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 do_it(join_tree):
InferenceController.reapply(join_tree, {0: [0.5, 0.5]})
from pybbn.pptc.inferencecontroller import InferenceController
# you have built a BBN
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))
# you have built a junction tree from the BBN
# let's call this "original" junction tree the left-hand side (lhs) junction tree
lhs_jt = InferenceController.apply(bbn)
# you may just update the CPTs with the original junction tree structure
# the algorithm to find/build the junction tree is avoided
# the CPTs are updated
rhs_jt = InferenceController.reapply(lhs_jt, {0: [0.3, 0.7], 1: [0.2, 0.8, 0.8, 0.2]})
# let's print out the marginal probabilities and see how things changed
# print the marginal probabilities for the lhs junction tree
print('lhs probabilities')
# print the posterior probabilities
for node, posteriors in lhs_jt.get_posteriors().items():
p = ', '.join([f'{val}={prob:.5f}' for val, prob in posteriors.items()])
print(f'{node} : {p}')
# print the marginal probabilities for the rhs junction tree
print('rhs probabilities')
for node, posteriors in rhs_jt.get_posteriors().items():
p = ', '.join([f'{val}={prob:.5f}' for val, prob in posteriors.items()])
print(f'{node} : {p}')
import json
import time
from pybbn.graph.dag import Bbn
from pybbn.pptc.inferencecontroller import InferenceController
def do_it(join_tree):
InferenceController.reapply(join_tree, {0: [0.5, 0.5]})
with open('singly-bbn.json', 'r') as f:
s = time.time()
bbn = Bbn.from_dict(json.loads(f.read()))
e = time.time()
d = e - s
print('{:.5f} seconds to load'.format(d))
s = time.time()
join_tree = InferenceController.apply(bbn)
e = time.time()
d = e - s
print('{:.5f} seconds to create join tree'.format(d))
InferenceController.reapply(join_tree, {0: [0.5, 0.5]})