def test_read_csv_and_anonymise(self):
from utils.read_adult_data import read_data as read_adult
DATA, INTUITIVE_ORDER = read_adult()
result, eval_result = mondrian(DATA, 40, False)
print(result)
print("K=%d" % k)
_, eval_result = clustering_based_k_anon(att_trees, data, type_alg, k)
data = copy.deepcopy(data_back)
print("NCP %0.2f" % eval_result[0] + "%")
all_ncp.append(round(eval_result[0], 2))
print("Running time %0.2f" % eval_result[1] + "seconds")
all_rtime.append(round(eval_result[1], 2))
print("All NCP", all_ncp)
print("All Running time", all_rtime)
if __name__ == '__main__':
print("Using Adult Dataset")
DATA = read_adult()
ATT_TREES = read_adult_tree()
TYPE_ALG = 'oka'
FLAG = ''
try:
FLAG = sys.argv[1]
except IndexError:
pass
if FLAG == 'k':
get_result_k(ATT_TREES, DATA, TYPE_ALG)
elif FLAG == 'n':
get_result_n(ATT_TREES, DATA, TYPE_ALG)
elif FLAG == '':
if __DEBUG:
cProfile.run('get_result_one(ATT_TREES, DATA, TYPE_ALG)')
if __name__ == '__main__':
FLAG = ''
LEN_ARGV = len(sys.argv)
try:
DATA_SELECT = sys.argv[1]
FLAG = sys.argv[2]
except:
pass
k = 10
if DATA_SELECT == 'i':
RAW_DATA = read_informs()
ATT_TREES = read_informs_tree()
else:
RAW_DATA = read_adult()
ATT_TREES = read_adult_tree()
print '#' * 30
if DATA_SELECT == 'a':
print "Adult data"
else:
print "INFORMS data"
print '#' * 30
if FLAG == 'k':
get_result_k(ATT_TREES, RAW_DATA)
elif FLAG == 'qi':
get_result_qi(ATT_TREES, RAW_DATA)
elif FLAG == 'data':
get_result_dataset(ATT_TREES, RAW_DATA)
elif FLAG == 'one':
if LEN_ARGV > 3:
RELAX = False
else:
RELAX = True
if RELAX:
print "Relax Mondrian"
else:
print "Strict Mondrian"
if DATA_SELECT == 'i':
print "INFORMS data"
DATA = read_informs()
else:
print "Adult data"
# INTUITIVE_ORDER is an intutive order for
# categorical attrbute. This order is produced
# by the reading (from dataset) order.
DATA, INTUITIVE_ORDER = read_adult()
if FLAG == 'k':
get_result_k(DATA)
elif FLAG == 'qi':
get_result_qi(DATA)
elif FLAG == 'data':
get_result_dataset(DATA)
elif FLAG == '':
get_result_one(DATA)
else:
try:
INPUT_K = int(FLAG)
get_result_one(DATA, INPUT_K)
except ValueError:
print "Usage: python anonymizer [r|s] [a | i] [k | qi | data]"
print "r: relax mondrian, s: strict mondrian"
RELAX = False
else:
RELAX = True
if RELAX:
print "Relax Mondrian"
else:
print "Strict Mondrian"
if DATA_SELECT == 'i':
print "INFORMS data"
DATA = read_informs()
else:
print "Adult data"
# INTUITIVE_ORDER is an intutive order for
# categorical attrbute. This order is produced
# by the reading (from dataset) order.
DATA, INTUITIVE_ORDER = read_adult()
if FLAG == 'k':
get_result_k(DATA)
elif FLAG == 'qi':
get_result_qi(DATA)
elif FLAG == 'data':
get_result_dataset(DATA)
elif FLAG == '':
get_result_one(DATA)
else:
try:
INPUT_K = int(FLAG)
get_result_one(DATA, INPUT_K)
except ValueError:
print "Usage: python anonymizer [r|s] [a | i] [k | qi | data]"
print "r: relax mondrian, s: strict mondrian"
if __name__ == '__main__':
FLAG = ''
LEN_ARGV = len(sys.argv)
try:
DATA_SELECT = sys.argv[1]
FLAG = sys.argv[2]
except:
pass
k = 10
if DATA_SELECT == 'i':
RAW_DATA = read_informs()
ATT_TREES = read_informs_tree()
else:
RAW_DATA = read_adult()
ATT_TREES = read_adult_tree()
print '#' * 30
if DATA_SELECT == 'a':
print "Adult data"
else:
print "INFORMS data"
print '#' * 30
if FLAG == 'k':
get_result_k(ATT_TREES, RAW_DATA)
elif FLAG == 'qi':
get_result_qi(ATT_TREES, RAW_DATA)
elif FLAG == 'data':
get_result_dataset(ATT_TREES, RAW_DATA)
elif FLAG == 'one':
if LEN_ARGV > 3:
LEN_ARGV = len(sys.argv)
try:
DATA_SELECT = sys.argv[1]
TYPE_ALG = 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()
if __DEBUG:
# DATA = DATA[:2000]
# print "Test anonymization with %d records" % len(DATA)
# ec_exam_by_dim(DATA)
# ec_exam_by_size_data(DATA)
# print ec_distribution(DATA)
# dim_ec_distribution(DATA)
# size_ec_distribution(DATA)
# pdb.set_trace()
print sys.argv
if FLAG == 'k':
get_result_k(ATT_TREES, DATA, TYPE_ALG)
elif FLAG == 'qi':
get_result_qi(ATT_TREES, DATA, TYPE_ALG)
