def testRatingsNoFeedback(self):
#Step 1 - generate simulated data
good_agent = {"range": [1,8], "values": [100,1000], "transactions": 10, "suppliers": 1, "consumers": 1}
bad_agent = {"range": [9,10], "values": [1,10], "transactions": 100, "suppliers": 1, "consumers": 1}
reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), 10, True, None, False)
#Step 2 - process simulated with reputaion engine in batch mode, grab results and check them
cmd = 'python reputation_simulate.py ../../bin testsim ./ transactions10_r_100_0.1.tsv users10.tsv 2018-01-01 2018-01-10 logarithm=False weighting=True norm=True default=0.5'
r = subprocess.check_output(cmd,shell=True)
lines = r.decode().splitlines()
self.assertEqual(lines[len(lines)-4],'0.9866744712267205')
self.assertEqual(lines[len(lines)-2],'0.9944481144111824')
def testRatingsNoFeedback(self):
print('Testing '+type(self.rs).__name__+' testRatingsNoFeedback')
#Step 1 - generate simulated data
good_agent = {"range": [1,8], "values": [100,1000], "transactions": 10, "suppliers": 1, "consumers": 1}
bad_agent = {"range": [9,10], "values": [1,10], "transactions": 100, "suppliers": 1, "consumers": 1}
reputation_simulate(good_agent,bad_agent, date(2018, 1, 1), 10, True, None, False, silent=True)
#Step 2 - process simulated with reputaion engine in batch mode, grab results and check them
cmd = 'python reputation_simulate.py ./bin testsim ./ transactions10_r_100_0.1.tsv users10.tsv 2018-01-01 2018-01-10 logratings=False weighting=True fullnorm=True default=0.5'
r = subprocess.check_output(cmd,shell=True)
lines = r.decode().splitlines()
#print(lines)
self.assertEqual(str(round(float(lines[len(lines)-4]),3)),'0.995')
self.assertEqual(str(round(float(lines[len(lines)-2]),3)),'0.984')
def testRatingsWithFeedback(self):
print('Testing '+type(self.rs).__name__+' testRatingsWithFeedback')
#Step 1 - generate simulated data with reputation feedback
good_agent = {"range": [1,8], "values": [100,1000], "transactions": 10, "suppliers": 1, "consumers": 1}
bad_agent = {"range": [9,10], "values": [1,10], "transactions": 100, "suppliers": 1, "consumers": 1}
reputation_simulate(good_agent,bad_agent, date(2018, 1, 1), 3, True, self.rs, False, silent=True)
#Step 2 - check reputations
r1 = self.rs.get_ranks_dict({'date':date(2018, 1, 1)})
r2 = self.rs.get_ranks_dict({'date':date(2018, 1, 2)})
#print(r1)
#print(r2)
#Checking good agents
assert r1['1'] > 60
assert r1['2'] > 60
assert r2['1'] > 60
assert r2['2'] > 60
#Checking bad agents
assert r1['9'] < 40
assert r1['10'] < 40
assert r2['9'] < 40
assert r2['10'] < 40
#Semi-healthy agent environment set
good_agent = {
"range": [1, 800],
"values": [100, 1000],
"transactions": 10,
"suppliers": suppliers,
"consumers": consumers
}
bad_agent = {
"range": [801, 1000],
"values": [5, 50],
"transactions": 100,
"suppliers": suppliers,
"consumers": consumers
}
"""
start = time.time()
rs = AigentsAPIReputationService('http://localtest.com:1288/', '*****@*****.**', 'q', 'a', False, 'test', True)
if rs is not None:
rs.set_parameters({'fullnorm':True,'weighting':True,'logratings':False})
reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), days, True, rs, verbose)
print("Time spent Aigents:",time.time()-start)
del rs
start = time.time()
rs = PythonReputationService()
if rs is not None:
rs.set_parameters({'fullnorm':True,'weighting':True,'logratings':False})
reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), days, True, rs, verbose)
print("Time spent Python:",time.time()-start)
del rs
"suppliers": suppliers,
"consumers": consumers
}
print('Good Agent:', str(good_agent))
print('Bad Agent :', str(bad_agent))
for sp in [364, 182, 92, 30]:
#for sp in [182,92,30,10]:
#for sp in [10,6,4,2]:
print('Scam period:', str(sp))
sip = sp / 2
print('No RS:', end=" ")
reputation_simulate(good_agent,
bad_agent,
datetime.date(2018, 1, 1),
days,
True,
None,
campaign=[sp, sip],
verbose=verbose)
print('Regular RS:', end=" ")
rs.set_parameters({
'fullnorm': True,
'weighting': False,
'logratings': False,
'denomination': False,
'unrated': False,
'default': 0.5,
'decayed': 0.5,
'ratings': 1.0,
'spendings': 0.0
good_agent = {
"range": [1, 8],
"values": [100, 1000],
"transactions": 10,
"suppliers": 1,
"consumers": 1
}
bad_agent = {
"range": [9, 10],
"values": [10, 100],
"transactions": 100,
"suppliers": 1,
"consumers": 1
}
reputation_simulate(good_agent, bad_agent, datetime.date(2018, 1, 1), 10, True,
rs)
#reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), 10, False, rs)
#Semi-healthy agent environment set
good_agent = {
"range": [1, 8],
"values": [100, 1000],
"transactions": 10,
"suppliers": 1,
"consumers": 1
}
bad_agent = {
"range": [9, 10],
"values": [5, 50],
"transactions": 100,
"suppliers": 1,