colors = ['r-', 'b--.', 'k-.', 'g:']
sys = 'sybilframe'
plt.figure(figsize=(3.5, 2.3))
plt.ylim(0, 1.1)
plt.suptitle('Sybilframe Edge Prior Influence - ' + str.upper(str(graph)[0]) +
str(graph)[1:],
weight='bold')
for enu, i in enumerate((0.1, 0.3, 0.6, 0.82)):
perTarAll = pickle.load(
open(
'../pickles/sybilEdgeProb/sybilEdgeProb{}PTar{}.p'.format(
i, graph), 'rb'))
perTar = perTarAll[0]
perTarAUC = getMergedAuc(perTar)
print(perTarAUC)
paras = perTarAll[1]
x = [x for x in (1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50)]
print(x)
print(list(perTarAUC['sybilframe'].values()))
plt.plot(x,
list(perTarAUC['sybilframe'].values()),
colors[enu],
label='FN = ' + str(i))
if graph == 'facebook':
plt.legend(loc='lower right')
plt.tight_layout()
f, axarr = plt.subplots(1, 4, figsize=(7.5, 2.3), sharey=True)
f.suptitle('Sybilframe Edge Prior Influence - ' +str.upper(str(graph)[0])+str(graph)[1:], weight='bold')
for enu, i in enumerate((0.1, 0.3, 0.6, 0.82)):
perTarAll = pickle.load(open('../pickles/sybilEdgeProb/sybilEdgeProb{}PTar{}.p'.format(i, graph), 'rb'))
perTar = perTarAll[0]
paras = perTarAll[1]
ranksPerTar = getMergedRanks(perTar)
""" plot random per"""
x_h = sorted(ranksPerTar[sys][0][:-(paras.numSybils)])
x_s = sorted(ranksPerTar[sys][0][-(paras.numSybils):])
perTarAuc = getMergedAuc(perTar)
print(list(perTarAuc['sybilframe'].values()))
scaler = prep.MinMaxScaler()
scaler.fit(sorted(ranksPerTar[sys][0]))
x_h = scaler.transform(x_h)
x_s = scaler.transform(x_s)
y_h = get_cdf(x_h)
y_s = get_cdf(x_s)
ind_row = lambda enu: 0 if enu in (0,1) else 1
ind_col = lambda enu: 0 if enu in (0, 2) else 1
from util.calc import getMergedAuc
import pickle
from matplotlib import pyplot as plt
from util import setMatplotlibPaper
graph = 'slashdot'
PRandAll = pickle.load(
open('../pickles/attackEdges/attackEdgesPRand{}.p'.format(graph), 'rb'))
PRandRes = PRandAll[0]
PRandAUC = getMergedAuc(PRandRes)
PRandParas = PRandAll[1]
PTarAll = pickle.load(
open('../pickles/attackEdges/attackEdgesPTar{}.p'.format(graph), 'rb'))
PTarRes = PTarAll[0]
PTarAUC = getMergedAuc(PTarRes)
PTarParas = PTarAll[1]
SRRandAll = pickle.load(
open('../pickles/attackEdges/attackEdgesSRRand{}.p'.format(graph), 'rb'))
SRRandRes = SRRandAll[0]
SRRandAUC = getMergedAuc(SRRandRes)
SRRandParas = SRRandAll[1]
PTarTwoPhaseAll = pickle.load(
open('../pickles/attackEdges/attackEdgesPTwoPhase{}.p'.format(graph),
'rb'))
PTarTwoPhaseRes = PTarTwoPhaseAll[0]
PTarTwoPhaseAUC = getMergedAuc(PTarTwoPhaseRes)
PTarTwoPhaseParas = PTarTwoPhaseAll[1]
for sys in ('integro', ):
f, axarr = plt.subplots(1, 3)
f.suptitle('Integro Victim Prior Influence - Peripheral Random',
fontsize=14,
weight='bold')
for enu, i in enumerate((0.1, 0.2, 0.4)):
print(" ### FP is {} ###".format(i))
SRall = pickle.load(
open('../pickles/victimProb/victimProb{}SRTar.p'.format(i), 'rb'))
Pall = pickle.load(
open('../pickles/victimProb/victimProb{}PTar.p'.format(i), 'rb'))
pRes = Pall[0]
pResAUC = getMergedAuc(pRes)
print(pResAUC)
print('auc peripheral')
print(list(pResAUC['integro'].values()))
parasP = Pall[1]
SrRes = SRall[0]
SrResAUC = getMergedAuc(SrRes)
print('auc SR')
print(list(SrResAUC['integro'].values()))
parasSR = SRall[1]
"""
x_h = sorted(ranksPerTar[sys][0][:-(paras.numSybils)])
x_s = sorted(ranksPerTar[sys][0][-(paras.numSybils):])
import pickle
from util.calc import get_cdf, getMergedRanks, getMergedAuc
graph = 'slashdot'
for i in (0.1, 0.2, 0.4):
resSR = pickle.load(
open('../pickles/victimProb/victimProb{}{}SRTar.p'.format(i, graph),
'rb'))[0]
resP = pickle.load(
open('../pickles/victimProb/victimProb{}{}PTar.p'.format(i, graph),
'rb'))[0]
SRAuc = getMergedAuc(resSR)
PAuc = getMergedAuc(resP)
print(SRAuc['integro'])
print(PAuc['integro'])
print('')