def get_subplot_x_y(density_of_gt_file,undersampling):
d = zkl.load(density_of_gt_file)
x = []
y = []
for i in range(0,100):
g2 = bfutils.undersample(d[i]['gt'],undersampling) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][undersampling]['ms']) #add the time
return x,y
def get_subplot_x_y(density_of_gt_file, undersampling):
d = zkl.load(density_of_gt_file)
x = []
y = []
for i in range(0, 100):
g2 = bfutils.undersample(d[i]['gt'], undersampling) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][undersampling]['ms']) #add the time
return x, y
def ra_wrapper(fold, n=10, k=10):
scipy.random.seed()
l = {}
while True:
try:
g = bfutils.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),':', traversal.density(g2),':',
startTime = int(round(time.time() * 1000))
#s = ur.liteqclass(g2, verbose=False, capsize=CAPSIZE)
s = ur.eqclass(g2)
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 fastest_g(L):
x = []
y = []
for l in L:
d = zkl.load(l)
for i in range(0,100):
gs = bfutils.call_undersamples(d[i]['gt']) #this helps us determine how far u will go
for u in range(1,len(d[i]['solutions'])+1):
g2 = bfutils.undersample(d[i]['gt'],u) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][u]['ms']) #add the time
return x,map(lambda x: x/1000./60., y)
def gen_x_y(L):
x = []
y = []
for l in L:
d = zkl.load(l)
for i in range(0,100):
gs = bfutils.call_undersamples(d[i]['gt']) #this helps us determine how far u will go
for u in range(1,len(d[i]['solutions'])+1):
#for u in range(1,min([len(gs),4])):
g2 = bfutils.undersample(d[i]['gt'],u) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][u]['ms']) #add the time
return x,y
def fastest_g(L):
x = []
y = []
for l in L:
d = zkl.load(l)
for i in range(0, 100):
gs = bfutils.call_undersamples(
d[i]['gt']) #this helps us determine how far u will go
for u in range(1, len(d[i]['solutions']) + 1):
g2 = bfutils.undersample(d[i]['gt'], u) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][u]['ms']) #add the time
return x, map(lambda x: x / 1000. / 60., y)
def gen_x_y(L):
x = []
y = []
for l in L:
d = zkl.load(l)
for i in range(0, 100):
gs = bfutils.call_undersamples(
d[i]['gt']) #this helps us determine how far u will go
for u in range(1, len(d[i]['solutions']) + 1):
#for u in range(1,min([len(gs),4])):
g2 = bfutils.undersample(d[i]['gt'], u) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][u]['ms']) #add the time
return x, y
def fan_wrapper(fold, n=10, k=10):
scipy.random.seed()
curr_proc = current_process()
curr_proc.daemon = False
output = Queue()
while True:
try:
g = bfutils.ringmore(n, k)
gdens = traversal.density(g)
g2 = bfutils.increment_u(g, g)
#g2 = bfutils.undersample(g,2)
def inside_wrapper():
scipy.random.seed()
try:
startTime = int(round(time.time() * 1000))
s = traversal.v2g22g1(g2, capsize=CAPSIZE)
#s = traversal.backtrack_more2(g2, rate=2, capsize=CAPSIZE)
endTime = int(round(time.time() * 1000))
print "{:2}: {:8} : {:4} {:10} seconds".\
format(fold, round(gdens,3), len(s),
round((endTime-startTime)/1000.,3))
output.put({'gt': g, 'eq': s, 'ms': endTime - startTime})
except MemoryError:
print 'memory error...'
raise
pl = [Process(target=inside_wrapper) for x in range(INPNUM)]
for e in pl:
e.start()
while True:
if killall(pl): break
r = output.get()
except MemoryError:
print 'memory error... retrying'
for p in pl:
p.terminate()
p.join()
continue
break
for p in pl:
p.join()
return r
def fan_wrapper(fold,n=10,k=10):
scipy.random.seed()
curr_proc=current_process()
curr_proc.daemon=False
output = Queue()
while True:
try:
g = bfutils.ringmore(n,k)
gdens = traversal.density(g)
g2 = bfutils.increment_u(g,g)
#g2 = bfutils.undersample(g,2)
def inside_wrapper():
scipy.random.seed()
try:
startTime = int(round(time.time() * 1000))
s = traversal.v2g22g1(g2, capsize=CAPSIZE)
#s = traversal.backtrack_more2(g2, rate=2, capsize=CAPSIZE)
endTime = int(round(time.time() * 1000))
print "{:2}: {:8} : {:4} {:10} seconds".\
format(fold, round(gdens,3), len(s),
round((endTime-startTime)/1000.,3))
output.put({'gt':g,'eq':s,'ms':endTime-startTime})
except MemoryError:
print 'memory error...'
raise
pl = [Process(target=inside_wrapper) for x in range(INPNUM)]
for e in pl: e.start()
while True:
if killall(pl): break
r = output.get()
except MemoryError:
print 'memory error... retrying'
for p in pl:
p.terminate()
p.join()
continue
break
for p in pl: p.join()
return r
def wrapper(fold, n=10, dens=0.1, urate=URATE):
scipy.random.seed()
rate = urate
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[:, ::rate], pval=0.0001)
elif EST == 'svar':
g2 = lm.data2graph(data[:, ::rate])
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(bfu.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
}
NODES = 6
DENSITY = 0.2
UMAX = 6
REPEATS = 100
d = zkl.load('leibnitz_nodes_6_density_0.2_ra_.zkl')
x = []
y = []
for i in range(0, REPEATS):
gs = bfutils.call_undersamples(
d[i]['gt']) #this helps us determine how far u will go
for u in range(1, min([len(gs), UMAX])):
g2 = bfutils.undersample(d[i]['gt'], u) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][u]['ms']) #add the time
print len(x)
print len(y)
fig = plt.figure()
ax = plt.gca()
ax.scatter(x, y)
ax.set_yscale('log')
plt.xlabel('edge density of H')
plt.ylabel('log scale time')
plt.title('Number of Nodes: %s , Density: %s ,UMAX: %s' %
(NODES, DENSITY, UMAX))
plt.xlim(0, 1)
plt.show()
import sys
sys.path.append('./tools/')
import traversal, bfutils
import numpy as np
from ortools.constraint_solver import pywrapcp
U = 3 # undersampling rate: 1 means no undersampling
N = 4 # number of nodes
k = 1 # number of extra edges
solver = pywrapcp.Solver("MSL")
# generate a random graph and undersample
g = bfutils.ringmore(N,k)
gdens = traversal.density(g)
g2 = bfutils.undersample(g,U-1)
# undersampled edges
dedgeu = {}
bedgeu = {}
for i in range(N):
for j in range(N):
dedgeu[(i,j)] = 0
bedgeu[(i,j)] = 0
v = str(i+1)
w = str(j+1)
if w in g2[v]:
if (0,1) in g2[v][w]: dedgeu[(i,j)] = 1
if (2,0) in g2[v][w]: bedgeu[(i,j)] = 1
def wrapper(fold,n=10,dens=0.1, urate=URATE):
scipy.random.seed()
rate = urate
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[:,::rate], pval=0.0001)
elif EST=='svar':
g2 = lm.data2graph(data[:,::rate])
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(bfu.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}
import sys
sys.path.append('./tools/')
import traversal, bfutils
import numpy as np
from ortools.constraint_solver import pywrapcp
U = 2 # undersampling rate: 1 means no undersampling
N = 10 # number of nodes
k = 25 # number of extra edges
solver = pywrapcp.Solver("MSL")
# generate a random graph and undersample
g = bfutils.ringmore(N,k)
gdens = traversal.density(g)
g2 = bfutils.undersample(g,U-1)
# undersampled edges
dedgeu = {}
bedgeu = {}
for i in range(N):
for j in range(N):
dedgeu[(i,j)] = 0
bedgeu[(i,j)] = 0
v = str(i+1)
w = str(j+1)
if w in g2[v]:
if (0,1) in g2[v][w]: dedgeu[(i,j)] = 1
if (2,0) in g2[v][w]: bedgeu[(i,j)] = 1
