\n'.join([str(v) for _, v in join_tree.potentials.items()
]).split('\n')
o_potentials = [p.split('|') for p in o_potentials]
o_potentials = {tokens[0]: float(tokens[1]) for tokens in o_potentials}
assert len(e_potentials) == len(o_potentials)
for k, lhs in o_potentials.items():
assert k in e_potentials
rhs = e_potentials[k]
assert lhs == rhs
def test_to_dict():
"""
Tests creating serializable dictionary representation.
:return: None.
"""
bbn = BbnUtil.get_huang_graph()
d = Bbn.to_dict(bbn)
j = json.dumps(d, sort_keys=True, indent=2)
e = """{
"edges": [
{
"ch": 1,
"pa": 0
},
{
"ch": 2,
"pa": 0
},
{
"ch": 3,
"pa": 1
},
{
"ch": 4,
"pa": 2
},
{
"ch": 5,
"pa": 3
},
{
"ch": 5,
"pa": 4
},
{
"ch": 6,
"pa": 2
},
{
"ch": 7,
"pa": 4
},
{
"ch": 7,
"pa": 6
}
],
"nodes": {
"0": {
"probs": [
0.5,
0.5
],
"variable": {
"id": 0,
"name": "a",
"values": [
"on",
"off"
]
}
},
"1": {
"probs": [
0.5,
0.5,
0.4,
0.6
],
"variable": {
"id": 1,
"name": "b",
"values": [
"on",
"off"
]
}
},
"2": {
"probs": [
0.7,
0.3,
0.2,
0.8
],
"variable": {
"id": 2,
"name": "c",
"values": [
"on",
"off"
]
}
},
"3": {
"probs": [
0.9,
0.1,
0.5,
0.5
],
"variable": {
"id": 3,
"name": "d",
"values": [
"on",
"off"
]
}
},
"4": {
"probs": [
0.3,
0.7,
0.6,
0.4
],
"variable": {
"id": 4,
"name": "e",
"values": [
"on",
"off"
]
}
},
"5": {
"probs": [
0.01,
0.99,
0.01,
0.99,
0.01,
0.99,
0.99,
0.01
],
"variable": {
"id": 5,
"name": "f",
"values": [
"on",
"off"
]
}
},
"6": {
"probs": [
0.8,
0.2,
0.1,
0.9
],
"variable": {
"id": 6,
"name": "g",
"values": [
"on",
"off"
]
}
},
"7": {
"probs": [
0.05,
0.95,
0.95,
0.05,
0.95,
0.05,
0.95,
0.05
],
"variable": {
"id": 7,
"name": "h",
"values": [
"on",
"off"
]
}
}
}
}"""
assert len(j) == len(e)
assert j == e