def main(bnfile,
bdtfile,
scoretype='BDeu',
ess=1.0,
constraint_file="",
cachefile=None,
topN=0,
dump_worklist=False):
cstrs = Constraints(constraint_file)
bn0 = bnmodule.load(bnfile)
sc = scorefactory.getscorer(data.Data(bdtfile),
scoretype,
ess,
cachefile=cachefile)
c = 0
for (_bnas, s) in hpcrawl([bn0],
sc,
cstrs=cstrs,
topN=topN,
dump_worklist=dump_worklist):
print c, s
# if c == 20000:
# bnmodule.BN(bn0.varc,_bnas,do_pic=False).save('X%d.bn' % c)
c += 1
def main(bnfile):
bn = load(bnfile)
stages = []
varsleft = set(bn.vars())
while len(varsleft) > 0:
sources = set(v for v in varsleft if len(bn.parents(v)) == 0)
stages.append(sources)
varsleft -= sources
for v in sources:
for c in bn.children(v):
bn.delarc((v, c))
return stages
def main(bdtfile,
scoretype='BDeu',
ess=1.0,
time=None,
iters=None,
outfile=None,
constraint_file="",
startbn=None,
cachefile=None):
sigpool.watch('SIGUSR2')
sigpool.watch('SIGUSR1')
if time:
sigpool.wait_n_raise(sigpool.str2time(time), 'SIGUSR2')
cstrs = Constraints(constraint_file)
if startbn != None:
bn = bnmodule.load(startbn, do_pic=False)
sc = scorefactory.getscorer(bdtfile,
scoretype,
ess,
cachefile=cachefile)
forests_left = False
else:
bn, sc = bnsearch.empty_net(bdtfile,
scoretype,
ess,
cachefile=cachefile)
bestforests.kruskal(bn, sc, cstrs)
fry = bestforests.Forest(bn)
forests_left = True
bn = fry.next()
if constraint_file: # should check if compatible with start
for a in cstrs.must:
bn.addarc(a)
sc.score_new(bn)
bn.picall()
good_nets = [(sc.score(), bn.copy())]
t = 0L
while True:
greedysearch.greedysearch(bn, sc, 1000, cstrs)
gs = sc.score()
t += 1
if gs > good_nets[0][0] and bn not in [gn for (sn, gn) in good_nets]:
good_nets.append((gs, bn.copy()))
good_nets.sort()
if len(good_nets) > 2 * 10:
good_nets = good_nets[10:]
start_from_forest = random.choice((0, 1)) # stupid if out of forests
if forests_left and start_from_forest:
try:
bn = fry.next()
bn.picall()
except StopIteration:
forests_left = False
if (not start_from_forest) or (not forests_left):
bn = random.choice(good_nets)[1].copy()
# bn = good_nets[0][1].copy()
sc.score_new(bn)
sas = bnsearch.score_arcs(bn, sc)
sas.reverse()
eas = list(enumerate(sas))
for x in xrange(len(sas) / 2):
i, n, sa = wheelselect(eas)
ii, (s, a) = eas.pop(i)
if not a in cstrs.must:
#print 'DEL', a
bn.delarc(a)
#print 'ADEL', bn.arcs()
#for v in bn.vars(): print v, bn.path_in_counts[v]
sc.score_new(bn)
if (iters and t > iters): break
if 'SIGUSR2' in sigpool.flags: break
if 'SIGUSR1' in sigpool.flags:
if outfile: good_nets[-1][1].save(outfile)
print good_nets[-1][0]
sigpool.flags.remove('SIGUSR1')
if outfile:
good_nets[-1][1].save(outfile)
print good_nets[-1][0]
def main(bnfile):
print 'DAG' if is_dag(load(bnfile, do_pic=False)) else 'not DAG'
def main(bnfile):
bn = load(bnfile)
print(" ".join(map(str, gen_totord(bn))))