def test_bde2(self):
myskl = Data(read_csv(open('myksl.dat')))
# > ksbln.prior <- jointprior(kslbn)
# Imaginary sample size: 64
# > kslbn.fit <- getnetwork(learn(kslbn,myksl,ksbln.prior))
# deal scores
# > score(nodes(kslbn.fit)$Smok)
# [1] -637.9544
# > score(nodes(kslbn.fit)$Alc)
# [1] -752.027
# > score(nodes(kslbn.fit)$Work)
# [1] -463.1716
# > score(nodes(kslbn.fit)$Sex)
# [1] -751.0772
# > score(nodes(kslbn.fit)$Year)
# [1] -666.6585
# > score(kslbn.fit)
# [1] -3270.889
node_scores = (-637.9544,-752.027,-463.1716,-751.0772,-666.6585)
net_score = -3270.889
score = 0
for i, cpt in enumerate(self.cpts):
cpt = cpt.get_counts_sql(myskl)
this_score = cpt.bdeu_score(64)
self.assertAlmostEqual(this_score,node_scores[i],places)
score += this_score
self.assertAlmostEqual(score,net_score,places)
def test_bde3(self):
# just tests that myskl has the right counts
myskl = Data(read_csv(open('myksl.dat')))
# > ksbln.prior <- jointprior(kslbn)
# Imaginary sample size: 64
# > kslbn.fit <- getnetwork(learn(kslbn,myksl,ksbln.prior))
# deal scores
# > score(nodes(kslbn.fit)$Smok)
# [1] -637.9544
# > score(nodes(kslbn.fit)$Alc)
# [1] -752.027
# > score(nodes(kslbn.fit)$Work)
# [1] -463.1716
# > score(nodes(kslbn.fit)$Sex)
# [1] -751.0772
# > score(nodes(kslbn.fit)$Year)
# [1] -666.6585
# > score(kslbn.fit)
# [1] -3270.889
node_scores = (-637.9544,-752.027,-463.1716,-751.0772,-666.6585)
net_score = -3270.889
score = 0
for i, cpt in enumerate(self.cpts):
child = cpt.child()
parents = list(cpt.variables() - set([child]))
this_score = myskl.family_score(child,parents,64.0)
self.assertAlmostEqual(this_score,node_scores[i],places)
score += this_score
self.assertAlmostEqual(score,net_score,places)
def runTest(self):
data = CompactFactor(read_csv(open('tetrad_asia.csv')),domain=Domain())
ci = PCCI(G2Separator(data))
g = ICPattern(ci)
self.assertEquals(g.shd(self._asia_pdag),5)
self.assertEquals(self._tetrad_pdag.shd(self._asia_pdag),4)
# I think tetrad is wrong (in terms of implementation)
self.assertEquals(g.shd(self._tetrad_pdag),1)
def test_smalldata_sql(self):
rawdata = read_csv(open('fake_data'))
dat = Data(rawdata)
self.assertEqual(dat.makeFactor([]).z(),5)
self.samefactor(dat.makeFactor(['bar']),Factor(['bar'],[2,2,1]))
self.samefactor(dat.makeFactor(['blah']),Factor(['blah'],[3,1,1]))
self.samefactor(dat.makeFactor(['foo']),Factor(['foo'],[3,1,1,0]))
self.samefactor(dat.makeFactor(['bar','foo']),
Factor(['bar','foo'],[2,0,0,0,1,1,0,0,0,0,1,0]))
def test_smalldata_sql(self):
rawdata = read_csv(open('fake_data'))
dat = Data(rawdata)
self.assertEqual(dat.makeFactor([]).z(), 5)
self.samefactor(dat.makeFactor(['bar']), Factor(['bar'], [2, 2, 1]))
self.samefactor(dat.makeFactor(['blah']), Factor(['blah'], [3, 1, 1]))
self.samefactor(dat.makeFactor(['foo']), Factor(['foo'], [3, 1, 1, 0]))
self.samefactor(
dat.makeFactor(['bar', 'foo']),
Factor(['bar', 'foo'], [2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0]))
def runTest(self):
data = CompactFactor(read_csv(open('tetrad_xor.csv')),domain=Domain())
ci = PCCI(G2Separator(data))
print ci._ind
for a,b in pairs(data.variables()):
if a == 'X1' and b == 'X2' or a == 'X2' and b == 'X1':
self.assert_(ci.has_independence(a, b))
self.assert_(not ci.has_independence_involving(a,b,'X3'))
else:
print a,b
self.assert_(not ci.has_independence(a,b))
data = CompactFactor(read_csv(open('tetrad_xor.csv')),domain=Domain())
ci = PCCI(G2Separator(data))
for a,b in pairs(data.variables()):
if a == 'X1' and b == 'X2' or a == 'X2' and b == 'X1':
self.assert_(ci.has_independence(a, b))
self.assert_(not ci.has_independence_involving(a,b,'X3'))
else:
print a,b
self.assert_(not ci.has_independence(a,b))
def test_ipf2(self):
# just tests termination
alarm = CompactFactor(read_csv(open('alarm_1K.dat')))
vs = list(alarm.variables())
vs.append(vs[0])
marginals = {}
model = RFR()
for i in range(len(vs) - 1):
hyperedge = frozenset(vs[i:i + 2])
model *= Factor(hyperedge)
marginals[hyperedge] = alarm[hyperedge].normalised()
model.ipf(marginals, 0.001)
def test_ipf2(self):
# just tests termination
alarm = CompactFactor(read_csv(open('alarm_1K.dat')))
vs = list(alarm.variables())
vs.append(vs[0])
marginals = {}
model = RFR()
for i in range(len(vs)-1):
hyperedge = frozenset(vs[i:i+2])
model *= Factor(hyperedge)
marginals[hyperedge] = alarm[hyperedge].normalised()
model.ipf(marginals,0.001)
def setUp(self):
from gPy.Variables import Domain
self.domain = Domain()
self.bnm = BN(domain=self.domain)
self.bnm.from_dnet(read_dnet('Asia.dnet'))
self.cptdict = {}
# taken directly from Netica output
self.marginals = [
Factor((('VisitAsia'),),
[0.99,0.01]),
Factor((('Tuberculosis'),),
[0.9896,0.0104]),
Factor((('Smoking'),),
[0.5,0.5]),
Factor((('Cancer'),),
[0.945,0.055]),
Factor((('TbOrCa'),),
[0.93517, 0.064828]),
Factor((('XRay'),),
[0.11029, 0.88971]),
Factor((('Bronchitis'),),
[0.55,0.45]),
Factor((('Dyspnea'),),
[0.56403,0.43597])
]
# taken directly from Netica output
self.cond_marginals = [
Factor((('VisitAsia'),),
[0.95192,0.048077]),
Factor((('Tuberculosis'),),
[0,1]),
Factor((('Smoking'),),
[0.52381,0.47619]),
#other marginals are conditional on these values
#Factor((('Cancer'),),
# [1,0]),
#Factor((('TbOrCa'),),
# [0,1]),
Factor((('XRay'),),
[0.98, 0.02]),
Factor((('Bronchitis'),),
[0.55714,0.44286]),
Factor((('Dyspnea'),),
[0.21143,0.78857])
]
for cpt in self.bnm:
self.cptdict[cpt.child()] = cpt
self.rawdata = read_csv(open('alarm_1K.dat'))
def setUp(self):
from gPy.Variables import Domain
self.domain = Domain()
self.bnm = BN(domain=self.domain)
self.bnm.from_dnet(read_dnet('Asia.dnet'))
self.cptdict = {}
# taken directly from Netica output
self.marginals = [
Factor((('VisitAsia'), ), [0.99, 0.01]),
Factor((('Tuberculosis'), ), [0.9896, 0.0104]),
Factor((('Smoking'), ), [0.5, 0.5]),
Factor((('Cancer'), ), [0.945, 0.055]),
Factor((('TbOrCa'), ), [0.93517, 0.064828]),
Factor((('XRay'), ), [0.11029, 0.88971]),
Factor((('Bronchitis'), ), [0.55, 0.45]),
Factor((('Dyspnea'), ), [0.56403, 0.43597])
]
# taken directly from Netica output
self.cond_marginals = [
Factor((('VisitAsia'), ), [0.95192, 0.048077]),
Factor((('Tuberculosis'), ), [0, 1]),
Factor((('Smoking'), ), [0.52381, 0.47619]),
#other marginals are conditional on these values
#Factor((('Cancer'),),
# [1,0]),
#Factor((('TbOrCa'),),
# [0,1]),
Factor((('XRay'), ), [0.98, 0.02]),
Factor((('Bronchitis'), ), [0.55714, 0.44286]),
Factor((('Dyspnea'), ), [0.21143, 0.78857])
]
for cpt in self.bnm:
self.cptdict[cpt.child()] = cpt
self.rawdata = read_csv(open('alarm_1K.dat'))
def setUp(self):
self.rawdata = read_csv(open('alarm_1K.dat'))
from gPy.IO import read_csv
from gPy.Parameters import CompactFactor
import gPy.Parameters
florida = CompactFactor(read_csv(open('florida.dat')))
#create a normal factor
table = florida['Murderer', 'Sentence', 'Victim']
print table
print 'Number of observations is %d' % table.z()
gPy.Parameters.precision = 6
print table.normalised()
from gPy.IO import read_csv
from gPy.Parameters import CompactFactor
import gPy.Parameters
florida = CompactFactor(read_csv(open('florida.dat')))
#create a normal factor
table = florida['Murderer', 'Sentence', 'Victim']
print table
print 'Number of observations is %d' % table.z()
gPy.Parameters.precision = 6
print table.normalised()
def setUp(self):
self.rawdata = read_csv(open('alarm_1K.dat'))
from gPy.Data import Data2
from gPy.IO import read_csv
import sys, gzip
data = Data2(read_csv(
open('/home/jc/godot/research/icml08/data/asia_100.data')),
rmin=3)
#for k, v in data._data.items():
# print k, v
#print data.marginal(['VisitAsia'])
#print data.marginal(['VisitAsia','TbOrCa','XRay','Dyspnea'])
#data = Data2(read_csv(gzip.open('/home/jc/godot/research/icml08/data/insurance_100.data.gz')),rmin=5)
#for k, v in data._data.items():
# print k, v
#sys.exit()
data._test(['VisitAsia', 'TbOrCa', 'XRay', 'Dyspnea'])
#data._test(['Accident','ILiCost'])
sys.exit()
print data.marginal(['Accident', 'ILiCost'])
vs = sorted(data._variables)
for v in vs:
for w in vs:
for z in vs:
print v, w, z, data.marginal(frozenset([v, w, z]))
from gPy.IO import read_csv
from gPy.Parameters import CompactFactor
from gPy.Demos import marginalise_gui
cancer = CompactFactor(read_csv(open('cancer.dat')))
#create a normal factor
data = cancer['Smoker', 'Cancer', 'Bronchitis']
marginalise_gui(data.normalised())
from gPy.Examples import asia
from gPy.Parameters import CompactFactor
from gPy.IO import read_csv
import sys
data = CompactFactor(read_csv(open(sys.argv[1])))
print asia.bdeu_score(data)
def score_adg(adg,data):
print '^^^^'
for child in adg.vertices():
parents = adg.parents(child)
family = parents | set([child])
data_cpt = data.makeFactor(family).makeCPT(child,False)
print child, data_cpt.bdeu_score()
print 'vvvvv'
print
adg = asia.adg()
score_adg(adg,data)
adg.remove_arrow('Smoking','Cancer')
score_adg(adg,data)
from gPy.Examples import asia
from gPy.Data import CompactFactor
from gPy.IO import read_csv
import sys
data = CompactFactor(read_csv(open(sys.argv[1])))
print asia.bdeu_score(data)
def score_adg(adg,data):
print '^^^^'
for child in adg.vertices():
parents = adg.parents(child)
family = parents | set([child])
data_cpt = data.makeFactor(family).makeCPT(child,False)
print child, data_cpt.bdeu_score()
print 'vvvvv'
print
adg = asia.adg()
score_adg(adg,data)
"""Throwaway script to test BIC score search
"""
#from gPy.Data import Data
from gPy.Data import CompactFactor
import sys, gzip
from gPy.IO import read_csv
data = CompactFactor(read_csv(gzip.open('/home/jc/godot/research/icml08/data/insurance_100.data.gz')))
for v in data.variables():
print v
print
print data.bic_search(v)
from gPy.Data import Data2
from gPy.IO import read_csv
import sys, gzip
data = Data2(read_csv(open("/home/jc/godot/research/icml08/data/asia_100.data")), rmin=3)
# for k, v in data._data.items():
# print k, v
# print data.marginal(['VisitAsia'])
# print data.marginal(['VisitAsia','TbOrCa','XRay','Dyspnea'])
# data = Data2(read_csv(gzip.open('/home/jc/godot/research/icml08/data/insurance_100.data.gz')),rmin=5)
# for k, v in data._data.items():
# print k, v
# sys.exit()
data._test(["VisitAsia", "TbOrCa", "XRay", "Dyspnea"])
# data._test(['Accident','ILiCost'])
sys.exit()
print data.marginal(["Accident", "ILiCost"])
vs = sorted(data._variables)
for v in vs:
for w in vs:
for z in vs:
print v, w, z, data.marginal(frozenset([v, w, z]))
