def setUp(self):
from gPy.Examples import minibn
self._world = CausalWorld(minibn)
self._skel = minibn
self._intervention = {'Smoking': frozenset(['nonsmoker'])}
self._query = CBN.from_bn(minibn.copy(copy_domain=True))
self._query.intervene(self._intervention)
def tryModel(self, model):
world = CausalWorld(model.copy())
world.observe(1000)
# and again to check updates...
world.observe(1000)
adg = model.adg()
data = world.intervention_data(frozenset())
for child in adg.topological_order():
family_bdeu = data.family_score(child, adg.parents(child))
self.assertAlmostEquals(family_bdeu, model[child].get_counts(data).bdeu_score())
for var in fact.variables():
print >> f, var,
print >> f, 'count'
for inst in fact.insts():
for i in inst:
print >> f, i,
print >> f, fact[inst]
f.close()
bn0 = BN(domain=Domain(), new_domain_variables={'a': [0, 1], 'b': [0, 1]})
bn0.add_cpts([
CPT(Factor(variables=['a'], data=[0.5, 0.5]), child='a'),
CPT(Factor(variables=['a', 'b'], data=[0.3, 0.7, 0.4, 0.6]), child='b')
])
w = CausalWorld(bn0)
samples = w.observe(10000)
disp('two_depend', samples)
bn1 = BN(domain=Domain(), new_domain_variables={'a': [0, 1], 'b': [0, 1]})
bn1.add_cpts([
CPT(Factor(variables=['a'], data=[0.5, 0.5]), child='a'),
CPT(Factor(variables=['b'], data=[0.3, 0.7]), child='b')
])
w = CausalWorld(bn1)
samples = w.observe(10000)
disp('two_independ', samples)
bn2 = BN(domain=Domain(),
new_domain_variables={
'a': [0, 1, 2],
num_observations = int(sys.argv[2]) num_runs = int(sys.argv[3]) burnin = int(sys.argv[4]) sample_every = int(sys.argv[5]) num_samples = int(sys.argv[6]) out=sys.stdout sys.stdout=sys.stderr print 'run correlation sampler: num runs =',num_runs,', instances =',num_observations print 'run correlation sampler: burnin =',burnin,', sample every =',sample_every, print 'num_samples =',num_samples sys.stdout=out # observe (gibbs sample) the model and generate the sampler # over orders of those observations w = CausalWorld(model) out=sys.stdout sys.stdout=sys.stderr print 'observing...', sys.stdout.flush() w.observe(num_observations) print 'done.' print 'scoring...', sys.stdout.flush() optimal_family = FamilyScoreCache(w,model.variables(), max_potential_parents = None, best_family_scale = None) optimal_family.from_adg(model.adg()) optimal_score = Order(optimal_family, model.topological_order()).score() print 'optimal:',optimal_score sys.stdout=out # settings from FK (Mixing rate test on alarm)
num_observations = int(sys.argv[2])
num_runs = int(sys.argv[3])
burnin = int(sys.argv[4])
sample_every = int(sys.argv[5])
num_samples = int(sys.argv[6])
out = sys.stdout
sys.stdout = sys.stderr
print 'run correlation sampler: num runs =', num_runs, ', instances =', num_observations
print 'run correlation sampler: burnin =', burnin, ', sample every =', sample_every,
print 'num_samples =', num_samples
sys.stdout = out
# observe (gibbs sample) the model and generate the sampler
# over orders of those observations
w = CausalWorld(model)
out = sys.stdout
sys.stdout = sys.stderr
print 'observing...',
sys.stdout.flush()
w.observe(num_observations)
print 'done.'
print 'scoring...',
sys.stdout.flush()
optimal_family = FamilyScoreCache(w,
model.variables(),
max_potential_parents=None,
best_family_scale=None)
optimal_family.from_adg(model.adg())
optimal_score = Order(optimal_family, model.topological_order()).score()
print 'optimal:', optimal_score
f = open(fn, 'w')
fact = samples.makeFactor(samples.variables())
for var in fact.variables():
print >>f, var,
print >>f, 'count'
for inst in fact.insts():
for i in inst:
print >>f, i,
print >>f, fact[inst]
f.close()
bn0 = BN(domain=Domain(), new_domain_variables={'a': [0,1], 'b':[0,1]})
bn0.add_cpts([CPT(Factor(variables=['a'], data=[0.5, 0.5]),child='a')
,CPT(Factor(variables=['a','b'], data=[0.3, 0.7, 0.4, 0.6]),child='b')
])
w = CausalWorld(bn0)
samples = w.observe(10000)
disp('two_depend', samples)
bn1 = BN(domain=Domain(), new_domain_variables={'a': [0,1], 'b':[0,1]})
bn1.add_cpts([CPT(Factor(variables=['a'], data=[0.5, 0.5]),child='a')
,CPT(Factor(variables=['b'], data=[0.3, 0.7]),child='b')
])
w = CausalWorld(bn1)
samples = w.observe(10000)
disp('two_independ', samples)
bn2 = BN(domain=Domain(), new_domain_variables={'a': [0,1,2], 'b':[0,1,2]})
bn2.add_cpts([CPT(Factor(variables=['a'], data=[1.0/3.0, 1.0/3.0, 1.0/3.0]),child='a')
,CPT(Factor(variables=['a','b'], data=[0.2, 0.6, 0.2, 0.5, 0.4, 0.1, 0.0, 1.0, 0.0]),child='b')
])
