from gPy.LearningUtils import * from gPy.Examples import asia from gPy.K2 import * from gPy.Models import CBN # generate 500 samples of the Asia network data = CausalWorld(asia) data.observe(5000) # for comparison true_model = asia true_adg = asia.adg() # search for the ADG found_adg = K2(max_parents_family=3).search(data, true_adg.topological_order()) # fit a model to the data using the ADG found_model = CBN.from_adg_data(found_adg, data) print 'True ADG\n', true_adg print 'Found ADG\n', found_adg print 'Structural Hamming distance:',shd(found_adg,true_adg) print 'BDeu scores of fitted models of' print 'found ADG:', bdeu(found_adg,data) print ' true ADG:', bdeu(true_adg,data) print 'KL-divergence of fitted found model from true model:',dkl(true_model,found_model)
from gPy.FKMCMC import fk_exp_graph
from gPy.Models import CBN
# generate 500 samples of the Asia network
data = CausalWorld(asia)
data.observe(500)
# for comparison
true_model = asia
true_adg = asia.adg()
# produce a sample of ADGs using K2 to search
search = K2(max_parents_family=3)
# generate 5*2 = 10 ADGs from every 7th step of the MCMC using 2
# `independent' order chains
found_adgs = fk_exp_graph(search, data,
num_samples = 5,
sample_every = 7,
num_orderings = 2)
# fit models to the data using the ADGs
found_models = [CBN.from_adg_data(found_adg, data) for found_adg in found_adgs]
print 'Structural Hamming distance:',[shd(found_adg,true_adg) for found_adg in found_adgs]
print 'BDeu scores of fitted models of'
print 'found ADG:', [bdeu(found_adg,data) for found_adg in found_adgs]
print ' true ADG:', bdeu(true_adg,data)
print 'KL-divergence of fitted found model from true model:',
print [dkl(true_model,found_model) for found_model in found_models]
## independencies into a PDAG. Note that this may fail if the found conditional
## independencies are inconsistent with an ADG!
pdag = ICPattern(ci)
## Meek's algorithm is used to resolve this PDAG into a particular ADG
found_adg = pdag.orient()
## or for short:
## found_adg = ICPattern(PCCI(G2Separator(data))).orient()
##
## Note that G2Separator can be replaced with X2Separator or GraphSeparator
## and PCCI can be replaced with GraphCI. The PDAG is the essential graph
## representing the Markov equivalence class of the conditional independencies.
## (This perhaps goes some way to explaining the complexity --- it depends what
## you are interested in.)
##
## Note also that you may want to scale the p-value with the sample size and
## that there is a BFSeparator but this is likely mathematically unsound.
# fit a model to the data using the ADG
found_model = CBN.from_adg_data(found_adg, data)
print 'Structural Hamming distance:', shd(found_adg, true_adg)
print 'BDeu scores of fitted models of'
print 'found ADG:', bdeu(found_adg, data)
print ' true ADG:', bdeu(true_adg, data)
print 'KL-divergence of fitted found model from true model:', dkl(
true_model, found_model)
