def test__liteqclass(self):
u = 1
set_of_u_1 = zkl.load("{}/liteqclass_output_n_5_u_1.zkl".format(self._ABS_PATH))
g2 = bfutils.undersample(self._G, u)
s = ur.liteqclass(g2, verbose=False, capsize=1000)
self.assertEqual(s, set_of_u_1)
u = 2
set_of_u_2 = zkl.load("{}/liteqclass_output_n_5_u_2.zkl".format(self._ABS_PATH))
g2 = bfutils.undersample(self._G, u)
s = ur.liteqclass(g2, verbose=False, capsize=1000)
self.assertEqual(s, set_of_u_2)
def supergraphs_in_eq(g, g2, rate=1):
'''Find all supergraphs of g that are also in the same equivalence
class with respect to g2 and the rate.
Currently works only for bfu.undersample by 1
'''
if bfu.undersample(g, rate) != g2:
raise ValueError('g is not in equivalence class of g2')
s = set()
def addnodes(g, g2, edges):
if edges:
masks = []
for e in edges:
if ok2addanedge(e[0], e[1], g, g2, rate=rate):
masks.append(True)
else:
masks.append(False)
nedges = [edges[i] for i in range(len(edges)) if masks[i]]
n = len(nedges)
if n:
for i in range(n):
mask = addanedge(g, nedges[i])
s.add(g2num(g))
addnodes(g, g2, nedges[:i] + nedges[i + 1:])
delanedge(g, nedges[i], mask)
edges = gk.edgelist(gk.complement(g))
addnodes(g, g2, edges)
return s
def ok2addaVpath(e, p, g, g2, rate=2):
mask = addaVpath(g, e, p)
if not gk.isedgesubset(bfu.undersample(g, rate), g2):
cleanVedges(g, e, p, mask)
return False
cleanVedges(g, e, p, mask)
return True
def estOE(d):
gt = d['gt']['graph']
gt = bfu.undersample(gt, 1)
e = gk.OCE(d['estimate'], gt)
N = np.double(len(gk.edgelist(gt))) +\
np.double(len(gk.bedgelist(gt)))
return (e['directed'][0] + e['bidirected'][0]) / N
def simple_loops(g, u):
"""
iterator over the list of simple loops of graph g at the undersample rate u
"""
gx = graph2nx(num2CG(g2num(undersample(g, u)), len(g)))
for l in networkx.simple_cycles(gx):
yield l
def estCOE(d):
gt = d['gt']['graph']
gt = bfu.undersample(gt, 1)
e = gk.OCE(d['estimate'], gt)
n = len(gt)
N = np.double(n ** 2 + (n - 1) ** 2 / 2.0
- len(gk.edgelist(gt))
- len(gk.bedgelist(gt)))
return (e['directed'][1] + e['bidirected'][1]) / N
def unfoldplot(G, steps=7, repeats=5, gap=0.5, R=1, hg=0.1, wgap=0.7, name='AAA', stl=''):
u = 0
dbnprint(undersample(G, u), repeats, w_gap=wgap,
h_gap=hg, mname=name + str(u), type='hid', stl=stl)
print "\\node[left=" + str(gap) + "cm of " + name + str(u) + ",scale=0.7] (C) {"
print "\\begin{tikzpicture}"
cdbnprint(G, mtype='hid', bend=15, curve=5, R=R)
print "\\end{tikzpicture}"
print "};"
for u in range(1, steps):
dbnprint(undersample(G, u), repeats, w_gap=wgap, h_gap=hg, mname=name +
str(u), type='ahid', stl=', below=0.25cm of ' + name + str(u - 1))
print "\\node[left=" + str(gap) + "cm of " + name + str(u) + ",scale=0.7] () {"
print "\\begin{tikzpicture}"
cdbnprint(undersample(G, u), mtype='lahid', bend=15, curve=5, R=R)
print "\\end{tikzpicture}"
print "};"
emacs_vars()
def SM_converging(Gstar, G):
"""Gstar is the undersampled reference graph, while G is the starting
graph. The code searches over all undersampled version of G to
find all matches with Gstar
"""
compat = []
GG = G
Gprev = G
if G == Gstar:
return [0]
j = 1
G = undersample(GG, j)
while not (G == Gprev):
if Gstar == G:
compat.append(j)
j += 1
Gprev = G
G = undersample(GG, j)
return compat
def cdbnwrap(G, u, name='AAA', R=1, gap=0.5):
output = StringIO.StringIO()
print >>output, "\\node[right=" + str(gap) + "cm of " + name + str(u - 1)\
+ ",scale=0.7](" + name + str(u) + "){"
print >>output, "\\begin{tikzpicture}"
s = cdbnprint(undersample(G, u), mtype='lahid', bend=25, curve=10, R=R)
print >>output, s.getvalue()
s.close()
print >>output, "\\end{tikzpicture}"
print >>output, "};"
return output
def nodesearch(g, g2, order, inlist, s, cds, pool, pc):
if order:
if bfu.undersample(g, rate) == g2:
s.add(g2num(g))
if capsize and len(s) > capsize:
raise ValueError('Too many elements')
s.update(supergraphs_in_eq(g, g2, rate=rate))
return g
key = order[0]
if pc:
tocheck = [x for x in pc if x in cds[len(inlist) - 1][inlist[0]]]
else:
tocheck = cds[len(inlist) - 1][inlist[0]]
if len(order) > 1:
kk = order[1]
pc = predictive_check(g, g2, pool[len(inlist)],
c[edge_function_idx(kk)], kk)
else:
pc = set()
adder, remover, masker = f[edge_function_idx(key)]
checks_ok = c[edge_function_idx(key)]
for n in tocheck:
if not checks_ok(key, n, g, g2, rate=rate):
continue
masked = np.prod(masker(g, key, n))
if masked:
nodesearch(g, g2, order[1:], [n] + inlist, s, cds, pool, pc)
else:
mask = adder(g, key, n)
nodesearch(g, g2, order[1:], [n] + inlist, s, cds, pool, pc)
remover(g, key, n, mask)
elif bfu.undersample(g, rate) == g2:
s.add(g2num(g))
if capsize and len(s) > capsize:
raise ValueError('Too many elements')
return g
def nodesearch(g, g2, edges, s):
if edges:
if bfu.undersample(g, rate) == g2:
s.add(g2num(g))
if capsize and len(s) > capsize:
raise ValueError('Too many elements')
return g
e = edges[0]
for n in ln:
if (n, e) in single_cache:
continue
if not ok2addaVpath(e, n, g, g2, rate=rate):
continue
mask = addaVpath(g, e, n)
r = nodesearch(g, g2, edges[1:], s)
delaVpath(g, e, n, mask)
elif bfu.undersample(g, rate) == g2:
s.add(g2num(g))
if capsize and len(s) > capsize:
raise ValueError('Too many elements in eqclass')
return g
def wrapper_rate_agnostic(fold, n=10, k=10):
scipy.random.seed()
l = {}
while True:
try:
g = gk.ringmore(n, k) # random ring of given density
gs = bfutils.call_undersamples(g)
for u in range(1, min([len(gs), UMAX])):
g2 = bfutils.undersample(g, u)
print fold, ': ', traversal.density(g), ':',
startTime = int(round(time.time() * 1000))
s = ur.iteqclass(g2, verbose=False)
endTime = int(round(time.time() * 1000))
print len(s)
l[u] = {'eq': s, 'ms': endTime - startTime}
except MemoryError:
print 'memory error... retrying'
continue
break
return {'gt': g, 'solutions': l}
def ra_wrapper_preset(fold, glist=[]):
scipy.random.seed()
l = {}
while True:
try:
g = glist[fold]
gs = bfutils.call_undersamples(g)
for u in range(1, min([len(gs), UMAX])):
g2 = bfutils.undersample(g, u)
print fold, ': ', traversal.density(g), ':',
startTime = int(round(time.time() * 1000))
s = ur.liteqclass(g2, verbose=False, capsize=CAPSIZE)
endTime = int(round(time.time() * 1000))
print len(s), u
l[u] = {'eq': s, 'ms': endTime - startTime}
except MemoryError:
print 'memory error... retrying'
continue
break
return {'gt': g, 'solutions': l}
def addnodes(g, g2, edges):
if edges:
masks = []
for e in edges:
if ok2addanedge_(e[0], e[1], g, g2, rate=rate):
masks.append(True)
else:
masks.append(False)
nedges = [edges[i] for i in range(len(edges)) if masks[i]]
n = len(nedges)
if n:
for i in range(n):
mask = addanedge(g, nedges[i])
if bfu.undersample(g, rate) == g2:
s.add(g2num(g))
addnodes(g, g2, nedges[:i] + nedges[i + 1:])
delanedge(g, nedges[i], mask)
return s
else:
return noop
else:
return noop
def g_single(G, u, scale=0.7, R=1, gap=0.5, mtype="lahid", layout=None):
g = undersample(G, u)
return gsingle(g, scale=scale, R=R, gap=gap, mtype=mtype, layout=layout)
def cdbn_single(G, u, scale=0.7, R=1, gap=0.5, mtype="lahid"):
return cdbnsingle(undersample(G, u), scale=scale, R=R, gap=gap, mtype=mtype)
def ok2addanedge2(s, e, g, g2, rate=1):
mask = addanedge(g, (s, e))
value = bfu.undersample(g, rate) == g2
delanedge(g, (s, e), mask)
return value
def ok2addanedge_sub(s, e, g, g2, rate=1):
mask = addanedge(g, (s, e))
value = gk.isedgesubset(bfu.undersample(g, rate), g2)
delanedge(g, (s, e), mask)
return value
def test__call_undersample(self):
u = 1
g_u_1 = {'1': {'1': {(0, 1)},
'2': {(0, 1), (2, 0)},
'3': {(0, 1), (2, 0)},
'4': {(0, 1), (2, 0)},
'5': {(2, 0)}},
'2': {'1': {(0, 1), (2, 0)},
'2': {(0, 1)},
'3': {(0, 1), (2, 0)},
'4': {(0, 1), (2, 0)},
'5': {(0, 1), (2, 0)}},
'3': {'1': {(0, 1), (2, 0)},
'2': {(0, 1), (2, 0)},
'3': {(0, 1)},
'4': {(0, 1), (2, 0)},
'5': {(0, 1), (2, 0)}},
'4': {'1': {(0, 1), (2, 0)},
'2': {(0, 1), (2, 0)},
'3': {(0, 1), (2, 0)},
'4': {(0, 1)}},
'5': {'1': {(0, 1), (2, 0)}, '2': {(0, 1), (2, 0)}, '3': {(0, 1), (2, 0)}}}
g2 = bfutils.undersample(self._G, u)
self.assertEqual(g_u_1, g2)
u = 2
g_u_2 = {'1': {'1': {(0, 1)},
'2': {(0, 1), (2, 0)},
'3': {(0, 1), (2, 0)},
'4': {(0, 1), (2, 0)},
'5': {(0, 1), (2, 0)}},
'2': {'1': {(0, 1), (2, 0)},
'2': {(0, 1)},
'3': {(0, 1), (2, 0)},
'4': {(0, 1), (2, 0)},
'5': {(0, 1), (2, 0)}},
'3': {'1': {(0, 1), (2, 0)},
'2': {(0, 1), (2, 0)},
'3': {(0, 1)},
'4': {(0, 1), (2, 0)},
'5': {(0, 1), (2, 0)}},
'4': {'1': {(0, 1), (2, 0)},
'2': {(0, 1), (2, 0)},
'3': {(0, 1), (2, 0)},
'4': {(0, 1)},
'5': {(0, 1), (2, 0)}},
'5': {'1': {(0, 1), (2, 0)},
'2': {(0, 1), (2, 0)},
'3': {(0, 1), (2, 0)},
'4': {(0, 1), (2, 0)}}}
g2 = bfutils.undersample(self._G, u)
self.assertEqual(g_u_2, g2)
u = 4
g_u_4 = {'1': {'1': {(0, 1)},
'2': {(0, 1), (2, 0)},
'3': {(0, 1), (2, 0)},
'4': {(0, 1), (2, 0)},
'5': {(0, 1), (2, 0)}},
'2': {'1': {(0, 1), (2, 0)},
'2': {(0, 1)},
'3': {(0, 1), (2, 0)},
'4': {(0, 1), (2, 0)},
'5': {(0, 1), (2, 0)}},
'3': {'1': {(0, 1), (2, 0)},
'2': {(0, 1), (2, 0)},
'3': {(0, 1)},
'4': {(0, 1), (2, 0)},
'5': {(0, 1), (2, 0)}},
'4': {'1': {(0, 1), (2, 0)},
'2': {(0, 1), (2, 0)},
'3': {(0, 1), (2, 0)},
'4': {(0, 1)},
'5': {(0, 1), (2, 0)}},
'5': {'1': {(0, 1), (2, 0)},
'2': {(0, 1), (2, 0)},
'3': {(0, 1), (2, 0)},
'4': {(0, 1), (2, 0)},
'5': {(0, 1)}}}
g2 = bfutils.undersample(self._G, u)
self.assertEqual(g_u_4, g2)
def SM_fixed(Gstar, G, iter=5):
compat = []
for j in range(0, iter):
if Gstar == undersample(G, j):
compat.append(j)
return compat
def wrapper(fold, n=10, dens=0.1):
scipy.random.seed()
rate = 2
r = None
s = set()
counter = 0
while not s:
scipy.random.seed()
sst = 0.9
r = None
while not r:
r = lm.getAring(n, dens, sst, False, dist=DIST)
print sst,
sys.stdout.flush()
if sst < 0.03:
sst -= 0.001
else:
sst -= 0.01
if sst < 0:
sst = 0.02
# pprint.pprint(r['transition'].round(2),width=200)
# d = zkl.load('leibnitz_nodes_'+str(n)+'_OCE_model_.zkl')
# r = d[dens][fold]
g = r['graph']
true_g2 = bfu.undersample(g, rate - 1)
data = lm.drawsamplesLG(r['transition'], samples=BURNIN + SAMPLESIZE * 2,
nstd=np.double(NOISE_STD))
data = data[:, BURNIN:]
if np.max(data) > 1000.:
pprint.pprint(r['transition'].round(2), width=200)
# raise ValueError
startTime = int(round(time.time() * 1000))
if EST == 'pc':
g2 = pc.dpc(data[:, ::2], pval=0.0001)
elif EST == 'svar':
g2 = lm.data2graph(data[:, ::2])
if trv.density(g2) < 0.7:
print gk.OCE(g2, true_g2)
# s = examine_bidirected_flips(g2, depth=DEPTH)
s = find_nearest_reachable(g2, max_depth=1)
# s = trv.v2g22g1(g2, capsize=CAPSIZE, verbose=False)
# s = trv.edge_backtrack2g1_directed(g2, capsize=CAPSIZE)
# s = timeout(trv.v2g22g1,
# s = timeout(trv.edge_backtrack2g1_directed,
# args=(g2,CAPSIZE),
# timeout_duration=1000, default=set())
print 'o',
sys.stdout.flush()
if -1 in s:
s = set()
endTime = int(round(time.time() * 1000))
# if counter > 3:
# print 'not found'
# return None
counter += 1
print ''
oce = [gk.OCE(num2CG(x, n), g) for x in s]
cum_oce = [sum(x['directed']) + sum(x['bidirected']) for x in oce]
idx = np.argmin(cum_oce)
print "{:2}: {:8} : {:4} {:10} seconds".\
format(fold, round(dens, 3), cum_oce[idx],
round((endTime - startTime) / 1000., 3))
# np.set_printoptions(formatter={'float': lambda x: format(x, '6.3f')+", "})
# pprint.pprint(r['transition'].round(2))
# np.set_printoptions()
return {'gt': r,
'eq': s,
'OCE': oce[idx],
'tries_till_found': counter,
'estimate': g2,
'graphs_tried': counter,
'strength': sst + 0.01,
'ms': endTime - startTime}
def backtrack_more(g2, rate=1, capsize=None):
'''
computes all g1 that are in the equivalence class for g2
'''
if bfu.isSclique(g2):
print 'Superclique - any SCC with GCD = 1 fits'
return set([-1])
single_cache = {}
if rate == 1:
ln = [n for n in g2]
else:
ln = []
for x in itertools.combinations_with_replacement(g2.keys(), rate):
ln.extend(itertools.permutations(x, rate))
ln = set(ln)
@memo # memoize the search
def nodesearch(g, g2, edges, s):
if edges:
if bfu.undersample(g, rate) == g2:
s.add(g2num(g))
if capsize and len(s) > capsize:
raise ValueError('Too many elements')
return g
e = edges[0]
for n in ln:
if (n, e) in single_cache:
continue
if not ok2addaVpath(e, n, g, g2, rate=rate):
continue
mask = addaVpath(g, e, n)
r = nodesearch(g, g2, edges[1:], s)
delaVpath(g, e, n, mask)
elif bfu.undersample(g, rate) == g2:
s.add(g2num(g))
if capsize and len(s) > capsize:
raise ValueError('Too many elements in eqclass')
return g
# find all directed g1's not conflicting with g2
n = len(g2)
edges = gk.edgelist(g2)
random.shuffle(edges)
g = cloneempty(g2)
for e in edges:
for n in ln:
mask = addaVpath(g, e, n)
if not gk.isedgesubset(bfu.undersample(g, rate), g2):
single_cache[(n, e)] = False
delaVpath(g, e, n, mask)
s = set()
try:
nodesearch(g, g2, edges, s)
except ValueError:
s.add(0)
return s
