def __init__(self, bn,burnin=1000):
"""
@param bn: A causal Bayesian network from which samples are drawn.
@type bn: L{CBN}
@param burnin: The burn in for the L{GibbsSampler} used for generating
interventional data.
@type burnin: int
"""
super(CausalWorld,self).__init__(domain=Domain.copy(bn),variables=bn.variables())
self._pure_model = bn.copy(copy_domain=True)
self._pure_sampler = ForwardSampler(self._pure_model)
self._burnin = burnin
self._data = None
self._inter_sampler = {}
self._inter_data = {}
def tryModel(self, model):
# generate some samples
cf = []
sampler = ForwardSampler(model)
samples = sampler.samples(100)
cf.append(CompactFactor(samples,domain=Domain()))
icf = IncrementalCompactFactor(samples, domain=Domain())
for i in xrange(10):
samples = sampler.samples(100)
cf.append(CompactFactor(samples,domain=Domain()))
icf.update(samples)
# see if the sum of the CPT Factors in cf match that of icf
for child in model.variables():
family = model.adg().parents(child) | set([child])
a = icf.makeFactor(family)
b = cf[0].makeFactor(family)
for f in cf[1:]:
b += f.makeFactor(family)
self.assert_(same_factor(a,b))
class CausalWorld(SubDomain):
"""A container for experimental data derived from a causal Bayesian networks."""
def __init__(self, bn,burnin=1000):
"""
@param bn: A causal Bayesian network from which samples are drawn.
@type bn: L{CBN}
@param burnin: The burn in for the L{GibbsSampler} used for generating
interventional data.
@type burnin: int
"""
super(CausalWorld,self).__init__(domain=Domain.copy(bn),variables=bn.variables())
self._pure_model = bn.copy(copy_domain=True)
self._pure_sampler = ForwardSampler(self._pure_model)
self._burnin = burnin
self._data = None
self._inter_sampler = {}
self._inter_data = {}
def copy(self):
cpy = copy(self)
cpy._pure_model = self._pure_model.copy(copy_domain=True)
cpy._pure_sampler = None
cpy._inter_sampler = None
cpy._inter_data = {}
for k in self._inter_data.keys():
cpy._inter_data[k] = self._inter_data[k].copy()
# merge _data into _inter_data!
if self._data is not None:
cpy._data = self._data.copy()
return cpy
def interventions(self):
"""
@return: A list of interventional data sets available. A zero length
element denotes observational data.
"""
avail_data = self._inter_data.keys()
if self._data is not None:
avail_data.append(frozenset())
return avail_data
def intervention_data(self, inter):
"""Obtain the L{IncrementalCompactFactor} corresponding to
the intervention C{inter}. C{inter} is typically an element of
the return value of L{interventions}. A zero length C{inter}
indicates observational data.
"""
if len(inter) == 0:
return self._data
return self._inter_data[inter]
def observe(self, num_samples, skip=0):
"""Draw C{num_samples} observational samples, separated by C{skip}
steps of the sampler. These samples are appended to any existing
observational samples and then returned.
@return: L{IncrementalCompactFactor}
"""
samples = self._pure_sampler.samples(num_samples,skip)
if self._data is None:
self._data = IncrementalCompactFactor(samples,domain=Domain())
else:
self._data.update(samples)
return self._data
def query(self, intervention, num_samples,skip=0):
"""Draw C{num_samples} interventional samples, separated by C{skip}
steps of the sampler. These samples are appended to any existing
interventional samples and then returned. The intervention made is
that of C{intervention}.
@param intervention: A dictionary mapping variables in the L{CBN} to
a single value in the domain.
@return: L{IncrementalCompactFactor}
"""
k = frozenset(intervention.keys())
if not self._inter_sampler.has_key(k):
do_model = CBN.from_bn(self._pure_model.copy(copy_domain=True))
do_model.intervene(intervention)
self._inter_sampler[k] = GibbsSampler(do_model, self._burnin)
do_sampler = self._inter_sampler[k]
samples = do_sampler.samples(num_samples,skip)
if not self._inter_data.has_key(k):
self._inter_data[k] = IncrementalCompactFactor(samples,domain=Domain())
else:
self._inter_data[k].update(samples)
return self._inter_data[k]
def makeFactor(self, variables):
"""Construct a factor from only observations (since cannot determine
whether interventional evidence is admissible)."""
return self._data.makeFactor(variables)
def makeCPT(self, child, parents, force_cpt=False, prior=1.0, check=False):
"""Use all data applicable to C{child} to make its CPT
@param prior: the Dirichlet prior parameter (the same parameter value
is used for all instances!) Note there may be some problems with
this method: a B{different} prior is used by the BDeu score. However,
in practice, for parameter estimation, this prior method seems to be ok.
I was lazy and it was simple to implement (cb). If prior is zero, then
the parameters are the maximum likelihood estimation solutions.
"""
# child can use all observable data, and all interventional data where
# child was not intervened.
variables = set(parents) | set([child])
f_child = self._data.makeFactor(variables)
for k in self._inter_data.keys():
if child in k:
continue
f = self._inter_data[k].makeFactor(variables)
# fill in the missing zeros
f.data_extend(dict([(var,f_child.values(var)) for var in k&f.variables()]))
# domain monkeying
for var in k:
f.change_domain_variable(var, f_child.values(var))
# eventually it looks like something edible.
f_child += self._inter_data[k].makeFactor(variables)
# chomp
return CPT(f_child+prior, child, cpt_check=check, cpt_force=force_cpt)
def family_score(self, child, parents):
"""Obtain the BDeu score of a particular family (consisting of a C{child} and
its C{parents}) using all applicable experimental data."""
return self.makeCPT(child, parents, prior=0.0, force_cpt=False, check=False).bdeu_score()
