def setUp(self):
super(TestGraphCI, self).setUp()
self.g = list(xrange(4))
self.indeps = list(xrange(4))
self.g[0] = UGraph(vertices=['a', 'b', 'c', 'd', 'e'],
lines=[('a', 'b'), ('a', 'c'), ('b', 'd'),
('b', 'c'), ('c', 'd'), ('c', 'e')])
self.indeps[0] = {
('b', 'e'): [frozenset(['c'])],
('d', 'e'): [frozenset(['c'])],
('a', 'd'): [frozenset(['c', 'b'])],
('a', 'e'): [frozenset(['c'])]
}
self.g[1] = ADG(vertices=['a', 'b', 'c', 'd', 'e', 'f'],
arrows=[('a', 'b'), ('b', 'c'), ('d', 'a'), ('d', 'e'),
('d', 'f'), ('e', 'c'), ('c', 'f')])
self.indeps[1] = {
('e', 'f'): [frozenset(['c', 'd'])],
('a', 'f'): [frozenset(['b', 'd']),
frozenset(['c', 'd'])],
('a', 'e'): [frozenset(['d'])],
('b', 'f'): [frozenset(['a', 'c', 'e']),
frozenset(['c', 'd'])],
('c', 'd'): [frozenset(['b', 'e']),
frozenset(['a', 'e'])],
('b', 'e'): [frozenset(['a']), frozenset(['d'])],
('a', 'c'): [frozenset(['b', 'd']),
frozenset(['b', 'e'])],
('b', 'd'): [frozenset(['a'])]
}
self.g[2] = ADG(vertices=['a', 'b', 'c', 'd'],
arrows=[('a', 'b'), ('b', 'c'), ('c', 'd')])
self.indeps[2] = {
('a', 'd'): [frozenset(['c']), frozenset(['b'])],
('a', 'c'): [frozenset(['b'])],
('b', 'd'): [frozenset(['c'])]
}
self.g[3] = ADG(vertices=['a', 'b', 'c', 'd'],
arrows=[('a', 'c'), ('b', 'c'), ('c', 'd')])
self.indeps[3] = {
('a', 'c'): [frozenset([])],
('b', 'c'): [frozenset([])],
('d', 'c'): [frozenset([])]
}
def search(self, data, order):
# no need to separate out interventional data
ci = PCCI(CBFSeparator(data), constrain_order=order,
hill_climb_cond=self._hill_climb_cond)
skel = ci.skeleton()
g = ADG(vertices=skel.vertices())
for a, b in skel.lines():
if a in ci.potential_ancestors(b):
g.add_arrow(a, b)
else:
g.add_arrow(b, a)
return g
def search(self, data, order, score=None):
arr = []
parents = {}
if score is None:
def score(data, child, parents):
return data.family_score(child, parents)
for i, child in enumerate(order):
# candidate parents are those variables occuring before child in
# the order
candidate_parents = set(order[:i])
parents[child] = self._find_parents(data, child, candidate_parents,
score)
for parent in parents[child]:
arr.append((parent, child))
return ADG(vertices=order, arrows=arr)
def setUp(self):
from gPy.Graphs import ADG
asia_adg = ADG()
asia_adg.add_vertices([
'VisitAsia', 'Tuberculosis', 'Smoking', 'Cancer', 'TbOrCa', 'XRay',
'Bronchitis', 'Dyspnea'
])
asia_adg.put_arrows(
(['VisitAsia',
'Tuberculosis'], ['Tuberculosis',
'TbOrCa'], ['Cancer',
'TbOrCa'], ['Smoking', 'Cancer'],
['Smoking',
'Bronchitis'], ['Bronchitis',
'Dyspnea'], ['TbOrCa',
'Dyspnea'], ['TbOrCa', 'XRay']))
self.asia_adg = asia_adg
self.asia_cpts = {
'VisitAsia': (['Visit', 'No_Visit'], [], [((0.01, 0.99), [])]),
'Tuberculosis':
(['Present',
'Absent'], ['VisitAsia'], [((0.05, 0.95), ['Visit']),
((0.01, 0.99), ['No_Visit'])]),
'Smoking': (['Smoker', 'NonSmoker'], [], [((0.5, 0.5), [])]),
'Cancer':
(['Present',
'Absent'], ['Smoking'], [((0.1, 0.9), ['Smoker']),
((0.01, 0.99), ['NonSmoker'])]),
'TbOrCa': (['True', 'False'], ['Tuberculosis', 'Cancer'],
[((1, 0), ['Present', 'Present']),
((1, 0), ['Present', 'Absent']),
((1, 0), ['Absent', 'Present']),
((0, 1), ['Absent', 'Absent'])]),
'XRay': (['Abnormal',
'Normal'], ['TbOrCa'], [((0.98, 0.02), ['True']),
((0.05, 0.95), ['False'])]),
'Bronchitis': (['Present', 'Absent'], ['Smoking'],
[((0.6, 0.4), ['Smoker']),
((0.3, 0.7), ['NonSmoker'])]),
'Dyspnea': (['Present', 'Absent'], ['TbOrCa', 'Bronchitis'],
[((0.9, 0.1), ['True', 'Present']),
((0.7, 0.3), ['True', 'Absent']),
((0.8, 0.2), ['False', 'Present']),
((0.1, 0.9), ['False', 'Absent'])])
}
"""
This is the easy question. To construct the BN a student can either
deduce it from the information given or use a brute-force search to
find a BN which satistfies all the conditions. The latter option is
possible due to the small number of variables.
The 'ci' function is a direct implementation of what is discussed in
lectures. Students who find the 'ancestral_adg' method in the gPy
Python package will find their job easier.
"""
from gPy.Graphs import ADG
_vertices = ['G' + str(i) for i in range(1, 7)]
_arrows = (('G1', 'G2'), ('G1', 'G3'), ('G2', 'G4'), ('G3', 'G4'),
('G4', 'G6'), ('G4', 'G5'))
bnq1 = ADG(_vertices, _arrows)
def ci(adg, a, b, s):
"""Return whether a is independent of b given s in adg
"""
return adg.ancestral_adg(a | b | s).moralise().separates(a, b, s)
def setUp(self):
from gPy.Graphs import ADG
asia_adg = ADG()
asia_adg.add_vertices(['VisitAsia', 'Tuberculosis', 'Smoking', 'Cancer',
'TbOrCa', 'XRay', 'Bronchitis', 'Dyspnea'])
asia_adg.put_arrows((['VisitAsia','Tuberculosis'],
['Tuberculosis','TbOrCa'],
['Cancer','TbOrCa'],
['Smoking','Cancer'],
['Smoking','Bronchitis'],
['Bronchitis','Dyspnea'],
['TbOrCa','Dyspnea'],
['TbOrCa','XRay']))
self.asia_adg = asia_adg
self.asia_cpts = {
'VisitAsia': (
['Visit', 'No_Visit'], [],
[
((0.01, 0.99), [])
]),
'Tuberculosis': (
['Present', 'Absent'], ['VisitAsia'],
[
((0.05, 0.95), ['Visit']),
((0.01, 0.99), ['No_Visit'])
]),
'Smoking': (
['Smoker', 'NonSmoker'], [],
[
((0.5, 0.5), [])
]),
'Cancer': (
['Present', 'Absent'], ['Smoking'],
[
((0.1, 0.9), ['Smoker']),
((0.01, 0.99), ['NonSmoker'])
]),
'TbOrCa': (
['True', 'False'], ['Tuberculosis', 'Cancer'],
[
((1, 0), ['Present', 'Present']),
((1, 0), ['Present', 'Absent']),
((1, 0), ['Absent', 'Present']),
((0, 1), ['Absent', 'Absent'])
]),
'XRay': (
['Abnormal', 'Normal'], ['TbOrCa'],
[
((0.98, 0.02), ['True']),
((0.05, 0.95), ['False'])
]),
'Bronchitis': (
['Present', 'Absent'], ['Smoking'],
[
((0.6, 0.4), ['Smoker']),
((0.3, 0.7), ['NonSmoker'])
]),
'Dyspnea': (
['Present', 'Absent'], ['TbOrCa', 'Bronchitis'],
[
((0.9, 0.1), ['True', 'Present']),
((0.7, 0.3), ['True', 'Absent']),
((0.8, 0.2), ['False', 'Present']),
((0.1, 0.9), ['False', 'Absent'])
])
}
best_score = score
best_parents = parents
except NameError:
best_score = score
best_parents = parents
return best_parents
def sublist(candidates,n):
if not candidates or n == 0:
yield []
else:
first = [candidates[0]]
for sl in sublist(candidates[1:],n-1):
yield first + sl
for sl in sublist(candidates[1:],n):
yield sl
best_adg = ADG(ordering)
for i, v in enumerate(ordering):
if verbose:
print 'Finding best parents for %s' % v
parents = best_parents(v,ordering[:i])
for parent in parents:
best_adg.add_arrow(parent,v)
print 'Best ADG'
print '*********'
print best_adg
best_score = score
best_parents = parents
except NameError:
best_score = score
best_parents = parents
return best_parents
def sublist(candidates, n):
if not candidates or n == 0:
yield []
else:
first = [candidates[0]]
for sl in sublist(candidates[1:], n - 1):
yield first + sl
for sl in sublist(candidates[1:], n):
yield sl
best_adg = ADG(ordering)
for i, v in enumerate(ordering):
if verbose:
print 'Finding best parents for %s' % v
parents = best_parents(v, ordering[:i])
for parent in parents:
best_adg.add_arrow(parent, v)
print 'Best ADG'
print '*********'
print best_adg
