forked from qiyuangong/Clustering_based_K_Anon
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anonymizer.py
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/
anonymizer.py
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"""
run clustering_based_k_anon with given parameters
"""
# !/usr/bin/env python
# coding=utf-8
from clustering_based_k_anon import clustering_based_k_anon
from utils.read_adult_data import read_data as read_adult
from utils.read_adult_data import read_tree as read_adult_tree
from utils.read_informs_data import read_data as read_informs
from utils.read_informs_data import read_tree as read_informs_tree
import sys
import copy
import pdb
import random
import cProfile
DATA_SELECT = 'a'
TYPE_ALG = 'knn'
DEFAULT_K = 10
def get_result_one(att_trees, data, type_alg, k=DEFAULT_K):
"""
run clustering_based_k_anon for one time, with k=10
"""
print "K=%d" % k
data_back = copy.deepcopy(data)
_, eval_result = clustering_based_k_anon(att_trees, data, type_alg, k)
data = copy.deepcopy(data_back)
print "NCP %0.2f" % eval_result[0] + "%"
print "Running time %0.2f" % eval_result[1] + " seconds"
def get_result_k(att_trees, data, type_alg):
"""
change k, whle fixing QD and size of dataset
"""
data_back = copy.deepcopy(data)
for k in range(5, 55, 5):
print '#' * 30
print "K=%d" % k
result, eval_result = clustering_based_k_anon(att_trees, data, type_alg, k)
data = copy.deepcopy(data_back)
print "NCP %0.2f" % eval_result[0] + "%"
print "Running time %0.2f" % eval_result[1] + " seconds"
def get_result_dataset(att_trees, data, type_alg, k=DEFAULT_K, num_test=10):
"""
fix k and QI, while changing size of dataset
num_test is the test nubmber.
"""
data_back = copy.deepcopy(data)
length = len(data_back)
print "K=%d" % k
joint = 5000
datasets = []
check_time = length / joint
if length % joint == 0:
check_time -= 1
for i in range(check_time):
datasets.append(joint * (i + 1))
datasets.append(length)
all_ncp = []
all_rtime = []
for pos in datasets:
ncp = rtime = 0
print '#' * 30
print "size of dataset %d" % pos
for j in range(num_test):
temp = random.sample(data, pos)
_, eval_result = clustering_based_k_anon(att_trees, temp, type_alg, k)
ncp += eval_result[0]
rtime += eval_result[1]
data = copy.deepcopy(data_back)
ncp /= num_test
rtime /= num_test
print "Average NCP %0.2f" % ncp + "%"
print "Running time %0.2f" % rtime + " seconds"
print '#' * 30
def get_result_qi(att_trees, data, type_alg, k=DEFAULT_K):
"""
change nubmber of QI, whle fixing k and size of dataset
"""
data_back = copy.deepcopy(data)
num_data = len(data[0])
print "L=%d" % k
for i in reversed(range(1, num_data)):
print '#' * 30
print "Number of QI=%d" % i
_, eval_result = clustering_based_k_anon(att_trees, data, type_alg, k, i)
data = copy.deepcopy(data_back)
print "NCP %0.2f" % eval_result[0] + "%"
print "Running time %0.2f" % eval_result[1] + " seconds"
if __name__ == '__main__':
FLAG = ''
LEN_ARGV = len(sys.argv)
try:
TYPE_ALG = sys.argv[1]
DATA_SELECT = sys.argv[2]
FLAG = sys.argv[3]
except IndexError:
pass
INPUT_K = 5
# read record
if DATA_SELECT == 'i':
print "INFORMS data"
DATA = read_informs()
ATT_TREES = read_informs_tree()
else:
print "Adult data"
DATA = read_adult()
ATT_TREES = read_adult_tree()
# DATA = DATA[:2000]
if FLAG == 'k':
get_result_k(ATT_TREES, DATA, TYPE_ALG)
elif FLAG == 'qi':
get_result_qi(ATT_TREES, DATA, TYPE_ALG)
elif FLAG == 'data':
get_result_dataset(ATT_TREES, DATA, TYPE_ALG)
elif FLAG == '':
cProfile.run('get_result_one(ATT_TREES, DATA, TYPE_ALG)')
# get_result_one(ATT_TREES, DATA, TYPE_ALG)
else:
try:
INPUT_K = int(FLAG)
get_result_one(ATT_TREES, DATA, TYPE_ALG, INPUT_K)
except ValueError:
print "Usage: python anonymizer [knn | kmember] [a | i] [k | qi | data]"
print "a: adult dataset, i: INFORMS ataset"
print "k: varying k"
print "qi: varying qi numbers"
print "data: varying size of dataset"
print "example: python anonymizer a 5"
print "example: python anonymizer a k"
# anonymized dataset is stored in result
print "Finish Cluster based K-Anon!!"