def test_deepcopy():
"""
Tests deep copy of join tree.
:return: None
"""
a = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
bbn = Bbn().add_node(a).add_node(b) \
.add_edge(Edge(a, b, EdgeType.DIRECTED))
lhs = InferenceController.apply(bbn)
rhs = copy.deepcopy(lhs)
lhs_nodes, rhs_nodes = lhs.get_nodes(), rhs.get_nodes()
lhs_edges, rhs_edges = lhs.get_edges(), rhs.get_edges()
lhs_neighbors, rhs_neighbors = lhs.neighbors, rhs.neighbors
lhs_evidences, rhs_evidences = lhs.evidences, rhs.evidences
lhs_potentials, rhs_potentials = lhs.potentials, rhs.potentials
assert len(lhs_nodes) == len(rhs_nodes)
assert len(lhs_edges) == len(rhs_edges)
assert len(lhs_neighbors) == len(rhs_neighbors)
assert len(lhs_evidences) == len(rhs_evidences)
assert len(lhs_potentials) == len(rhs_potentials)
list(lhs.get_nodes())[0].nodes[0].variable.values[0] = 'true'
lhs_v = list(lhs.get_nodes())[0].nodes[0].variable.values[0]
rhs_v = list(rhs.get_nodes())[0].nodes[0].variable.values[0]
assert lhs_v != rhs_v
def test_inference_4():
"""
Tests inference on simple customized graph.
:return: None.
"""
a = BbnNode(Variable(0, 'a', ['on', 'off']), [0.7, 0.3])
b = BbnNode(Variable(1, 'b', ['on', 'off']), [0.4, 0.6])
c = BbnNode(Variable(2, 'c', ['on', 'off']),
[0.9, 0.1, 0.3, 0.7, 0.5, 0.5, 0.1, 0.9])
e = BbnNode(Variable(4, 'e', ['on', 'off']), [0.6, 0.4, 0.2, 0.8])
bbn = Bbn() \
.add_node(a) \
.add_node(b) \
.add_node(c) \
.add_node(e) \
.add_edge(Edge(a, c, EdgeType.DIRECTED)) \
.add_edge(Edge(b, c, EdgeType.DIRECTED)) \
.add_edge(Edge(c, e, EdgeType.DIRECTED))
join_tree = InferenceController.apply(bbn)
expected = {
'a': [0.7, 0.3],
'b': [0.4, 0.6],
'c': [0.456, 0.544],
'e': [0.3824, 0.6176]
}
__validate_posterior__(expected, join_tree)
def test_sampling_with_rejection():
"""
Tests sampling a serial graph with rejection and evidence set.
:return: None.
"""
a = BbnNode(Variable(0, 'a', ['on', 'off']), [0.5, 0.5])
b = BbnNode(Variable(1, 'b', ['on', 'off']), [0.5, 0.5, 0.4, 0.6])
c = BbnNode(Variable(2, 'c', ['on', 'off']), [0.7, 0.3, 0.2, 0.8])
bbn = Bbn() \
.add_node(a) \
.add_node(b) \
.add_node(c) \
.add_edge(Edge(a, b, EdgeType.DIRECTED)) \
.add_edge(Edge(b, c, EdgeType.DIRECTED))
sampler = LogicSampler(bbn)
n_samples = 10000
samples = pd.DataFrame(
sampler.get_samples(evidence={0: 'on'}, n_samples=n_samples, seed=37))
samples.columns = ['a', 'b', 'c']
assert n_samples == samples.shape[0]
assert 3 == samples.shape[1]
s_a = samples.a.value_counts()
s_b = samples.b.value_counts()
s_c = samples.c.value_counts()
s_a = s_a / s_a.sum()
s_b = s_b / s_b.sum()
s_c = s_c / s_c.sum()
s_a = s_a.sort_index().values
s_b = s_b.sort_index().values
s_c = s_c.sort_index().values
assert_almost_equal(s_a, np.array([1.0]))
assert_almost_equal(s_b, np.array([0.5006, 0.4994]))
assert_almost_equal(s_c, np.array([0.5521, 0.4479]))
join_tree = InferenceController.apply(bbn)
ev = EvidenceBuilder() \
.with_node(join_tree.get_bbn_node_by_name('a')) \
.with_evidence('on', 1.0) \
.build()
join_tree.set_observation(ev)
posteriors = join_tree.get_posteriors()
assert_almost_equal(s_a, np.array([posteriors['a']['on']]), decimal=1)
assert_almost_equal(s_b,
np.array(
[posteriors['b']['off'], posteriors['b']['on']]),
decimal=1)
assert_almost_equal(s_c,
np.array(
[posteriors['c']['off'], posteriors['c']['on']]),
decimal=1)
def __init__(self, bbn):
"""
ctor
:param bbn: Bayesian belief network.
"""
self.bbn = bbn
self.jt = InferenceController.apply(bbn)
def test_from_data_simple():
"""
Tests create BBN from data.
:return: None.
"""
a = BbnNode(Variable(0, 'a', ['on', 'off']), [0.5, 0.5])
b = BbnNode(Variable(1, 'b', ['on', 'off']), [0.5, 0.5, 0.4, 0.6])
c = BbnNode(Variable(2, 'c', ['on', 'off']), [0.7, 0.3, 0.2, 0.8])
bbn1 = Bbn() \
.add_node(a) \
.add_node(b) \
.add_node(c) \
.add_edge(Edge(a, b, EdgeType.DIRECTED)) \
.add_edge(Edge(b, c, EdgeType.DIRECTED))
sampler = LogicSampler(bbn1)
samples = sampler.get_samples(n_samples=10000, seed=37)
i2n = {n.variable.id: n.variable.name for n in bbn1.get_nodes()}
samples = pd.DataFrame(samples).rename(columns=i2n)
parents = {
'a': [],
'b': ['a'],
'c': ['b']
}
bbn2 = Factory.from_data(parents, samples)
join_tree1 = InferenceController.apply(bbn1)
join_tree2 = InferenceController.apply(bbn2)
posteriors1 = join_tree1.get_posteriors()
posteriors2 = join_tree2.get_posteriors()
for k, v1 in posteriors1.items():
assert k in posteriors2
v2 = posteriors2[k]
assert len(v1) == len(v2)
for k2 in v1:
assert k2 in v2
diff = abs(v1[k2] - v2[k2])
assert diff < 0.01
def test_sampling():
"""
Tests sampling a serial graph.
:return: None.
"""
a = BbnNode(Variable(0, 'a', ['on', 'off']), [0.5, 0.5])
b = BbnNode(Variable(1, 'b', ['on', 'off']), [0.5, 0.5, 0.4, 0.6])
c = BbnNode(Variable(2, 'c', ['on', 'off']), [0.7, 0.3, 0.2, 0.8])
bbn = Bbn() \
.add_node(a) \
.add_node(b) \
.add_node(c) \
.add_edge(Edge(a, b, EdgeType.DIRECTED)) \
.add_edge(Edge(b, c, EdgeType.DIRECTED))
sampler = LogicSampler(bbn)
n_samples = 10000
samples = pd.DataFrame(sampler.get_samples(n_samples=n_samples, seed=37))
samples.columns = ['a', 'b', 'c']
assert n_samples == samples.shape[0]
assert 3 == samples.shape[1]
s_a = samples.a.value_counts()
s_b = samples.b.value_counts()
s_c = samples.c.value_counts()
s_a = s_a / s_a.sum()
s_b = s_b / s_b.sum()
s_c = s_c / s_c.sum()
s_a = s_a.sort_index()
s_b = s_b.sort_index()
s_c = s_c.sort_index()
assert_almost_equal(s_a.values, np.array([0.4985, 0.5015]))
assert_almost_equal(s_b.values, np.array([0.5502, 0.4498]))
assert_almost_equal(s_c.values, np.array([0.5721, 0.4279]))
join_tree = InferenceController.apply(bbn)
posteriors = join_tree.get_posteriors()
assert_almost_equal(s_a.values,
np.array(
[posteriors['a']['off'], posteriors['a']['on']]),
decimal=1)
assert_almost_equal(s_b.values,
np.array(
[posteriors['b']['off'], posteriors['b']['on']]),
decimal=1)
assert_almost_equal(s_c.values,
np.array(
[posteriors['c']['off'], posteriors['c']['on']]),
decimal=1)
def test_trivial_inference():
"""
Tests inference on trivial graphs.
:return: None.
"""
a1 = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b1 = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
bbn1 = Bbn().add_node(a1).add_node(b1).add_edge(
Edge(a1, b1, EdgeType.DIRECTED))
jt1 = InferenceController.apply(bbn1)
a2 = BbnNode(Variable(1, 'a', ['t', 'f']), [0.2, 0.8])
b2 = BbnNode(Variable(0, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
bbn2 = Bbn().add_node(a2).add_node(b2).add_edge(
Edge(a2, b2, EdgeType.DIRECTED))
jt2 = InferenceController.apply(bbn2)
a3 = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b3 = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9])
bbn3 = Bbn().add_node(a3).add_node(b3)
jt3 = InferenceController.apply(bbn3)
__validate_posterior__({
'a': [0.2, 0.8],
'b': [0.74, 0.26]
},
jt1,
debug=False)
__validate_posterior__({
'a': [0.2, 0.8],
'b': [0.74, 0.26]
},
jt2,
debug=False)
__validate_posterior__({
'a': [0.2, 0.8],
'b': [0.1, 0.9]
},
jt3,
debug=False)
def test_reapply():
"""
Tests reinitializing join tree after updating CPTs.
:return: None.
"""
a = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
bbn = Bbn().add_node(a).add_node(b) \
.add_edge(Edge(a, b, EdgeType.DIRECTED))
lhs = InferenceController.apply(bbn)
rhs = InferenceController.reapply(lhs, {
0: [0.3, 0.7],
1: [0.2, 0.8, 0.8, 0.2]
})
lhs_pot = [lhs.get_bbn_potential(n) for n in lhs.get_bbn_nodes()]
rhs_pot = [rhs.get_bbn_potential(n) for n in rhs.get_bbn_nodes()]
lhs_d = Potential.to_dict(lhs_pot)
rhs_d = Potential.to_dict(rhs_pot)
# lhs should not match rhs after CPT update
for k, prob in lhs_d.items():
assert k in rhs_d
assert prob != rhs_d[k]
# now create lhs with same params as param used to update old
# should match with rhs since params are now the same
a = BbnNode(Variable(0, 'a', ['t', 'f']), [0.3, 0.7])
b = BbnNode(Variable(1, 'b', ['t', 'f']), [0.2, 0.8, 0.8, 0.2])
bbn = Bbn().add_node(a).add_node(b) \
.add_edge(Edge(a, b, EdgeType.DIRECTED))
lhs = InferenceController.apply(bbn)
lhs_pot = [lhs.get_bbn_potential(n) for n in lhs.get_bbn_nodes()]
lhs_d = Potential.to_dict(lhs_pot)
for k, prob in lhs_d.items():
assert k in rhs_d
assert_almost_equals(prob, rhs_d[k], 0.001)
def test_inference_var_permutation():
"""
Tests inference on graphs where id are reversed.
:return: None.
"""
a1 = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b1 = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
c1 = BbnNode(Variable(2, 'c', ['t', 'f']), [0.2, 0.8, 0.7, 0.3])
bbn1 = Bbn().add_node(a1).add_node(b1).add_node(c1) \
.add_edge(Edge(a1, b1, EdgeType.DIRECTED)) \
.add_edge(Edge(b1, c1, EdgeType.DIRECTED))
jt1 = InferenceController.apply(bbn1)
a2 = BbnNode(Variable(2, 'a', ['t', 'f']), [0.2, 0.8])
b2 = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
c2 = BbnNode(Variable(0, 'c', ['t', 'f']), [0.2, 0.8, 0.7, 0.3])
bbn2 = Bbn().add_node(a2).add_node(b2).add_node(c2) \
.add_edge(Edge(a2, b2, EdgeType.DIRECTED)) \
.add_edge(Edge(b2, c2, EdgeType.DIRECTED))
jt2 = InferenceController.apply(bbn2)
__validate_posterior__(
{
'a': [0.2, 0.8],
'b': [0.74, 0.26],
'c': [0.33, 0.67]
},
jt1,
debug=False)
__validate_posterior__(
{
'a': [0.2, 0.8],
'b': [0.74, 0.26],
'c': [0.33, 0.67]
},
jt2,
debug=False)
def main():
# defining bbn variable to create a bayesian belief network
global machine_name
global join_tree
# enter 1 or 2 as per your choice
machine_type = int(
input(
"Choose : \n 1. Use Existing Machine \n 2. Configure a new machine \n\n"
))
# if existing machine then ask for machine name and open it and process further
if machine_type == 1:
machine_name = str(input("Please input your machine name: "))
machine_name_file = '%s.sav' % machine_name
try:
join_tree = pickle.load(open(machine_name_file, 'rb'))
for node in join_tree.get_bbn_nodes():
print(node)
check(machine_name)
potential_func()
except:
print("Machine name does not exists")
main()
else:
machine_name = str(input("Please input your machine name: "))
globals()['machine_%s' % machine_name] = Bbn()
# create the nodes
create_bbn_nodes()
# create the network structure by edges and nodes
create_bbn_edges()
join_tree = InferenceController.apply(globals()['machine_%s' %
machine_name])
filename = '%s.sav' % machine_name
pickle.dump(join_tree, open(filename, 'wb'))
print(globals()['machine_%s' % machine_name])
check(machine_name)
potential_func()
def test_inference_1():
"""
Tests inference on the Huang graph with manual construction.
:return: None.
"""
a = BbnNode(Variable(0, 'a', ['on', 'off']), [0.5, 0.5])
b = BbnNode(Variable(1, 'b', ['on', 'off']), [0.5, 0.5, 0.4, 0.6])
c = BbnNode(Variable(2, 'c', ['on', 'off']), [0.7, 0.3, 0.2, 0.8])
d = BbnNode(Variable(3, 'd', ['on', 'off']), [0.9, 0.1, 0.5, 0.5])
e = BbnNode(Variable(4, 'e', ['on', 'off']), [0.3, 0.7, 0.6, 0.4])
f = BbnNode(Variable(5, 'f', ['on', 'off']),
[0.01, 0.99, 0.01, 0.99, 0.01, 0.99, 0.99, 0.01])
g = BbnNode(Variable(6, 'g', ['on', 'off']), [0.8, 0.2, 0.1, 0.9])
h = BbnNode(Variable(7, 'h', ['on', 'off']),
[0.05, 0.95, 0.95, 0.05, 0.95, 0.05, 0.95, 0.05])
bbn = Bbn() \
.add_node(a) \
.add_node(b) \
.add_node(c) \
.add_node(d) \
.add_node(e) \
.add_node(f) \
.add_node(g) \
.add_node(h) \
.add_edge(Edge(a, b, EdgeType.DIRECTED)) \
.add_edge(Edge(a, c, EdgeType.DIRECTED)) \
.add_edge(Edge(b, d, EdgeType.DIRECTED)) \
.add_edge(Edge(c, e, EdgeType.DIRECTED)) \
.add_edge(Edge(d, f, EdgeType.DIRECTED)) \
.add_edge(Edge(e, f, EdgeType.DIRECTED)) \
.add_edge(Edge(c, g, EdgeType.DIRECTED)) \
.add_edge(Edge(e, h, EdgeType.DIRECTED)) \
.add_edge(Edge(g, h, EdgeType.DIRECTED))
join_tree = InferenceController.apply(bbn)
expected = {
'a': [0.5, 0.5],
'b': [0.45, 0.55],
'c': [0.45, 0.55],
'd': [0.680, 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)
def get_bbn(fpath):
with open(fpath, 'r') as f:
start = time.time()
bbn = Bbn.from_dict(json.loads(f.read())) if fpath.endswith('.json') else Bbn.from_csv(fpath)
stop = time.time()
diff = stop - start
print(f'{diff:.5f} : load time')
start = time.time()
jt = InferenceController.apply(bbn)
stop = time.time()
diff = stop - start
print(f'{diff:.5f} : inference time')
return bbn, jt
def test_github_issue_4():
"""
Tests issue #4 https://github.com/vangj/py-bbn/issues/4
:return: None.
"""
a = BbnNode(Variable(0, 'A', ['T', 'F']), [0.5, 0.5])
b = BbnNode(Variable(1, 'B', ['T', 'F']), [0.2, 0.8, 0.1, 0.9])
c = BbnNode(Variable(2, 'C', ['T', 'F']), [0.5, 0.5, 0.5, 0.5])
d = BbnNode(Variable(3, 'D', ['T', 'F']), [0.5, 0.5, 0.5, 0.5])
e = BbnNode(Variable(4, 'E', ['T', 'F']), [0.5, 0.5, 0.5, 0.5])
f = BbnNode(Variable(5, 'F', ['T', 'F']), [0.5, 0.5, 0.5, 0.5])
g = BbnNode(Variable(6, 'G', ['T', 'F']), [
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
0.5, 0.5
])
bbn = Bbn() \
.add_node(a) \
.add_node(b) \
.add_node(c) \
.add_node(d) \
.add_node(e) \
.add_node(f) \
.add_node(g) \
.add_edge(Edge(a, b, EdgeType.DIRECTED)) \
.add_edge(Edge(a, c, EdgeType.DIRECTED)) \
.add_edge(Edge(b, d, EdgeType.DIRECTED)) \
.add_edge(Edge(b, e, EdgeType.DIRECTED)) \
.add_edge(Edge(c, f, EdgeType.DIRECTED)) \
.add_edge(Edge(e, g, EdgeType.DIRECTED)) \
.add_edge(Edge(d, g, EdgeType.DIRECTED)) \
.add_edge(Edge(f, g, EdgeType.DIRECTED))
join_tree = InferenceController.apply(bbn)
expected = {
'A': [0.5, 0.5],
'B': [0.15, 0.85],
'C': [0.5, 0.5],
'D': [0.5, 0.5],
'E': [0.5, 0.5],
'F': [0.5, 0.5],
'G': [0.5, 0.5]
}
__validate_posterior__(expected, join_tree)
__print_potentials__(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_from_libpgm_discrete_dictionary():
"""
Tests create py-bbn BBN from dictionary specifying libpgm BBN.
:return: None.
"""
d = get_dict()
bbn = Factory.from_libpgm_discrete_dictionary(d)
assert len(bbn.nodes) == 5
assert len(bbn.edges) == 4
# FIXME this is not working right somehow
join_tree = InferenceController.apply(bbn)
for node in join_tree.get_bbn_nodes():
potential = join_tree.get_bbn_potential(node)
print(node)
print(potential)
print('>')
def test_inference_2():
"""
Tests inference on customized graph.
:return: None.
"""
a = BbnNode(Variable(0, 'a', ['on', 'off']), [0.7, 0.3])
b = BbnNode(Variable(1, 'b', ['on', 'off']), [0.4, 0.6])
c = BbnNode(Variable(2, 'c', ['on', 'off']),
[0.9, 0.1, 0.3, 0.7, 0.5, 0.5, 0.1, 0.9])
d = BbnNode(Variable(3, 'd', ['on', 'off']), [0.3, 0.7, 0.8, 0.2])
e = BbnNode(Variable(4, 'e', ['on', 'off']), [0.6, 0.4, 0.2, 0.8])
f = BbnNode(Variable(5, 'f', ['on', 'off']), [0.7, 0.3, 0.1, 0.9])
g = BbnNode(Variable(6, 'g', ['on', 'off']), [0.4, 0.6, 0.9, 0.1])
bbn = Bbn() \
.add_node(a) \
.add_node(b) \
.add_node(c) \
.add_node(d) \
.add_node(e) \
.add_node(f) \
.add_node(g) \
.add_edge(Edge(a, c, EdgeType.DIRECTED)) \
.add_edge(Edge(b, c, EdgeType.DIRECTED)) \
.add_edge(Edge(c, d, EdgeType.DIRECTED)) \
.add_edge(Edge(c, e, EdgeType.DIRECTED)) \
.add_edge(Edge(d, f, EdgeType.DIRECTED)) \
.add_edge(Edge(d, g, EdgeType.DIRECTED))
join_tree = InferenceController.apply(bbn)
expected = {
'a': [0.7, 0.3],
'b': [0.4, 0.6],
'c': [0.456, 0.544],
'd': [0.572, 0.428],
'e': [0.382, 0.618],
'f': [0.443, 0.557],
'g': [0.614, 0.386]
}
__validate_posterior__(expected, join_tree)
def test_from_libpgm_discrete_dictionary():
"""
Tests create py-bbn BBN from dictionary specifying libpgm BBN.
:return: None.
"""
d = get_dict()
bbn = Factory.from_libpgm_discrete_dictionary(d)
assert len(bbn.nodes) == 5
assert len(bbn.edges) == 4
join_tree = InferenceController.apply(bbn)
__validate_posterior__({
'Difficulty': [0.6, 0.4],
'Intelligence': [0.7, 0.3],
'Grade': [0.362, 0.288, 0.350],
'SAT': [0.725, 0.275],
'Letter': [0.498, 0.502]
}, join_tree, debug=True)
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_inference_libpgm2():
"""
Tests libpgm graph where ordering messes up computation.
:return: None.
"""
letter = BbnNode(Variable(4, 'Letter', ['weak', 'strong']),
[0.1, 0.9, 0.4, 0.6, 0.99, 0.01])
grade = BbnNode(
Variable(2, 'Grade', ['a', 'b', 'c']),
[0.3, 0.4, 0.3, 0.9, 0.08, 0.02, 0.05, 0.25, 0.7, 0.5, 0.3, 0.2])
intelligence = BbnNode(Variable(3, 'Intelligence', ['low', 'high']),
[0.7, 0.3])
sat = BbnNode(Variable(1, 'SAT', ['low', 'high']), [0.95, 0.05, 0.2, 0.8])
difficulty = BbnNode(Variable(0, 'Difficulty', ['easy', 'hard']),
[0.6, 0.4])
bbn = Bbn() \
.add_node(letter) \
.add_node(grade) \
.add_node(intelligence) \
.add_node(sat) \
.add_node(difficulty) \
.add_edge(Edge(difficulty, grade, EdgeType.DIRECTED)) \
.add_edge(Edge(intelligence, grade, EdgeType.DIRECTED)) \
.add_edge(Edge(intelligence, sat, EdgeType.DIRECTED)) \
.add_edge(Edge(grade, letter, EdgeType.DIRECTED))
join_tree = InferenceController.apply(bbn)
__validate_posterior__(
{
'Difficulty': [0.6, 0.4],
'Intelligence': [0.7, 0.3],
'Grade': [0.362, 0.288, 0.350],
'SAT': [0.725, 0.275],
'Letter': [0.498, 0.502]
},
join_tree,
debug=False)
def test_simple_serde():
"""
Tests join tree serde with only 1 clique.
:return: None.
"""
a = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
bbn = Bbn().add_node(a).add_node(b) \
.add_edge(Edge(a, b, EdgeType.DIRECTED))
lhs = InferenceController.apply(bbn)
d = JoinTree.to_dict(lhs)
rhs = JoinTree.from_dict(d)
rhs = InferenceController.apply_from_serde(rhs)
lhs_pot = [lhs.get_bbn_potential(n) for n in lhs.get_bbn_nodes()]
rhs_pot = [rhs.get_bbn_potential(n) for n in rhs.get_bbn_nodes()]
lhs_pot = Potential.to_dict(lhs_pot)
rhs_pot = Potential.to_dict(rhs_pot)
assert len(lhs_pot) == len(rhs_pot)
def test_forest_inference():
"""
Tests inference on a disconnected DAG; sub-DAGs are a -> b, c -> d and e -> f.
:return: None.
"""
a = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
c = BbnNode(Variable(2, 'c', ['t', 'f']), [0.2, 0.8])
d = BbnNode(Variable(3, 'd', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
e = BbnNode(Variable(4, 'e', ['t', 'f']), [0.2, 0.8])
f = BbnNode(Variable(5, 'f', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
bbn = Bbn().add_node(a).add_node(b).add_node(c).add_node(d).add_node(e).add_node(f) \
.add_edge(Edge(a, b, EdgeType.DIRECTED)) \
.add_edge(Edge(c, d, EdgeType.DIRECTED)) \
.add_edge(Edge(e, f, EdgeType.DIRECTED))
jt = InferenceController.apply(bbn)
pot = [jt.get_bbn_potential(n) for n in jt.get_bbn_nodes()]
o = Potential.to_dict(pot)
e = {
'0=t': 0.2,
'0=f': 0.8,
'1=t': 0.7400000000000001,
'1=f': 0.26,
'2=t': 0.2,
'2=f': 0.8,
'3=t': 0.7400000000000001,
'3=f': 0.26,
'4=t': 0.2,
'4=f': 0.8,
'5=t': 0.7400000000000001,
'5=f': 0.26
}
for k, p in e.items():
assert_almost_equals(p, o[k], 0.001)
def do_it(bbn):
InferenceController.apply(bbn)
.add_edge(Edge(ap, sir, EdgeType.DIRECTED)) \
.add_edge(Edge(p, sir, EdgeType.DIRECTED)) \
.add_edge(Edge(sir, wbc, EdgeType.DIRECTED))
options = {
"font_size": 16,
"node_size": 3000,
"node_color": "white",
"edgecolors": "black",
"edge_color": "red",
"linewidths": 5,
"width": 5,
}
n, d = bbn.to_nx_graph()
nx.draw(n, with_labels=True, labels=d, **options)
join_tree = InferenceController.apply(bbn)
ev = EvidenceBuilder() \
.with_node(join_tree.get_bbn_node_by_name('ap')) \
.with_evidence('yes', 1) \
.build()
ev2 = EvidenceBuilder() \
.with_node(join_tree.get_bbn_node_by_name('p')) \
.with_evidence('yes', 1) \
.build()
ev3 = EvidenceBuilder() \
.with_node(join_tree.get_bbn_node_by_name('wbc')) \
.with_evidence('high', 1) \
.build()
def test_inference_libpgm():
"""
Tests inference with evidence on libpgm graph.
:return: None.
"""
difficulty = BbnNode(Variable(0, 'difficulty', ['easy', 'hard']),
[0.6, 0.4])
intelligence = BbnNode(Variable(1, 'intelligence', ['low', 'high']),
[0.7, 0.3])
grade = BbnNode(
Variable(2, 'grade', ['a', 'b', 'c']),
[0.3, 0.4, 0.3, 0.9, 0.08, 0.02, 0.05, 0.25, 0.7, 0.5, 0.3, 0.2])
sat = BbnNode(Variable(3, 'sat', ['low', 'high']), [0.95, 0.05, 0.2, 0.8])
letter = BbnNode(Variable(4, 'letter', ['weak', 'strong']),
[0.1, 0.9, 0.4, 0.6, 0.99, 0.01])
bbn = Bbn() \
.add_node(difficulty) \
.add_node(intelligence) \
.add_node(grade) \
.add_node(sat) \
.add_node(letter) \
.add_edge(Edge(difficulty, grade, EdgeType.DIRECTED)) \
.add_edge(Edge(intelligence, grade, EdgeType.DIRECTED)) \
.add_edge(Edge(intelligence, sat, EdgeType.DIRECTED)) \
.add_edge(Edge(grade, letter, EdgeType.DIRECTED))
join_tree = InferenceController.apply(bbn)
__validate_posterior__(
{
'difficulty': [0.6, 0.4],
'intelligence': [0.7, 0.3],
'grade': [0.362, 0.288, 0.350],
'sat': [0.725, 0.275],
'letter': [0.498, 0.502]
}, join_tree)
ev = EvidenceBuilder() \
.with_node(join_tree.get_bbn_node_by_name('sat')) \
.with_evidence('high', 1.0) \
.build()
join_tree.unobserve_all()
join_tree.set_observation(ev)
__validate_posterior__(
{
'difficulty': [0.6, 0.4],
'intelligence': [0.127, 0.873],
'grade': [0.671, 0.190, 0.139],
'sat': [0.0, 1.0],
'letter': [0.281, 0.719]
}, join_tree)
ev = EvidenceBuilder() \
.with_node(join_tree.get_bbn_node_by_name('sat')) \
.with_evidence('low', 1.0) \
.build()
join_tree.unobserve_all()
join_tree.set_observation(ev)
__validate_posterior__(
{
'difficulty': [0.6, 0.4],
'intelligence': [0.917, 0.0828],
'grade': [0.245, 0.326, 0.430],
'sat': [1.0, 0.0],
'letter': [0.58, 0.42]
}, join_tree)
def bayesian_belief_network():
deadline = BbnNode(Variable(0, 'deadline', ['long', 'medium', 'short']),
[0.33, 0.33, 0.33])
goal = BbnNode(Variable(1, 'goal', ['large', 'medium', 'small']),
[0.33, 0.33, 0.33])
backers = BbnNode(Variable(3, 'backers', ['many', 'medium', 'few']),
[0.6, 0.3, 0.2, 0.3, 0.4, 0.4, 0.1, 0.3, 0.4])
pledges = BbnNode(Variable(4, 'pledges', ['large', 'medium', 'small']),
[0.6, 0.3, 0.1, 0.2, 0.5, 0.2, 0.2, 0.2, 0.7])
state = BbnNode(Variable(5, 'state', ['success', 'fail']), [
0.7, 0.5, 0.2, 0.8, 0.6, 0.2, 0.9, 0.7, 0.3, 0.3, 0.5, 0.8, 0.2, 0.4,
0.8, 0.1, 0.3, 0.7
])
global Bbn
Bbn = Bbn() \
.add_node(deadline) \
.add_node(goal) \
.add_node(backers) \
.add_node(pledges) \
.add_node(state) \
.add_edge(Edge( goal, backers, EdgeType.DIRECTED)) \
.add_edge(Edge( goal, pledges, EdgeType.DIRECTED)) \
.add_edge(Edge( backers, state, EdgeType.DIRECTED)) \
.add_edge(Edge( deadline, state, EdgeType.DIRECTED))
with warnings.catch_warnings():
warnings.simplefilter('ignore')
graph = convert_for_drawing(Bbn)
pos = nx.nx_agraph.graphviz_layout(graph, prog='dot')
#uncomment this section to print BBN
#plt.figure(figsize=(8, 8))
#plt.subplot(121)
#labels = dict([(k, node.variable.name) for k, node in Bbn.nodes.items()])
#nx.draw(graph, pos=pos, with_labels=True, labels=labels)
#plt.title('Bayesian Belief Network Diagram')
#plt.show()
join_tree = InferenceController.apply(Bbn)
print("BBN - Original Probabilities")
for node in join_tree.get_bbn_nodes():
potential = join_tree.get_bbn_potential(node)
print(node)
print(potential)
print('--------------------->')
# insert an observation evidence
ev = EvidenceBuilder() \
.with_node(join_tree.get_bbn_node_by_name('goal')) \
.with_evidence('large', 1.0) \
.build()
join_tree.set_observation(ev)
#print the marginal probabilities
print()
print()
print("BBN - Set Goal = Large")
for node in join_tree.get_bbn_nodes():
potential = join_tree.get_bbn_potential(node)
print(node)
print(potential)
print('--------------------->')
def test_to_dict():
"""
Tests serializing join tree to dictionary.
:return: None.
"""
n0 = BbnNode(Variable(0, 'n0', ['t', 'f']), [0.2, 0.8])
n1 = BbnNode(Variable(1, 'n1', ['t', 'f']), [0.9, 0.1, 0.9, 0.1])
n2 = BbnNode(Variable(2, 'n2', ['t', 'f']), [0.6, 0.4, 0.4, 0.6])
bbn = Bbn().add_node(n0).add_node(n1).add_node(n2) \
.add_edge(Edge(n0, n1, EdgeType.DIRECTED)) \
.add_edge(Edge(n1, n2, EdgeType.DIRECTED))
jt = InferenceController.apply(bbn)
d = JoinTree.to_dict(jt)
lhs = json.dumps(d, sort_keys=True, indent=2)
rhs = """{
"bbn_nodes": {
"0": {
"probs": [
0.2,
0.8
],
"variable": {
"id": 0,
"name": "n0",
"values": [
"t",
"f"
]
}
},
"1": {
"probs": [
0.9,
0.1,
0.9,
0.1
],
"variable": {
"id": 1,
"name": "n1",
"values": [
"t",
"f"
]
}
},
"2": {
"probs": [
0.6,
0.4,
0.4,
0.6
],
"variable": {
"id": 2,
"name": "n2",
"values": [
"t",
"f"
]
}
}
},
"jt": {
"edges": [
"0-1-1-1-2"
],
"nodes": {
"0-1": {
"node_ids": [
0,
1
],
"type": "clique"
},
"0-1-1-1-2": {
"left": "0-1",
"right": "1-2",
"type": "sepset"
},
"1-2": {
"node_ids": [
1,
2
],
"type": "clique"
}
},
"parent_info": {
"0": [],
"1": [
0
],
"2": [
1
]
}
}
}"""
assert lhs == rhs
def test_from_dict():
"""
Tests deserializing from dictionary.
:return:
"""
s = """{
"bbn_nodes": {
"0": {
"probs": [
0.2,
0.8
],
"variable": {
"id": 0,
"name": "n0",
"values": [
"t",
"f"
]
}
},
"1": {
"probs": [
0.9,
0.1,
0.9,
0.1
],
"variable": {
"id": 1,
"name": "n1",
"values": [
"t",
"f"
]
}
},
"2": {
"probs": [
0.6,
0.4,
0.4,
0.6
],
"variable": {
"id": 2,
"name": "n2",
"values": [
"t",
"f"
]
}
}
},
"jt": {
"edges": [
"0-1-1-1-2"
],
"nodes": {
"0-1": {
"node_ids": [
0,
1
],
"type": "clique"
},
"0-1-1-1-2": {
"left": "0-1",
"right": "1-2",
"type": "sepset"
},
"1-2": {
"node_ids": [
1,
2
],
"type": "clique"
}
},
"parent_info": {
"0": [],
"1": [
0
],
"2": [
1
]
}
}
}"""
d = json.loads(s)
lhs = JoinTree.from_dict(d)
lhs = InferenceController.apply_from_serde(lhs)
n0 = BbnNode(Variable(0, 'n0', ['t', 'f']), [0.2, 0.8])
n1 = BbnNode(Variable(1, 'n1', ['t', 'f']), [0.9, 0.1, 0.9, 0.1])
n2 = BbnNode(Variable(2, 'n2', ['t', 'f']), [0.6, 0.4, 0.4, 0.6])
bbn = Bbn().add_node(n0).add_node(n1).add_node(n2) \
.add_edge(Edge(n0, n1, EdgeType.DIRECTED)) \
.add_edge(Edge(n1, n2, EdgeType.DIRECTED))
rhs = InferenceController.apply(bbn)
lhs_pot = [lhs.get_bbn_potential(n) for n in lhs.get_bbn_nodes()]
rhs_pot = [rhs.get_bbn_potential(n) for n in rhs.get_bbn_nodes()]
lhs_pot = Potential.to_dict(lhs_pot)
rhs_pot = Potential.to_dict(rhs_pot)
assert len(lhs_pot) == len(rhs_pot)
for k, p in lhs_pot.items():
assert_almost_equals(p, rhs_pot[k], 0.001)
import json
from pybbn.graph.dag import Bbn
from pybbn.graph.edge import EdgeType, Edge
from pybbn.graph.jointree import JoinTree
from pybbn.graph.node import BbnNode
from pybbn.graph.variable import Variable
from pybbn.pptc.inferencecontroller import InferenceController
a = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
bbn = Bbn().add_node(a).add_node(b) \
.add_edge(Edge(a, b, EdgeType.DIRECTED))
jt = InferenceController.apply(bbn)
with open('simple-join-tree.json', 'w') as f:
d = JoinTree.to_dict(jt)
j = json.dumps(d, sort_keys=True, indent=2)
f.write(j)