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_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 _ic_discovery(self, separator):
n = 0
self._skel.complete(self._skel.vertices())
skel = self._skel
too_many_adjacent = True
while too_many_adjacent:
too_many_adjacent = False
# find a pair (x,y) such that the cardinality of the neighbourhood
# exceeds n
for x, y in pairs(self.variables()):
if x not in self._must_have and y not in self._must_have:
continue
n_x = set(skel.neighbours(x))
n_y = set(skel.neighbours(y))
if y not in n_x:
continue
if x not in n_y:
raise RuntimeError,'inconsistent neighbourhoods'
# separators must be potential ancestors of both variables.
# constrain the neighbourhood to contain only potential ancestors
cond = n_x & self.potential_ancestors(x)
cond |= n_y & self.potential_ancestors(y)
cond -= frozenset([x,y])
# if the neighbourhood is too small, try the next pair
if n > len(cond):
continue
# find an untested subset s of neighbours of x of cardinality n
for s in subsetn(tuple(cond), n):
s = frozenset(s)
self.num_tests += 1
# test for x _|_ y | s
if separator.separates(x, y, s):
# see if we can find a more probable separator
s = self.hill_climb_cond(separator,x,y,s,cond)
# record independence since found
self._add_independence(x,y,s)
skel.remove_line(x, y)
break
# increment required neighbourhood minimum size
n += 1
# see if we've found all the CIs
too_many_adjacent = False
for x in self.variables():
too_many_adjacent |= len(skel.neighbours(x)) > n
if too_many_adjacent:
break
def _add_undirected_independencies(self):
# global markov property:
# a _|_ b | s
# if s separates a and b
for va, vb in pairs(self._graph.vertices()):
if self._graph.is_neighbour(va,vb):
continue
a = frozenset([va])
b = frozenset([vb])
for cond in powerset(self._graph.vertices() - (a|b)):
cond = set(cond)
if self.has_independence(va, vb):
skip = False
for pc in self._ind[self._index(va,vb)]:
if pc <= cond:
skip = True
break
if skip:
continue
if self._graph.separates(a,b,cond):
self._add_independence(va,vb,cond)
def _add_directed_independencies(self):
# directed global markov property:
# a _|_ b | s
# where s separates a and b in the moralised graph of
# the smallest ancestral set of {a,b,s}
for va,vb in pairs(self._graph.vertices()):
if self._graph.is_parent(va,vb) or self._graph.is_parent(vb,va):
continue
a = frozenset([va])
b = frozenset([vb])
for cond in powerset(self._graph.vertices() - (a|b)):
cond = frozenset(cond)
if self.has_independence(va, vb):
skip = False
for pc in self._ind[self._index(va,vb)]:
if pc <= cond:
skip = True
break
if skip:
continue
g = self._graph.ancestral_adg(a | b | cond).moralise()
if g.separates(a,b,cond):
self._add_independence(va,vb,cond)
ensemble = OrderEnsemble(w,model.variables(), num_runs,
burnin = burnin,
max_potential_parents=20, max_parents_family=3,
max_best_families=4000, best_family_scale=log(10))
print 'run','optimal',
for i in xrange(num_runs):
print 'order_'+str(i),
print
samples = []
for i in xrange(num_samples):
esample = ensemble.sample(skip=sample_every)
print i,optimal_score,
for sample in esample:
print sample.score(),
print
samples.append(esample[0:2])
print
print 'blanket_score_x','blanket_score_y'
for i, j in pairs(model.variables()):
e_ij_x = sum([samples[k][0].markov_blanket_score(i,j) for k in xrange(num_samples)])/num_samples
e_ij_y = sum([samples[k][1].markov_blanket_score(i,j) for k in xrange(num_samples)])/num_samples
print e_ij_x, e_ij_y
e_ji_x = sum([samples[k][0].markov_blanket_score(j,i) for k in xrange(num_samples)])/num_samples
e_ji_y = sum([samples[k][1].markov_blanket_score(j,i) for k in xrange(num_samples)])/num_samples
print e_ji_x, e_ji_y
for i in xrange(num_runs):
print 'order_' + str(i),
print
samples = []
for i in xrange(num_samples):
esample = ensemble.sample(skip=sample_every)
print i, optimal_score,
for sample in esample:
print sample.score(),
print
samples.append(esample[0:2])
print
print 'blanket_score_x', 'blanket_score_y'
for i, j in pairs(model.variables()):
e_ij_x = sum([
samples[k][0].markov_blanket_score(i, j) for k in xrange(num_samples)
]) / num_samples
e_ij_y = sum([
samples[k][1].markov_blanket_score(i, j) for k in xrange(num_samples)
]) / num_samples
print e_ij_x, e_ij_y
e_ji_x = sum([
samples[k][0].markov_blanket_score(j, i) for k in xrange(num_samples)
]) / num_samples
e_ji_y = sum([
samples[k][1].markov_blanket_score(j, i) for k in xrange(num_samples)
]) / num_samples
print e_ji_x, e_ji_y
def _remove_indeps(self,ci):
# for each pair, test a _|_ b | s, for any s.
for (a,b) in pairs(ci.variables()):
if ci.has_independence(a,b):
# remove the edge a - b
self.remove_line(a,b)
