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_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_generated_serde():
"""
Tests serde of generated BBN.
:return: Nonde.
"""
g, p = generate_singly_bbn(100, max_iter=10)
e_bbn = convert_for_exact_inference(g, p)
d = Bbn.to_dict(e_bbn)
s = json.dumps(d, sort_keys=True, indent=2)
d = json.loads(s)
o_bbn = Bbn.from_dict(d)
assert len(e_bbn.get_nodes()) == len(o_bbn.get_nodes())
assert len(e_bbn.get_edges()) == len(o_bbn.get_edges())
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_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 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 build_bbn(variable_profiles, g, p):
"""
Builds a BBN from a DAG, g, and paremeters, p.
:param variable_profiles: Variable profiles.
:param g: DAG.
:param p: Parameters.
:return: BBN.
"""
bbn = Bbn()
nodes = list(g.nodes)
bbn_node_dict = {}
for idx in nodes:
name = g.nodes[idx]['name']
domain = variable_profiles[name]
cpt = p[idx]
v = Variable(idx, name, domain)
n = BbnNode(v, cpt)
bbn.add_node(n)
bbn_node_dict[idx] = n
edges = list(g.edges)
for edge in edges:
pa = bbn_node_dict[edge[0]]
ch = bbn_node_dict[edge[1]]
e = Edge(pa, ch, EdgeType.DIRECTED)
bbn.add_edge(e)
return bbn
def convert_for_exact_inference(g, p):
"""
Converts the graph and parameters to a BBN.
:param g: Directed acyclic graph (DAG in the form of networkx).
:param p: Parameters.
:return: BBN.
"""
bbn = Bbn()
bbn_nodes = {}
for node in g.nodes:
id = node
params = p[id]['params'].flatten()
states = [
'state{}'.format(state) for state in range(p[id]['shape'][1])
]
v = Variable(id, str(id), states)
n = BbnNode(v, params)
bbn.add_node(n)
bbn_nodes[id] = n
for e in g.edges:
pa = bbn_nodes[e[0]]
ch = bbn_nodes[e[1]]
bbn.add_edge(Edge(pa, ch, EdgeType.DIRECTED))
return bbn
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 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_toplogical_sort_reversed():
"""
Tests topological sorting of graph with nodes in reverse order.
:return: None.
"""
a = BbnNode(Variable(0, 'a', ['on', 'off']), [0.7, 0.3, 0.2, 0.8])
b = BbnNode(Variable(1, 'b', ['on', 'off']), [0.5, 0.5, 0.4, 0.6])
c = BbnNode(Variable(2, 'c', ['on', 'off']), [0.5, 0.5])
bbn = Bbn() \
.add_node(a) \
.add_node(b) \
.add_node(c) \
.add_edge(Edge(c, b, EdgeType.DIRECTED)) \
.add_edge(Edge(b, a, EdgeType.DIRECTED))
sampler = LogicSampler(bbn)
assert_almost_equal([2, 1, 0], sampler.nodes)
def test_toplogical_sort_mixed():
"""
Tests topological sort of diverging structure.
:return: None.
"""
a = BbnNode(Variable(0, 'a', ['on', 'off']), [0.7, 0.3, 0.2, 0.8])
b = BbnNode(Variable(1, 'b', ['on', 'off']), [0.5, 0.5])
c = BbnNode(Variable(2, 'c', ['on', 'off']), [0.5, 0.5, 0.4, 0.6])
bbn = Bbn() \
.add_node(a) \
.add_node(b) \
.add_node(c) \
.add_edge(Edge(b, a, EdgeType.DIRECTED)) \
.add_edge(Edge(b, c, EdgeType.DIRECTED))
sampler = LogicSampler(bbn)
assert_almost_equal([1, 0, 2], sampler.nodes)
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_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_sampler_tables():
"""
Tests sampler creation of tables.
: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)
assert_almost_equal([0, 1, 2], sampler.nodes)
tables = sampler.tables
assert 3 == len(tables)
assert 0 in tables
assert 1 in tables
assert 2 in tables
lhs = np.array(tables[0].probs)
rhs = np.array([0.5, 1.0])
assert_almost_equal(lhs, rhs)
lhs = np.array(list(tables[1].probs.values()))
rhs = np.array([[0.5, 1.0], [0.4, 1.0]])
assert_almost_equal(lhs, rhs)
lhs = np.array(list(tables[2].probs.values()))
rhs = np.array([[0.7, 1.0], [0.2, 1.0]])
assert_almost_equal(lhs, rhs)
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 get_drug_network():
gender_probs = [0.49, 0.51]
drug_probs = [
0.23323615160349853, 0.7667638483965015, 0.7563025210084033,
0.24369747899159663
]
recovery_probs = [
0.31000000000000005, 0.69, 0.27, 0.73, 0.13, 0.87, 0.06999999999999995,
0.93
]
X = BbnNode(Variable(1, 'drug', ['false', 'true']), drug_probs)
Y = BbnNode(Variable(2, 'recovery', ['false', 'true']), recovery_probs)
Z = BbnNode(Variable(0, 'gender', ['female', 'male']), gender_probs)
bbn = Bbn() \
.add_node(X) \
.add_node(Y) \
.add_node(Z) \
.add_edge(Edge(Z, X, EdgeType.DIRECTED)) \
.add_edge(Edge(Z, Y, EdgeType.DIRECTED)) \
.add_edge(Edge(X, Y, EdgeType.DIRECTED))
return bbn
from pybbn.graph.variable import Variable
from pybbn.pptc.inferencecontroller import InferenceController
import networkx as nx # for drawing graphs
import matplotlib.pyplot as plt # for drawing graphs
if __name__ == '__main__':
ap = BbnNode(Variable(0, 'ap', ['yes', 'no']), [0.3, 0.7])
p = BbnNode(Variable(1, 'p', ['yes', 'no']), [0.6, 0.4])
sir = BbnNode(Variable(2, 'sir', ['yes', 'no']),
[0.7, 0.3, 0.45, 0.55, 0.55, 0.45, 0.2, 0.8])
wbc = BbnNode(Variable(3, 'wbc', ['high', 'low']), [0.6, 0.4, 0.3, 0.7])
bbn = Bbn() \
.add_node(ap) \
.add_node(p) \
.add_node(sir) \
.add_node(wbc) \
.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)
import time
from pybbn.graph.dag import Bbn
from pybbn.pptc.inferencecontroller import InferenceController
# deserialization 0.02801
# junction tree 6.10584
start = time.time()
bbn = Bbn.from_json('singly-bbn.json')
stop = time.time()
diff = stop - start
print(f'deserialization {diff:.5f}')
start = time.time()
join_tree = InferenceController.apply(bbn)
stop = time.time()
diff = stop - start
print(f'junction tree {diff:.5f}')
from pybbn.graph.dag import Bbn
from pybbn.graph.edge import Edge, EdgeType
from pybbn.graph.node import BbnNode
from pybbn.graph.variable import Variable
# create graph
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))
# serialize
Bbn.to_csv(bbn, 'simple-bbn.csv')
import json
import numpy as np
from pybbn.generator.bbngenerator import generate_multi_bbn, convert_for_exact_inference
from pybbn.graph.dag import Bbn
np.random.seed(37)
g, p = generate_multi_bbn(900, max_iter=10)
s_bbn = convert_for_exact_inference(g, p)
with open('multi-bbn.json', 'w') as f:
f.write(json.dumps(Bbn.to_dict(s_bbn), sort_keys=True, indent=2))
import functools
import json
import timeit
from pybbn.graph.dag import Bbn
from pybbn.pptc.inferencecontroller import InferenceController
def do_it(bbn):
InferenceController.apply(bbn)
with open('singly-bbn.json', 'r') as f:
bbn = Bbn.from_dict(json.loads(f.read()))
print('finished loading')
n = 20
t = timeit.Timer(functools.partial(do_it, bbn))
d = t.timeit(n) / float(n)
print(d)
