def get_result_K(att_trees, data, L=5):
"change K, while fixing L"
data_back = copy.deepcopy(data)
for K in range(5, 55, 5):
result = APA(att_trees[-1], data, K, L)
data = copy.deepcopy(data_back)
save_to_file((att_trees, data, result, K, L))
def get_result_L(att_trees, data, K=10):
"change L, while fixing K"
data_back = copy.deepcopy(data)
for L in range(5, 55, 5):
result = APA(att_trees[-1], data, K, L)
data = copy.deepcopy(data_back)
save_to_file((att_trees, data, result, K, L))
def get_result_K(att_trees, data, L=5):
"change K, while fixing L"
data_back = copy.deepcopy(data)
for K in range(5, 55, 5):
result = PAA(att_trees[-1], data, K, L)
data = copy.deepcopy(data_back)
save_to_file((att_trees, data, result, K, L))
def get_result_L(att_trees, data, K=10):
"change L, while fixing K"
data_back = copy.deepcopy(data)
for L in range(5, 55, 5):
result = PAA(att_trees[-1], data, K, L)
data = copy.deepcopy(data_back)
save_to_file((att_trees, data, result, K, L))
def get_result_m(att_tree, data, type_alg, k=DEFALUT_K, threshold=DEFALUT_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 = []
for m in [1, 2, 3, 4, 5, M_MAX]:
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 "RNCP", all_rncp
print "TNCP", all_tncp
print "Running time", all_rtime
def get_result_k(att_tree, data, type_alg, m=DEFALUT_M, threshold=DEFALUT_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
for k in [2, 5, 10, 25, 50, 100]:
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 "RNCP", all_rncp
print "TNCP", all_tncp
print "Running time", all_rtime
def get_result_one(att_tree, data, type_alg, k=DEFALUT_K, m=DEFALUT_M, threshold=DEFALUT_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_dataset(att_trees, data, K=10, L=5):
"fix k and l, while changign dataset size"
data_back = copy.deepcopy(data)
length = len(data_back)
joint = 5000
h = length / joint
if length % joint == 0:
h += 1
for i in range(1, h + 1):
pos = i * joint
if pos > length:
continue
result = APA(att_trees[-1], data[0:pos], K, L)
data = copy.deepcopy(data_back)
save_to_file((att_trees, data[0:pos], result, K, L))
def get_result_dataset(att_trees, data, K=10, L=5):
"fix k and l, while changign dataset size"
data_back = copy.deepcopy(data)
length = len(data_back)
joint = 5000
h = length / joint
if length % joint == 0:
h += 1
for i in range(1, h+1):
pos = i * joint
if pos > length:
continue
result = PAA(att_trees[-1], data[0:pos], K, L)
data = copy.deepcopy(data_back)
save_to_file((att_trees, data[0:pos], result, K, L))
def get_result_dataset(att_tree, data, type_alg='RMR',
k=DEFALUT_K, m=DEFALUT_M, threshold=DEFALUT_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
dataset_num = length / joint
if length % joint == 0:
dataset_num += 1
for i in range(1, dataset_num + 1):
pos = i * joint
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 "RNCP", all_rncp
print "TNCP", all_tncp
print "Running time", all_rtime
def get_result_one(att_trees, data, K=10, L=5):
"fix K=10, L=5"
data_back = copy.deepcopy(data)
result = APA(att_trees[-1], data, K, L)
data = copy.deepcopy(data_back)
save_to_file((att_trees, data, result, K, L))
def get_result_one(att_trees, data, K=10, L=5):
"fix K=10, L=5"
data_back = copy.deepcopy(data)
result = PAA(att_trees[-1], data, K, L)
data = copy.deepcopy(data_back)
save_to_file((att_trees, data, result, K, L))
