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anonymizer.py
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anonymizer.py
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"""
run DA and AA with given parameters
"""
#!/usr/bin/env python
# coding=utf-8
from RT_ANON import rt_anon
from utils.read_informs_data import read_data as read_informs
from utils.read_informs_data import read_tree as read_informs_tree
from utils.read_youtube_data import read_data as read_youtube
from utils.read_youtube_data import read_tree as read_youtube_tree
from models.gentree import GenTree
from utils.maketree import gen_gh_trees
from utils.save_result import save_to_file
import sys
import copy
import random
import cProfile
import pdb
sys.setrecursionlimit(50000)
TYPE_ALG = 'RMR'
DEFAULT_M = 2
M_MAX = 161
DEFAULT_K = 10
DEFAULT_T = 0.65
def get_result_one(att_tree, data, type_alg, k=DEFAULT_K, m=DEFAULT_M, threshold=DEFAULT_T):
"""
run RT_ANON for one time, with k=10
"""
print "K=%d" % k
print "Size of Data", len(data)
print "m=%d" % m
print "Threshold=%.2f" % threshold
result, eval_result = rt_anon(att_tree, data, type_alg, k, m, threshold)
# save_to_file((att_tree, data, result, k, m))
print "RNCP %0.2f" % eval_result[0] + "%"
print "TNCP %0.2f" % eval_result[1] + "%"
print "Running time %0.2f" % eval_result[2] + " seconds"
def get_result_k(att_tree, data, type_alg, m=DEFAULT_M, threshold=DEFAULT_T):
"""
change k, whle fixing size of dataset
"""
data_back = copy.deepcopy(data)
# for k in range(5, 105, 5):
print "m=%d" % m
print "Threshold=%.2f" % threshold
print "Size of Data", len(data)
all_rncp = []
all_tncp = []
all_rtime = []
# for k in range(5, 55, 5):
# if k in [2, 5, 10, 25, 50, 100]:
# continue
k_range = [2, 5, 10, 25, 50, 100]
for k in k_range:
print '#' * 30
print "K=%d" % k
result, eval_result = rt_anon(att_tree, data, type_alg, k, m, threshold)
# save_to_file((att_tree, data, result, k, m))
data = copy.deepcopy(data_back)
print "RNCP %0.2f" % eval_result[0] + "%"
all_rncp.append(round(eval_result[0], 2))
print "TNCP %0.2f" % eval_result[1] + "%"
all_tncp.append(round(eval_result[1], 2))
print "Running time %0.2f" % eval_result[2] + " seconds"
all_rtime.append(round(eval_result[2], 2))
print "K range", k_range
print "RNCP", all_rncp
print "TNCP", all_tncp
print "Running time", all_rtime
def get_result_m(att_tree, data, type_alg, k=DEFAULT_K, threshold=DEFAULT_T):
"""
change k, whle fixing size of dataset
"""
print "K=%d" % k
print "Threshold=%.2f" % threshold
print "Size of Data", len(data)
data_back = copy.deepcopy(data)
# for m in range(1, 100, 5):
all_rncp = []
all_tncp = []
all_rtime = []
m_range = [1, 2, 3, 4, 5, M_MAX]
for m in m_range:
print '#' * 30
print "m=%d" % m
result, eval_result = rt_anon(att_tree, data, type_alg, k, m, threshold)
# save_to_file((att_tree, data, result, k, m))
data = copy.deepcopy(data_back)
print "RNCP %0.2f" % eval_result[0] + "%"
all_rncp.append(round(eval_result[0], 2))
print "TNCP %0.2f" % eval_result[1] + "%"
all_tncp.append(round(eval_result[1], 2))
print "Running time %0.2f" % eval_result[2] + " seconds"
all_rtime.append(round(eval_result[2], 2))
print "m range", m_range
print "RNCP", all_rncp
print "TNCP", all_tncp
print "Running time", all_rtime
def get_result_t(att_tree, data, type_alg, k=DEFAULT_K, m=DEFAULT_M):
"""
change k, whle fixing size of dataset
"""
print "K=%d" % k
print "m=%d" % m
print "Size of Data", len(data)
data_back = copy.deepcopy(data)
# for m in range(1, 100, 5):
all_rncp = []
all_tncp = []
all_rtime = []
t_range = [0.15, 0.25, 0.4, 0.65]
for t in t_range:
print '#' * 30
print "Threshold=%.2f" % t
result, eval_result = rt_anon(att_tree, data, type_alg, k, m, t)
# save_to_file((att_tree, data, result, k, m))
data = copy.deepcopy(data_back)
print "RNCP %0.2f" % eval_result[0] + "%"
all_rncp.append(round(eval_result[0], 2))
print "TNCP %0.2f" % eval_result[1] + "%"
all_tncp.append(round(eval_result[1], 2))
print "Running time %0.2f" % eval_result[2] + " seconds"
all_rtime.append(round(eval_result[2], 2))
print "threshold range", t_range
print "RNCP", all_rncp
print "TNCP", all_tncp
print "Running time", all_rtime
def get_result_dataset(att_tree, data, type_alg='RMR',
k=DEFAULT_K, m=DEFAULT_M, threshold=DEFAULT_T, num_test=10):
"""
fix k, while changing size of dataset
num_test is the test nubmber.
"""
print "K=%d" % k
print "m=%d" % m
print "Threshold=%.2f" % threshold
data_back = copy.deepcopy(data)
length = len(data_back)
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_rncp = []
all_tncp = []
all_rtime = []
for pos in datasets:
rncp = tncp = rtime = 0
if pos > length:
continue
print '#' * 30
print "size of dataset %d" % pos
for j in range(num_test):
temp = random.sample(data, pos)
result, eval_result = rt_anon(att_tree, temp, type_alg, k, m, threshold)
# save_to_file((att_tree, temp, result, k, m), number=j)
rncp += eval_result[0]
tncp += eval_result[1]
rtime += eval_result[2]
data = copy.deepcopy(data_back)
rncp /= num_test
tncp /= num_test
rtime /= num_test
print "RNCP %0.2f" % rncp + "%"
all_rncp.append(round(rncp, 2))
print "TNCP %0.2f" % tncp + "%"
all_tncp.append(round(tncp, 2))
print "Running time %0.2f" % rtime + " seconds"
all_rtime.append(round(rtime, 2))
print "Size of datasets", datasets
print "RNCP", all_rncp
print "TNCP", all_tncp
print "Running time", all_rtime
if __name__ == '__main__':
# set K=10 as default
FLAG = ''
DATA_SELECT = 'i'
# gen_even_BMS_tree(5)
try:
DATA_SELECT = sys.argv[1]
TYPE_ALG = sys.argv[2]
FLAG = sys.argv[3]
except IndexError:
pass
INPUT_K = 10
print "*" * 30
if DATA_SELECT == 'i':
print "INFORMS data"
DATA = read_informs()
# gen_gh_trees(DATA_SELECT)
ATT_TREES = read_informs_tree()
elif DATA_SELECT == 'y':
print "Youtube data"
DATA = read_youtube()
# gen_gh_trees(DATA_SELECT)
ATT_TREES = read_youtube_tree()
else:
print "INFORMS data"
DATA = read_informs()
# gen_gh_trees(DATA_SELECT)
ATT_TREES = read_informs_tree()
# read generalization hierarchy
# read record
# remove duplicate items
# DATA = DATA[:1000]
# for i in range(len(DATA)):
# if len(DATA[i]) <= 40:
# DATA[i] = list(set(DATA[i]))
# else:
# DATA[i] = list(set(DATA[i][:40]))
for i in range(len(DATA)):
DATA[i][-1] = list(set(DATA[i][-1]))
print "Begin to run", TYPE_ALG
print "*" * 10
# print "Begin Apriori based Anon"
if FLAG == 'k':
get_result_k(ATT_TREES, DATA, TYPE_ALG)
elif FLAG == 'm':
get_result_m(ATT_TREES, DATA, TYPE_ALG)
elif FLAG == 't':
get_result_t(ATT_TREES, DATA, TYPE_ALG)
elif FLAG == 'data':
k = DEFAULT_K
try:
k = int(sys.argv[4])
except:
pass
if k != DEFAULT_K:
get_result_dataset(ATT_TREES, DATA, TYPE_ALG, k)
else:
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 [k | m | data]"
print "k: varying k"
print "m: varying m"
print "data: varying size of dataset"
print "example: python anonymizer RMR 10"
print "example: python anonymizer RMT k"
# anonymized dataset is stored in result
print "Finish RT_ANON!!"