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clustering_based_k_anon.py
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"""
main module for cluster_based_k_anon
"""
#!/usr/bin/env python
#coding=utf-8
from models.numrange import NumRange
from models.gentree import GenTree
from utils.utility import get_num_list_from_str, cmp_str, qid_to_key
import random
import time
import operator
import pdb
__DEBUG = False
# att_tree store root node for each att
ATT_TREES = []
# databack store all reacord for dataset
LEN_DATA = 0
QI_LEN = 0
QI_RANGE = []
IS_CAT = []
# get_LCA, gen_result and NCP require huge running time, while most of the function are duplicate
# we can use cache to reduce the running time
LCA_CACHE = []
NCP_CACHE = {}
class Cluster(object):
"""Cluster is for cluster based k-anonymity
self.member: record list in cluster
self.gen_result: generlized value for one cluster
"""
def __init__(self, member, gen_result, information_loss=0.0):
self.information_loss = information_loss
self.member = member
self.gen_result = gen_result[:]
self.center = gen_result[:]
for i in range(QI_LEN):
if IS_CAT[i] is False:
self.center[i] = str(sum([float(t[i]) for t in self.member]) * 1.0 / len(self.member))
def add_record(self, record):
"""
add record to cluster
"""
self.member.append(record)
self.update_gen_result(record, record)
def update_cluster(self):
"""update cluster information when member is changed
"""
self.gen_result = cluster_generalization(self.member)
for i in range(QI_LEN):
if IS_CAT[i]:
self.center[i] = self.gen_result[i]
else:
self.center[i] = str(sum([float(t[i]) for t in self.member]) * 1.0 / len(self.member))
self.information_loss = len(self.member) * NCP(self.gen_result)
def update_gen_result(self, merge_gen_result, center, num=1):
"""
update gen_result and information_loss after adding record or merging cluster
:param merge_gen_result:
:return:
"""
self.gen_result = generalization(self.gen_result, merge_gen_result)
current_len = len(self.member)
for i in range(QI_LEN):
if IS_CAT[i]:
self.center[i] = self.gen_result[i]
else:
self.center[i] = str((float(self.center[i]) * (current_len - num) + float(center[i]) * num) / current_len)
self.information_loss = len(self.member) * NCP(self.gen_result)
def add_same_record(self, record):
"""
add record with same qid to cluster
"""
self.member.append(record)
def merge_cluster(self, cluster):
"""merge cluster into self and do not delete cluster elements.
update self.gen_result
"""
self.member.extend(cluster.member)
self.update_gen_result(cluster.gen_result, cluster.center, len(cluster))
def __getitem__(self, item):
"""
:param item: index number
:return: gen_result[item]
"""
return self.gen_result[item]
def __len__(self):
"""
return number of records in cluster
"""
return len(self.member)
def __str__(self):
return str(self.gen_result)
def r_distance(source, target):
"""
Return distance between source (cluster or record)
and target (cluster or record). The distance is based on
NCP (Normalized Certainty Penalty) on relational part.
If source or target are cluster, func need to multiply
source_len (or target_len).
"""
source_gen = source
target_gen = target
source_len = 1
target_len = 1
# check if target is Cluster
if isinstance(target, Cluster):
target_gen = target.gen_result
target_len = len(target)
# check if souce is Cluster
if isinstance(source, Cluster):
source_gen = source.gen_result
source_len = len(source)
if source_gen == target_gen:
return 0
gen = generalization(source_gen, target_gen)
# len should be taken into account
distance = (source_len + target_len) * NCP(gen)
return distance
def diff_distance(record, cluster):
"""
Return IL(cluster and record) - IL(cluster).
"""
gen_after = generalization(record, cluster.gen_result)
return NCP(gen_after) * (len(cluster) + 1) - cluster.information_loss
def NCP(record):
"""Compute NCP (Normalized Certainty Penalty)
when generate record to gen_result.
"""
ncp = 0.0
# exclude SA values(last one type [])
list_key = qid_to_key(record)
try:
return NCP_CACHE[list_key]
except KeyError:
pass
for i in range(QI_LEN):
# if leaf_num of numerator is 1, then NCP is 0
width = 0.0
if IS_CAT[i] is False:
try:
float(record[i])
except ValueError:
temp = record[i].split(',')
width = float(temp[1]) - float(temp[0])
else:
width = len(ATT_TREES[i][record[i]]) * 1.0
width /= QI_RANGE[i]
ncp += width
NCP_CACHE[list_key] = ncp
return ncp
def get_LCA(index, item1, item2):
"""Get lowest commmon ancestor (including themselves)"""
# get parent list from
if item1 == item2:
return item1
try:
return LCA_CACHE[index][item1 + item2]
except KeyError:
pass
parent1 = ATT_TREES[index][item1].parent[:]
parent2 = ATT_TREES[index][item2].parent[:]
parent1.insert(0, ATT_TREES[index][item1])
parent2.insert(0, ATT_TREES[index][item2])
min_len = min(len(parent1), len(parent2))
last_LCA = parent1[-1]
# note here: when trying to access list reversely, take care of -0
for i in range(1, min_len + 1):
if parent1[-i].value == parent2[-i].value:
last_LCA = parent1[-i]
else:
break
LCA_CACHE[index][item1 + item2] = last_LCA.value
return last_LCA.value
def generalization(record1, record2):
"""
Compute relational generalization result of record1 and record2
"""
gen = []
for i in range(QI_LEN):
if IS_CAT[i] is False:
split_number = []
split_number.extend(get_num_list_from_str(record1[i]))
split_number.extend(get_num_list_from_str(record2[i]))
split_number = list(set(split_number))
if len(split_number) == 1:
gen.append(split_number[0])
else:
split_number.sort(cmp=cmp_str)
gen.append(split_number[0] + ',' + split_number[-1])
else:
gen.append(get_LCA(i, record1[i], record2[i]))
return gen
def cluster_generalization(records):
"""
calculat gen_result of records(list) recursively.
Compute both relational gen_result for records (list).
"""
len_r = len(records)
gen = records[0]
for i in range(1, len_r):
gen = generalization(gen, records[i])
return gen
def find_best_knn(index, k, data):
"""key fuction of KNN. Find k nearest neighbors of record, remove them from data"""
dist_dict = {}
record = data[index]
# add random seed to cluster
for i, t in enumerate(data):
if i == index:
continue
dist = r_distance(record, t)
dist_dict[i] = dist
sorted_dict = sorted(dist_dict.iteritems(), key=operator.itemgetter(1))
knn = sorted_dict[:k - 1]
knn.append((index, 0))
record_index = [t[0] for t in knn]
elements = [data[t[0]] for t in knn]
gen = cluster_generalization(elements)
cluster = Cluster(elements, gen, k * NCP(gen))
# delete multiple elements from data according to knn index list
return cluster, record_index
def find_best_cluster_iloss(record, clusters):
"""residual assignment. Find best cluster for record."""
min_distance = 1000000000000
min_index = 0
best_cluster = clusters[0]
for i, t in enumerate(clusters):
distance = r_distance(record, t.gen_result)
if distance < min_distance:
min_distance = distance
min_index = i
best_cluster = t
# add record to best cluster
return min_index
def find_best_cluster_iloss_increase(record, clusters):
"""residual assignment. Find best cluster for record."""
min_diff = 1000000000000
min_index = 0
best_cluster = clusters[0]
for i, t in enumerate(clusters):
IF_diff = diff_distance(record, t)
if IF_diff < min_diff:
min_distance = IF_diff
min_index = i
best_cluster = t
# add record to best cluster
return min_index
def find_furthest_record(record, data):
"""
:param record: the latest record be added to cluster
:param data: remain records in data
:return: the index of the furthest record from r_index
"""
max_distance = 0
max_index = -1
for index in range(len(data)):
current_distance = r_distance(record, data[index])
if current_distance >= max_distance:
max_distance = current_distance
max_index = index
return max_index
def find_best_record_iloss_increase(cluster, data):
"""
:param cluster: current
:param data: remain dataset
:return: index of record with min diff on information loss
"""
# pdb.set_trace()
min_diff = 1000000000000
min_index = 0
for index, record in enumerate(data):
# IF_diff = diff_distance(record, cluster)
# IL(cluster and record) and |cluster| + 1 is a constant
# so IL(record, cluster.gen_result) is enough
IF_diff = diff_distance(record, cluster)
if IF_diff < min_diff:
min_diff = IF_diff
min_index = index
return min_index
def clustering_knn(data, k=25):
"""
Group record according to QID distance. KNN
"""
clusters = []
# randomly choose seed and find k-1 nearest records to form cluster with size k
while len(data) >= k:
index = random.randrange(len(data))
cluster, record_index = find_best_knn(index, k, data)
data = [t for i, t in enumerate(data[:]) if i not in set(record_index)]
clusters.append(cluster)
# residual assignment
while len(data) > 0:
t = data.pop()
cluster_index = find_best_cluster_iloss(t, clusters)
clusters[cluster_index].add_record(t)
return clusters
def clustering_kmember(data, k=25):
"""
Group record according to NCP. K-member
"""
clusters = []
# randomly choose seed and find k-1 nearest records to form cluster with size k
r_pos = random.randrange(len(data))
r_i = data[r_pos]
while len(data) >= k:
r_pos = find_furthest_record(r_i, data)
r_i = data.pop(r_pos)
cluster = Cluster([r_i], r_i)
while len(cluster) < k:
r_pos = find_best_record_iloss_increase(cluster, data)
r_j = data.pop(r_pos)
cluster.add_record(r_j)
clusters.append(cluster)
# pdb.set_trace()
# residual assignment
while len(data) > 0:
t = data.pop()
cluster_index = find_best_cluster_iloss_increase(t, clusters)
clusters[cluster_index].add_record(t)
return clusters
def adjust_cluster(cluster, residual, k):
center = cluster.center
dist_dict = {}
# add random seed to cluster
for i, t in enumerate(cluster.member):
dist = r_distance(center, t)
dist_dict[i] = dist
sorted_dict = sorted(dist_dict.iteritems(), key=operator.itemgetter(1))
need_adjust_index = [t[0] for t in sorted_dict[k:]]
need_adjust = [cluster.member[t] for t in need_adjust_index]
residual.extend(need_adjust)
# update cluster
cluster.member = [t for i, t in enumerate(cluster.member)
if i not in set(need_adjust_index)]
cluster.update_cluster()
def clustering_oka(data, k=25):
"""
Group record according to NCP. OKA: one time pass k-means
"""
clusters = []
can_clusters = []
less_clusters = []
# randomly choose seed and find k-1 nearest records to form cluster with size k
seed_index = random.sample(range(len(data)), len(data) / k)
for index in seed_index:
record = data[index]
can_clusters.append(Cluster([record], record))
data = [t for i, t in enumerate(data[:]) if i not in set(seed_index)]
# pdb.set_trace()
while len(data) > 0:
record = data.pop()
index = find_best_cluster_iloss(record, can_clusters)
can_clusters[index].add_record(record)
# pdb.set_trace()
residual = []
for cluster in can_clusters:
if len(cluster) < k:
less_clusters.append(cluster)
else:
if len(cluster) > k:
adjust_cluster(cluster, residual, k)
clusters.append(cluster)
while len(residual) > 0:
record = residual.pop()
if len(less_clusters) > 0:
index = find_best_cluster_iloss(record, less_clusters)
less_clusters[index].add_record(record)
if less_clusters[index] >= k:
clusters.append(less_clusters.pop(index))
else:
index = find_best_cluster_iloss(record, clusters)
clusters[index].add_record(record)
return clusters
def init(att_trees, data, QI_num=-1):
"""
init global variables
"""
global ATT_TREES, DATA_BACKUP, LEN_DATA, QI_RANGE, IS_CAT, QI_LEN, LCA_CACHE, NCP_CACHE
ATT_TREES = att_trees
QI_RANGE = []
IS_CAT = []
LEN_DATA = len(data)
LCA_CACHE = []
NCP_CACHE = {}
if QI_num <= 0:
QI_LEN = len(data[0]) - 1
else:
QI_LEN = QI_num
for i in range(QI_LEN):
LCA_CACHE.append(dict())
if isinstance(ATT_TREES[i], NumRange):
IS_CAT.append(False)
QI_RANGE.append(ATT_TREES[i].range)
else:
IS_CAT.append(True)
QI_RANGE.append(len(ATT_TREES[i]['*']))
def clustering_based_k_anon(att_trees, data, type_alg='knn', k=10, QI_num=-1):
"""
the main function of clustering based k-anon
"""
init(att_trees, data, QI_num)
result = []
start_time = time.time()
if type_alg == 'knn':
print "Begin to KNN Cluster based on NCP"
clusters = clustering_knn(data, k)
elif type_alg == 'kmember':
print "Begin to K-Member Cluster based on NCP"
clusters = clustering_kmember(data, k)
elif type_alg == 'oka':
print "Begin to OKA Cluster based on NCP"
clusters = clustering_oka(data, k)
else:
print "Please choose merge algorithm types"
print "knn | kmember"
return (0, (0, 0))
rtime = float(time.time() - start_time)
ncp = 0.0
for cluster in clusters:
final_result = []
for i in range(len(cluster)):
final_result.append(cluster.gen_result + [cluster.member[i][-1]])
result.extend(final_result)
ncp += cluster.information_loss
ncp /= LEN_DATA
ncp /= QI_LEN
ncp *= 100
if __DEBUG:
print "NCP=", ncp
return (result, (ncp, rtime))