def main2():
f = open(sys.argv[1])
data = [[1,2,3,4,5],[1,2,3,4,5,6,7,7], [5,3,2,4,5,6]]*3
#boxNames = ["Gauss-Jordan", "Gauss-Jordan (confidence variant)", "Genie", "Naive"]
#print data
#boxNames = ["Gauss", "Naive", "SMILE"]
#boxNames = ["Gauss", "Naive"]
boxNames = ["Gauss", "Gauss(f)", "Naive"]
#boxColors = ['darkkhaki', 'indianred', 'forestgreen', 'royalblue' ]
boxColors = ['darkkhaki', '#aaaa00', 'indianred', 'forestgreen', 'royalblue' ]
labels = ["Size1","Size2","Size3"]
#make_boxplot(data, labels, boxColors, boxNames, title="Euclidian distance for network %s")
data = []
labels = []
subdatas = [[] for i in xrange(len(boxNames))]
for line in f.readlines():
line = line[:-1]
if line.startswith("CASE"):
_, count = line.split(' ')
if subdatas[0] != []:
data.extend(subdatas)
subdatas = [[] for i in xrange(len(boxNames))]
if int(count) == 0:
break
#labels.append(line)
labels.append("Size %s"%(count))
else:
results = [float(i) for i in line.split(' ')]
for j in xrange(len(boxNames)):
subdatas[j].append(results[j])
#print labels
#print data
make_boxplot(data, labels, boxColors, boxNames, title="Weighted Euclidian Distance (as defined for CPT) for #parents = %s"%(sys.argv[2]))
def main():
def eucl_dist(A, B):
assert not any(((a < 0.0 or a > 1.0) for a in A)), "Error in set A: %s"%(A)
assert not any(((b < 0.0 or b > 1.0) for b in B)), "Error in set B: %s"%(B)
assert len(A) == len(B), "sets differ in size %d vs %d" % (len(A), len(B))
return sqrt(sum((A[i] - B[i])**2 for i in range(len(A))))
def kl_dist(P, Q):
assert len(P) == len(Q)
suma = 0.0;
for i in range(len(P)):
q = Q[i]
if Q[i] < 10e-7:
q = 10e-7
suma += P[i]*log((P[i]/q),e);
return suma
def hellinger_dist(P, Q):
assert len(P) == len(Q)
assert all((Q[i]>=0 for i in xrange(len(Q)))), "%s"%(Q)
suma = sum(( (sqrt(P[i]) - sqrt(Q[i]))**2 for i in range(len(P)) ))
return sqrt(suma) * 0.7071067811865475 # * 1./sqrt(2)
# OLD PREGENERATED DATA
# filenames = [ "CancerOR_1k.txt", "CancerOR_2k.txt", "CancerOR_3k.txt", "CancerOR_4k.txt", "CancerOR_1100.txt", "CancerOR_1200.txt", "CancerOR_1300.txt", "CancerOR_1400.txt", "CancerOR_1500.txt", "CancerOR_1600.txt",
# "CancerOR_1800.txt", "CancerOR_1900.txt", "CancerOR_800.txt", "CancerOR_2500.txt", "CancerOR_2400.txt", "CancerOR_2300.txt", "CancerOR_2200.txt", "CancerOR_2100.txt", "CancerOR_3100.txt",
# "CancerOR_3300.txt", "CancerOR_3500.txt", "CancerOR_3700.txt", "CancerOR_3900.txt"]
# nums = [1000, 2000, 3000, 4000, 1100, 1200, 1300, 1400, 1500, 1600, 1800, 1900, 800, 2500, 2400, 2300, 2200, 2100, 3100, 3300, 3500, 3700, 3900]
#dist_func = hellinger_dist
dist_func = eucl_dist
cpp_generator = "./NoisyMAXSmile/generator"
cpp_learner = "./NoisyMAXSmile/smile_learner"
py_learner = "./noisyMAX.py"
naive_learner = "./naiveMAX.py"
def exec_command(command):
p = os.popen(command, "r")
output = ""
while 1:
line = p.readline()
if not line:
break
output += line
return output[:-1]
def makedict(text):
r_dict = {}
for line in text.split('\n')[1:]:
cut = line.split(' ')
val = float(cut[-1])
case = " ".join(cut[:-1])
r_dict[case] = val
return r_dict
def distance(A,B, dist_f):
valA = []
valB = []
for key, value in A.iteritems():
if key.endswith("True"):
valA.append(value)
valB.append(B[key])
return dist_f(valA, valB)
#print makedict(max_based2(real_or))
#real_or = [ [0.61, 0, 0.25, 0, 0.15, 0, 0.04, 0, 0.01,],
# [0.39, 1, 0.75, 0, 0.85, 0, 0.96, 0, 0.99,], ]
#real_or_dim = 5
#network = "./src/CancerOR.xdsl"
#network_cpt = "./src/CancerOR_CPT.xdsl"
real_or = [ [0.61, 0, 0.25, 0, 0.15, 0, 0.04, 0, 0.35, 0, 0.89, 0, 0.01,],
[0.39, 1, 0.75, 1, 0.85, 1, 0.96, 1, 0.65, 1, 0.11, 1, 0.99,], ]
real_or_dim = 7
network = "./src/OR_6.xdsl"
network_cpt = "./src/OR_6_CPT.xdsl"
print real_or
parent_dims = [2]*(real_or_dim - 1)
#labels = ["Smoker","Genetic","CoalWorker","BadDiet","LungCancer"]
labels = ["P%d"%(i) for i in range(1,7)] + ['C1',]
real_or_cpt = CPT(real_or, parent_dims=parent_dims, network_type=CPT.TYPE_NOISY_MAX, labels=labels, states = [["True","False"]]*real_or_dim).max_based()
#max_based_real = max_based(real_or, leakdef = LEAKDEF.HENRION)
#print max_based_real
#real_out = dict(real_or_cpt.print_raw())
real_out = makedict(real_or_cpt.print_raw())
#print real_out
#sety = zip(filenames, nums)
#dataset_file = "data/"+filename
#nums = range(800,5001,500)
nums = [1000, 3000, 10000, 50000, 100000]
#nums = [100, 1000, 10000, 100000]
#nums = [2000, 2500]
#nums = [500, 1000 , 1500, 2000, 2500]
labels = [ "%d records"%(num, ) for num in nums]
#labels = ["1k records", "3k records", "10k records", "50k records", "100k records"]
#boxNames = ["Gauss-Jordan elimination", "SMILE Noisy-MAX fitting"]
#boxColors = ['darkkhaki', 'forestgreen']
reps = 100
#sety = xrange(1000,5000,50)
sety = zip("A"*len(nums), nums)
dataset_file = "tmp/tmp_data.txt"
#network_generator_seed = 3333 # used for 1k-100k GJ vs naive
#network_generator_seed = 123456 # used for 500 - 2500 GJ vs naive
#network_generator_seed = 44444 # used for 500 - 2500 GJ vs smile
#network_generator_seed = 444445 # used for 1k -100k GJ vs GJFit
#network_generator_seed = 5555 # used for 500-2500 all
#network_generator_seed = 3355 # used for 1k-100k all
#network_generator_seed = 3357 # used for 1k-100k all
network_generator_seed = 123412333
rnd = random.Random()
rnd.seed(network_generator_seed)
data = []
for filename, i in sety:
subdata_gj = []
subdata_gjf = []
subdata_genie = []
subdata_naive = []
for t in xrange(reps):
seed = rnd.randint(1,10**6)
gen_command = "%s %d %s %d > %s" % (cpp_generator, i, network, seed, dataset_file)
#learn_command_em = "%s %s %s EM" %(cpp_learner, dataset_file, network_cpt)
learn_command_genie = "%s %s %s Smile" %(cpp_learner, dataset_file, network_cpt)
learn_command_GJ = "python %s %s GJ" %(py_learner, dataset_file)
learn_command_GJFit = "python %s %s GJFit" %(py_learner, dataset_file)
learn_command_naive = "python %s %s" %(naive_learner, dataset_file)
print "s = %d, t = %d"%(i,t,)
exec_command(gen_command)
#em_out = makedict(exec_command(learn_command_em))
GJ_out = makedict(exec_command(learn_command_GJ))
#print real_out, GJ_out
d_r_gj = distance(real_out, GJ_out, dist_func)
print "GJ", d_r_gj
GJFit_out = makedict(exec_command(learn_command_GJFit))
d_r_gjf = distance(real_out, GJFit_out, dist_func)
print "GJFit", d_r_gjf
print learn_command_genie
command_out = exec_command(learn_command_genie)
genie_out = makedict(command_out)
d_r_g = distance(real_out, genie_out, dist_func)
print "Genie", d_r_g
naive_out = makedict(exec_command(learn_command_naive))
d_r_naive = distance(real_out, naive_out, dist_func)
print "Naive", d_r_naive
subdata_gj.append(d_r_gj)
subdata_gjf.append(d_r_gjf)
subdata_genie.append(d_r_g)
subdata_naive.append(d_r_naive)
data.append(subdata_gj)
data.append(subdata_gjf)
data.append(subdata_genie)
data.append(subdata_naive)
boxNames = ["Gauss-Jordan", "Gauss-Jordan (confidence variant)", "Genie", "Naive"]
boxColors = ['darkkhaki', 'indianred', 'forestgreen', 'royalblue' ]
make_boxplot(data, labels, boxColors, boxNames, title="Euclidian distance for network %s"%(network,))
def main():
def eucl_dist(A, B):
return sqrt(sum((A[i] - B[i])**2 for i in range(len(A)) ))
def kl_dist(P, Q):
assert len(P) == len(Q)
suma = 0.0;
for i in range(len(P)):
q = Q[i]
if Q[i] < 10e-7:
q = 10e-7
suma += P[i]*log((P[i]/q),e);
return suma
def hellinger_dist(P, Q):
assert len(P) == len(Q)
assert all((Q[i]>=0 for i in xrange(len(Q)))), "%s"%(Q)
suma = sum(( (sqrt(P[i]) - sqrt(Q[i]))**2 for i in range(len(P)) ))
return sqrt(suma) * 0.7071067811865475 # * 1./sqrt(2)
filenames = [ "CancerOR_1k.txt", "CancerOR_2k.txt", "CancerOR_3k.txt", "CancerOR_4k.txt", "CancerOR_1100.txt", "CancerOR_1200.txt", "CancerOR_1300.txt", "CancerOR_1400.txt", "CancerOR_1500.txt", "CancerOR_1600.txt",
"CancerOR_1800.txt", "CancerOR_1900.txt", "CancerOR_800.txt", "CancerOR_2500.txt", "CancerOR_2400.txt", "CancerOR_2300.txt", "CancerOR_2200.txt", "CancerOR_2100.txt", "CancerOR_3100.txt",
"CancerOR_3300.txt", "CancerOR_3500.txt", "CancerOR_3700.txt", "CancerOR_3900.txt"]
nums = [1000, 2000, 3000, 4000, 1100, 1200, 1300, 1400, 1500, 1600, 1800, 1900, 800, 2500, 2400, 2300, 2200, 2100, 3100, 3300, 3500, 3700, 3900]
nums = range(800, 5001, 100) #+ [5000, 10000, 100000]
filenames = [ "CancerOR_%d.txt"%(n) for n in nums]
real_or = (0.61, 0.25, 0.15, 0.04, 0.01,)
X_plot = []
Y_plot = []
#dist_func = hellinger_dist
dist_func = eucl_dist
cpp_generator = "./NoisyMAXSmile/generator"
cpp_learner = "./NoisyMAXSmile/smile_learner"
py_learner = "./noisyMAX.py"
naive_learner = "./naiveMAX.py"
#network = "./src/CancerOR.xdsl"
#network_cpt = "./src/CancerOR_CPT.xdsl"
network = "./src/CancerOR2.xdsl"
network_cpt = "./src/CancerOR2_CPT.xdsl"
def exec_command(command):
p = os.popen(command, "r")
output = ""
while 1:
line = p.readline()
if not line:
break
output += line
return output[:-1]
def makedict(text):
r_dict = {}
for line in text.split('\n'):
cut = line.split(' ')
val = float(cut[-1])
case = " ".join(cut[:-1])
r_dict[case] = val
return r_dict
def distance(A,B, dist_f):
valA = []
valB = []
for key, value in A.iteritems():
if key.endswith("True"):
valA.append(value)
valB.append(B[key])
return dist_f(valA, valB)
#print makedict(max_based2(real_or))
real_out = makedict(max_based2(real_or))
#sety = zip(filenames, nums)
#dataset_file = "data/"+filename
#nums = range(800,5001,500)
nums = [1000, 3000, 10000, 50000, 100000]
#nums = [500, 1000 , 1500, 2000, 2500]
labels = [ "%d records"%(num, ) for num in nums]
#labels = ["1k records", "3k records", "10k records", "50k records", "100k records"]
#boxNames = ["Gauss-Jordan elimination", "SMILE Noisy-MAX fitting"]
#boxColors = ['darkkhaki', 'forestgreen']
reps = 100
#sety = xrange(1000,5000,50)
sety = zip("A"*len(nums), nums)
dataset_file = "tmp/tmp_data.txt"
#network_generator_seed = 3333 # used for 1k-100k GJ vs naive
#network_generator_seed = 123456 # used for 500 - 2500 GJ vs naive
#network_generator_seed = 44444 # used for 500 - 2500 GJ vs smile
#network_generator_seed = 444445 # used for 1k -100k GJ vs GJFit
#network_generator_seed = 5555 # used for 500-2500 all
network_generator_seed = 3355 # used for 1k-100k all
rnd = random.Random()
rnd.seed(network_generator_seed)
data = []
for filename, i in sety:
subdata_gj = []
subdata_gjf = []
subdata_smile = []
subdata_naive = []
for t in xrange(reps):
seed = rnd.randint(1,10**6)
gen_command = "%s %d %s %d > %s" % (cpp_generator, i, network, seed, dataset_file)
#learn_command_em = "%s %s %s EM" %(cpp_learner, dataset_file, network_cpt)
learn_command_smile = "%s %s %s Smile" %(cpp_learner, dataset_file, network_cpt)
learn_command_GJ = "python %s %s GJ" %(py_learner, dataset_file)
learn_command_GJFit = "python %s %s GJFit" %(py_learner, dataset_file)
learn_command_naive = "python %s %s" %(naive_learner, dataset_file)
print "s = %d, t = %d"%(i,t,)
exec_command(gen_command)
#em_out = makedict(exec_command(learn_command_em))
GJ_out = makedict(exec_command(learn_command_GJ))
d_r_gj = distance(real_out, GJ_out, dist_func)
print "GJ", d_r_gj
GJFit_out = makedict(exec_command(learn_command_GJFit))
d_r_gjf = distance(real_out, GJFit_out, dist_func)
print "GJFit", d_r_gjf
smile_out = makedict(exec_command(learn_command_smile))
d_r_g = distance(real_out, smile_out, dist_func)
print "SMILE", d_r_g
naive_out = makedict(exec_command(learn_command_naive))
d_r_naive = distance(real_out, naive_out, dist_func)
print "Naive", d_r_naive
subdata_gj.append(d_r_gj)
subdata_gjf.append(d_r_gjf)
subdata_smile.append(d_r_g)
subdata_naive.append(d_r_naive)
data.append(subdata_gj)
data.append(subdata_gjf)
data.append(subdata_smile)
data.append(subdata_naive)
boxNames = ["Gauss-Jordan", "Gauss-Jordan (confidence variant)", "SMILE", "Naive"]
boxColors = ['darkkhaki', 'indianred', 'forestgreen', 'royalblue' ]
make_boxplot(data, labels, boxColors, boxNames )
