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))