def plot_random(self, dataDir, plotDir, files): output = plotDir pyf = PyFunctions() for x in range(len(files)): path = dataDir + files[x] try: f = open(path, 'r') except Exception, e: continue l = [map(float, line.split()) for line in f] if files[x] == 'cmp_NoCountermeasure.txt': label = "No countermeasure" marker = '.' elif files[x] == 'cmp_AdditiveNoise.txt': label = "Additive noise" marker = "*" elif files[x] == 'cmp_Transfiguration.txt': label = "Transfiguration" marker = "o" elif files[x] == 'cmp_KAnonymity.txt': label = "K anonymity" marker = "^" elif files[x] == 'cmp_KClustering.txt': label = "K clustering" marker = "D" plt.plot(l[0], pyf.normalize(pyf.average(l[1:])), label = label, marker = marker)
def plot(self, dataDir, plotDir, files):
output = plotDir
pyf = PyFunctions()
for x in range(len(files)):
path = dataDir + files[x]
try:
f = open(path, 'r')
except Exception, e:
continue
l = [map(float, line.split()) for line in f]
if files[x] == 'traddOff_KAnonymity.txt':
title = "Trade-off bar of k anonymity"
elif files[x] == 'traddOff_KClustering.txt':
title = "Trade-off bar of k clustering"
plt.figure()
plt.bar([k - 0.25 for k in l[0]],
pyf.normalize(l[1]),
0.5,
color='g')
plt.xticks(map(int, l[0]))
plt.yticks([0.1 * i for i in range(1, 12)])
plt.title(title)
plt.xlabel('k')
plt.ylabel('IC')
plt.tight_layout()
# plt.show()
plt.savefig(output + files[x].replace('txt', 'png'))
def plot(self, dataDir, plotDir, files):
n = len(files) # number of bar pairs needed
cmName = {
'NoCountermeasure': 'No countermeasure',
'AdditiveNoise': 'Additive noise',
'Transfiguration': 'Transfiguration',
'KAnonymity': 'K Anonymity',
'KClustering': 'K Clustering'
}
width = 0.35
pyf = PyFunctions()
ic_random = [] # hold ic value of random queries
ic_smart = [] # hold ic value of smart queries
ctm = []
for x in range(len(files)):
try:
path = dataDir + 'cmp_' + files[x] + '.txt'
f = open(path, 'r')
l = [map(float, line.split()) for line in f]
avgl = pyf.average(l[1:])
ic_random.append(avgl[len(avgl) - 1])
path = dataDir + 'cmp_smart_' + files[x] + '.txt'
f = open(path, 'r')
l = [map(float, line.split()) for line in f]
avgl = pyf.average(l[1:])
ic_smart.append(avgl[len(avgl) - 1])
ctm.append(cmName[files[x]])
except Exception, e:
continue
def plot_random(self, dataDir, plotDir, files):
output = plotDir
pyf = PyFunctions()
for x in range(len(files)):
path = dataDir + files[x]
try:
f = open(path, 'r')
except Exception, e:
continue
l = [map(float, line.split()) for line in f]
if files[x] == 'cmp_NoCountermeasure.txt':
label = "No countermeasure"
marker = '.'
elif files[x] == 'cmp_AdditiveNoise.txt':
label = "Additive noise"
marker = "*"
elif files[x] == 'cmp_Transfiguration.txt':
label = "Transfiguration"
marker = "o"
elif files[x] == 'cmp_KAnonymity.txt':
label = "K anonymity"
marker = "^"
elif files[x] == 'cmp_KClustering.txt':
label = "K clustering"
marker = "D"
plt.plot(l[0],
pyf.normalize(pyf.average(l[1:])),
label=label,
marker=marker)
def plot(self, dataDir, plotDir, files):
output = plotDir
pyf = PyFunctions()
for x in range(len(files)):
path = dataDir + files[x]
try:
f = open(path, 'r')
except Exception, e:
continue
plt.figure()
l = [map(float, line.split()) for line in f]
if files[x] == 'traddOff_smart_AdditiveNoise.txt':
title = "Trade-off curve of additive noise with smart queries"
xlabel = 'Noise level'
elif files[x] == 'traddOff_smart_Transfiguration.txt':
title = "Trade-off curve of transfiguration with smart queries"
xlabel = 'Number of sides'
plt.xticks(map(int, l[0]))
plt.plot(l[0], pyf.normalize(l[1]))
plt.legend()
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel('IC')
plt.tight_layout()
plt.savefig(output + files[x].replace('txt', 'png'))
def plot_random_smart(self, dataDir, plotDir, files): output = plotDir pyf = PyFunctions() for x in range(len(files)): path = dataDir + files[x] try: f = open(path, 'r') except Exception, e: continue l = [map(float, line.split()) for line in f] if files[x] == 'cmp_NoCountermeasure.txt': label = "Random queries" marker = '.' elif files[x] == 'cmp_smart_NoCountermeasure.txt': label = "Smart qeuries" marker = "*" plt.plot(l[0], pyf.normalize(pyf.average(l[1:])), label = label, marker = marker)
def plot_random_smart(self, dataDir, plotDir, files):
output = plotDir
pyf = PyFunctions()
for x in range(len(files)):
path = dataDir + files[x]
try:
f = open(path, 'r')
except Exception, e:
continue
l = [map(float, line.split()) for line in f]
if files[x] == 'cmp_NoCountermeasure.txt':
label = "Random queries"
marker = '.'
elif files[x] == 'cmp_smart_NoCountermeasure.txt':
label = "Smart qeuries"
marker = "*"
plt.plot(l[0],
pyf.normalize(pyf.average(l[1:])),
label=label,
marker=marker)
def plot(self, dataDir, plotDir, files):
output = plotDir
pyf = PyFunctions()
for x in range(len(files)):
path = dataDir + files[x]
try:
f = open(path, 'r')
except Exception, e:
continue
l = [map(float, line.split()) for line in f]
if files[x] == 'traddOff_smart_KAnonymity.txt':
title = "Trade-off bar of k anonymity with smart queries"
elif files[x] == 'traddOff_smart_KClustering.txt':
title = "Trade-off bar of k clustering with smart queries"
plt.figure()
plt.bar([k - 0.25 for k in l[0]], pyf.normalize(l[1]), 0.5, color = 'g')
plt.xticks(map(int, l[0]))
plt.yticks([0.1 * i for i in range(1, 12)])
plt.title(title)
plt.xlabel('k')
plt.ylabel('IC')
plt.tight_layout()
# plt.show()
plt.savefig(output + files[x].replace('txt', 'png'))
def plot(self, dataDir, plotDir, files):
n = len(files) # number of bar pairs needed
cmName = {'NoCountermeasure': 'No countermeasure', 'AdditiveNoise': 'Additive noise',
'Transfiguration': 'Transfiguration', 'KAnonymity': 'K Anonymity', 'KClustering': 'K Clustering'}
width = 0.35
pyf = PyFunctions()
ic_random = [] # hold ic value of random queries
ic_smart = [] # hold ic value of smart queries
ctm = []
for x in range(len(files)):
try:
path = dataDir + 'cmp_' + files[x] + '.txt'
f = open(path, 'r')
l = [map(float, line.split()) for line in f]
avgl = pyf.average(l[1:])
ic_random.append(avgl[len(avgl) - 1])
path = dataDir + 'cmp_smart_' + files[x] + '.txt'
f = open(path, 'r')
l = [map(float, line.split()) for line in f]
avgl = pyf.average(l[1:])
ic_smart.append(avgl[len(avgl) - 1])
ctm.append(cmName[files[x]])
except Exception, e:
continue