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BBMax_Accuracy.py
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BBMax_Accuracy.py
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#-------------------------------------------------------------------------------
# Name: BBMax_Accuracy.py
# Purpose: Implements FIH algorithm found in "A Transversal Hypergraph Approach for the Frequent Itemset Hiding Problem" by Stavropoulos et al.
# Author: Vasileios Kagklis
# Created: 11/09/2014
# Copyright: (c) Vasileios Kagklis
#-------------------------------------------------------------------------------
from __future__ import division, print_function
from time import clock
import cplex
from cplex import SparsePair
from math import ceil
from fim import apriori
from random import randrange
from myiolib import *
from SetOp import *
###################################################
def convert2frozen_m(f):
result = []
for itemset in f:
result.append(frozenset(itemset[0]))
return(result)
###################################################
def get_indices(lst, item):
for i, x in enumerate(lst):
if x == item:
yield i
###################################################
def BBMax_Accuracy_main(fname1, fname2, fname3, sup, mod_name):
change_raw_data = 0
lines,tid = readDataset(fname3)
abs_supp = ceil(sup*lines-0.5)
F = readLargeData(fname1)
S = minSet(readSensitiveSet(fname2))
start_time = clock()
SS = supersets(S, F.keys())
Rev_Fd = list(set(F) - SS)
Rev_pos_bord = convert2frozen_m(apriori(Rev_Fd, target = 'm', supp = float(0.0), conf=100))
rev_t = clock()-start_time
with open("positive_border.dat", "w") as f:
for itemset in Rev_pos_bord:
f.write(' '.join(list(itemset))+"\n")
start_time = clock()
sens_ind = []
for i in xrange(lines):
flag = True
for itemset in S:
if itemset.issubset(tid[i]):
sens_ind.append(i)
flag = False
break
if flag:
for itemset in Rev_pos_bord:
if itemset.issubset(tid[i]):
sens_ind.append(i)
break
sens_ind = list(set(sens_ind))
N = len(sens_ind)
cpx = cplex.Cplex()
cpx.set_results_stream(None)
cpx.objective.set_sense(cpx.objective.sense.minimize)
cpx.variables.add(obj = (1,)*N + (lines,)*len(Rev_pos_bord), lb =(0,)*(N+len(Rev_pos_bord)),
ub=(1,)*N+(cplex.infinity,)*len(Rev_pos_bord),
types=(cpx.variables.type.integer,)*(N+len(Rev_pos_bord)))
for itemset in S:
ind = []
cur_supp = 0
for i in xrange(N):
if itemset.issubset(tid[sens_ind[i]]):
ind.append(i)
cur_supp += 1
cpx.linear_constraints.add(lin_expr = [SparsePair(ind = ind, val=(1,)*len(ind))],
senses=["G"], rhs=[cur_supp - abs_supp + 1])
rpb_c = 0
for itemset in Rev_pos_bord:
ind = []
cur_supp = 0
for i in range(N):
if itemset.issubset(tid[sens_ind[i]]):
ind.append(i)
cur_supp += 1
ind.append(N+rpb_c)
rpb_c += 1
cpx.linear_constraints.add(lin_expr = [SparsePair(ind = ind, val=(1,)*(len(ind)-1)+(-1,))],
senses=["L"], rhs=[cur_supp - abs_supp])
cpx.parameters.mip.pool.relgap.set(0)
cpx.solve()
with open("Logfile.dat", "a") as log:
log.write("Dataset: "+fname3+" No. Sens.:"+str(len(S))+" Relaxed Constraints: "+
str(sum(i > 0 for i in map(int, cpx.solution.get_values())[N:])) +
"/"+str(len(Rev_pos_bord))+"\n"
)
if any([i for i in map(int, cpx.solution.get_values())[N:]]):
log.write("System would be infeasible!!\n")
print(Rev_pos_bord)
print(map(int, cpx.solution.get_values()))
for i in get_indices(map(int, cpx.solution.get_values())[0:N], 1):
temp_set = set()
for itemset in S:
if itemset.issubset(tid[sens_ind[i]]):
temp_set.add(itemset)
while len(temp_set) > 0:
item_dic = {}
for itemset in temp_set:
for item in itemset:
if item not in item_dic:
item_dic[item] = 0
item_dic[item] += 1
max_val = 0
for item, freq in item_dic.items():
if max_val < freq:
max_val = freq
element = frozenset([item])
if item_dic.values().count(max_val) > 1:
candidates = [frozenset([item]) for item, freq in item_dic.items() if freq==max_val]
element = candidates[randrange(0, len(candidates))]
tid[sens_ind[i]] = tid[sens_ind[i]] - element
change_raw_data += 1
for itemset in temp_set:
if element.issubset(itemset):
temp_set = temp_set - set([itemset])
cpx = None
F = None
Rev_Fd = None
exec_time = clock()-start_time
######----create out files-----######
out_file = open(mod_name+'_results.txt', 'w')
for i in xrange(lines):
k = ' '.join(sorted(tid[i]))
print(k, file = out_file)
out_file.close()
tid = None
return(rev_t, change_raw_data, exec_time)