def runTest(self):
# results from R version 2.6.2 (2008-02-08) ISBN 3-900051-07-0
r_dx2 = 3453.429
r_dp = 2.2e-16
r_ddf = 4
# regression (NOT known correct! NOT from R)
r_ix2 = 1.08094376504
r_ip = 0.58247332853
r_idf = 2
vars = ['a','b']
vals = {'a': [0,1,2], 'b':[0,1,2]}
ddata = (vars,vals,vars,[(0,0,640),(0,1,1947),(0,2,648),(1,0,1709),(1,1,1364),(1,2,335),(2,0,0),(2,1,3357),(2,2,0)])
iddata = (vars,vals,vars,[(0,0,994),(0,1,2359),(0,2,0),(1,0,1027),(1,1,2312),(1,2,0),(2,0,989),(2,1,2319),(2,2,0)])
x2d = X2Separator(CompactFactor(ddata, domain=Domain()))
x2i = X2Separator(CompactFactor(iddata, domain=Domain()))
dp, dx2, dd = x2d.test_independ('a','b',set())
self.assertEquals(dd,r_ddf)
self.assertAlmostEquals(dx2,r_dx2,3)
self.assertAlmostEquals(dp,r_dp,4)
ip, ix2, id = x2i.test_independ('a','b',set())
self.assertEquals(id,r_idf)
self.assertAlmostEquals(ix2,r_ix2,4)
self.assertAlmostEquals(ip,r_ip,4)
# since there are more zero counts there should be fewer dof.
self.assert_(id < dd)
print ' x2 depend: p =',dp,'x2 =',dx2,'d =',dd
print '!! x2 independ: p =',ip,'x2 =',ix2,'d =',id
# regression (NOT known correct! NOT from R)
r_dg2 = 4224.3017675
r_dp = 0.0
r_ddf = 4
r_ig2 = 1.07917600592
r_ip = 0.582988392395
r_idf = 2
g2d = G2Separator(CompactFactor(ddata, domain=Domain()))
g2i = G2Separator(CompactFactor(iddata, domain=Domain()))
dp, dg2, dd = g2d.test_independ('a','b',set())
self.assertEquals(dd,r_ddf)
self.assertAlmostEquals(dg2,r_dg2,4)
self.assertAlmostEquals(dp,r_dp,4)
ip, ig2, id = g2i.test_independ('a','b',set())
self.assertEquals(id,r_idf)
self.assertAlmostEquals(ig2,r_ig2,4)
self.assertAlmostEquals(ip,r_ip,4)
print '!! g2 depend: p =',dp,'g2 =',dg2,'d =',dd
print '!! g2 independ: p =',ip,'g2 =',ig2,'d =',id
def runTest(self):
# results from R version 2.6.2 (2008-02-08) ISBN 3-900051-07-0
r_dx2 = 109.1547
r_dp = 0
r_ddf = 1
r_ix2 = 0.1236
r_ip = 0.7252
r_idf = 1
vars = ['a','b']
vals = {'a': [0,1], 'b':[0,1]}
ddata = (vars,vals,vars,[(0,0,1509), (0,1,3538), (1,0,1974), (1,1,2979)])
iddata = (vars,vals,vars,[(0,0,1474), (0,1,3610), (1,0,1441), (1,1,3475)])
x2d = X2Separator(CompactFactor(ddata, domain=Domain()))
x2i = X2Separator(CompactFactor(iddata, domain=Domain()))
dp, dx2, dd = x2d.test_independ('a','b',set())
self.assertEquals(dd,r_ddf)
self.assertAlmostEquals(dx2,r_dx2,4)
self.assertAlmostEquals(dp,r_dp,4)
ip, ix2, id = x2i.test_independ('a','b',set())
self.assertEquals(id,r_idf)
self.assertAlmostEquals(ix2,r_ix2,4)
self.assertAlmostEquals(ip,r_ip,4)
print ' x2 depend: p =',dp,'x2 =',dx2,'d =',dd
print ' x2 independ: p =',ip,'x2 =',ix2,'d =',id
# regression (NOT known correct! NOT from R)
r_dg2 = 109.389800878
r_dp = 0
r_ddf = 1
r_ig2 = 0.123551704118
r_ip = 0.725213854926
r_idf = 1
g2d = G2Separator(CompactFactor(ddata, domain=Domain()))
g2i = G2Separator(CompactFactor(iddata, domain=Domain()))
dp, dg2, dd = g2d.test_independ('a','b',set())
self.assertEquals(dd,r_ddf)
self.assertAlmostEquals(dg2,r_dg2,4)
self.assertAlmostEquals(dp,r_dp,4)
ip, ig2, id = g2i.test_independ('a','b',set())
self.assertEquals(id,r_idf)
self.assertAlmostEquals(ig2,r_ig2,4)
self.assertAlmostEquals(ip,r_ip,4)
print '!! g2 depend: p =',dp,'g2 =',dg2,'d =',dd
print '!! g2 independ: p =',ip,'g2 =',ig2,'d =',id
def runTest(self):
samples = GibbsSampler(self._minibn_do).samples(100000)
data = CompactFactor(samples,domain=Domain())
p = self._minibn_do.copy(copy_domain=True)
p.estimate_parameters(data)
self.failUnless(same_factor(distribution_of(p), distribution_of(self._minibn_do), dp=2, verbose=True))
def runTest(self):
# construct factors of various sizes with no data
for sz in xrange(6):
vars = ['V'+str(i) for i in xrange(sz)]
vals = dict([(v,[0,1]) for v in vars])
data = (vars,vals,vars,[])
for v_on in subsetn(vars, sz):
inst = []
for v in vars:
if v in v_on:
inst.append(1)
else:
inst.append(0)
data[3].append(tuple(inst+[0]))
d = CompactFactor(data,domain=Domain())
x2 = X2Separator(d)
g2 = G2Separator(d)
for a,b in pairs(vars):
for s in powerset(set(vars) - set([a,b])):
x2p, x2s, x2d = x2.test_independ(a, b, set(s))
g2p, g2s, g2d = g2.test_independ(a, b, set(s))
# one degree of freedom
self.assertEquals(x2d, 0)
self.assertEquals(g2d, 0)
# default to independent
self.assertEquals(x2p, 1)
self.assertEquals(g2p, 1)
# zero statistics (no data)
self.assertEquals(x2s, 0)
self.assertEquals(g2s, 0)
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 runTest(self):
num_vars = 5
num_vals = 10
for i in xrange(num_runs):
vs = dict([('V'+str(i),range(num_vals)) for i in xrange(num_vars)])
# should get some random data with 0.
f = Factor(variables = vs.keys()
,data = [abs(randint(0,5)) for i in xrange(num_vals**num_vars)]
,domain = Domain()
,new_domain_variables=vs
,check = True)
records = []
for inst in f.insts():
records.append(inst + (f[inst],))
rawdata = (vs.keys(), vs, vs.keys(), records)
cf = CompactFactor(rawdata)
for variables in powerset(vs.keys()):
g = f.copy(copy_domain=True)
g.marginalise_away(g.variables() - frozenset(variables))
self.assert_(same_factor(g,cf.makeFactor(variables),verbose=True))
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))
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))
"""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.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)