def aprioriWorker(data_fname, sup, out_fname, conn):
try:
conn.send(Apriori.Apriori_main(data_fname, sup, out_fname)[1])
conn.close()
return
except MemoryError:
conn.send(-1)
conn.close()
return
except Exception as e:
conn.send((-1,e.message,))
conn.close()
return
def aprioriWorker(data_fname, sup, conn, flag=True):
try:
if flag:
Apriori.Apriori_main(data_fname, sup)
conn.send(0)
conn.close()
return
else:
conn.send(Apriori.Apriori_main(data_fname, sup)[1])
conn.close()
return
except MemoryError:
conn.send(-1)
conn.close()
return
except Exception as e:
conn.send((
-1,
e.message,
))
conn.close()
return
def metricWorker(fname, sanitized, sens, sup, conn):
Apriori_results_init = readLargeData(fname)
S = minSet(readSensitiveSet(sens))
SS = supersets(S, Apriori_results_init.keys())
r_fd = list(set(Apriori_results_init) - SS)
Apriori_results = Apriori.Apriori_main(sanitized, sup)[0]
side_effects = len(r_fd)-len(Apriori_results)
if side_effects<0:
conn.send((side_effects,0,))
conn.close()
return
else:
## a1 = 0.
## a2 = 0.
## for itemset in convert2frozen_m(apriori(r_fd, target='m', supp = float(0.0))):
## a1 += 1.0
## for itemset2 in convert2frozen_m(apriori(Apriori_results.keys(), target='m', supp = float(0.0))):
## if itemset == itemset2:
## a2 += 1.0
##
## Bd_rate = abs(round(float((a1-a2)/a1),2))
SumAll = 0
AbsDif = 0.0
for itemset in r_fd:
SumAll += Apriori_results_init[itemset]
if itemset in Apriori_results:
AbsDif += float(abs(Apriori_results_init[itemset] - Apriori_results[itemset]))
else:
AbsDif += float(Apriori_results_init[itemset])
if SumAll == 0:
inls = round(float(AbsDif), 3)
else:
inls = round(float(AbsDif/SumAll), 3)
conn.send((side_effects, inls,))
conn.close()
return
def metricWorker(fname, sanitized, sens, sup, conn):
Apriori_results_init = readLargeData(fname)
S = minSet(readSensitiveSet(sens))
SS = supersets(S, Apriori_results_init.keys())
r_fd = list(set(Apriori_results_init) - SS)
Apriori_results = Apriori.Apriori_main(sanitized, sup)[0]
side_effects = len(r_fd) - len(Apriori_results)
if side_effects < 0:
conn.send((
side_effects,
0,
))
conn.close()
return
else:
SumAll = 0
AbsDif = 0.0
for itemset in r_fd:
SumAll += Apriori_results_init[itemset]
if itemset in Apriori_results:
AbsDif += float(
abs(Apriori_results_init[itemset] -
Apriori_results[itemset]))
else:
AbsDif += float(Apriori_results_init[itemset])
if SumAll == 0:
inls = round(float(AbsDif), 3)
else:
inls = round(float(AbsDif / SumAll), 3)
conn.send((
side_effects,
inls,
))
conn.close()
return
