Newer
Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
import datetime
from data_integrate import *
log_path = dirname(__file__) + '/log/' + str(datetime.datetime.now().strftime(
'%Y-%m-%d')) + '_nezha.log'
logger = Logger(log_path, logging.DEBUG, __name__).getlog()
# def frequent_pattern_miner(event_sequences):
# """
# mining frequent pattern in event sequences (Discard)
# input:
# - event_sequences: event sequences belonging to the traces in time window, e.g., [[1,2,3],[2,3,4]]
# output:
# - pattern: frequent_pattern in the events, e.g., [['54', '29', '#SUP: 9'], ['54', '30', '#SUP: 9'], ['54', '32', '#SUP: 9']]
# """
# print(datetime.datetime.now())
# spmf_path = dirname(__file__) + "/spmf"
# spmf = Spmf("CM-SPAM", input_direct=event_sequences,
# output_filename="./spmf/SPAM.txt", arguments=[0.01, 2, 2], spmf_bin_location_dir=spmf_path, memory=8192)
# spmf.run()
# pattern = spmf.parse_output()
# print(pattern)
# print(datetime.datetime.now())
# return pattern
# def frequent_graph_miner(file_name, topk=30):
# """
# mining frequent graph in event graph
# input:
# - file_name: input filename e.g.,
# output:
# - pattern_list: frequent_child_graph_list [{'support': '519', 'node1': '180', 'node2': '264'}]
# """
# # print(datetime.datetime.now())
# spmf_path = dirname(__file__) + "/spmf"
# spmf = Spmf("TKG", input_filename=file_name,
# output_filename="./spmf/tkg.txt", arguments=[topk, 2, False, False, True], spmf_bin_location_dir=spmf_path, memory=8192)
# spmf.run()
# pattern_result = spmf.parse_output()
# # print(pattern_result)
# # print(datetime.datetime.now())
# pattern_list = []
# for i in range(0, len(pattern_result), 6):
# """ parse ['t # 29 * 519'], ['v 0 5'], ['v 1 265'], ['e 0 1 1'] """
# support = pattern_result[i][0].split(' ')[-1]
# node1 = pattern_result[i+1][0].split(' ')[-1]
# node2 = pattern_result[i+2][0].split(' ')[-1]
# pattern = {"support": support, "child_graph": node1 + "_" + node2}
# pattern_list.append(pattern)
# pattern_list.sort(key=lambda k: k['support'], reverse=True)
# return pattern_list
# def generate_tkg_input(event_graphs):
# """
# generate_tkg_input:
# :parameter
# event_graphs - graph list
# :return
# file_name - tkg input filename
# details see at https://www.philippe-fournier-viger.com/spmf/TKG.php
# t # 0
# v 0 10
# v 1 11
# e 0 1 20
# """
# file_name = dirname(__file__) + "/spmf/" + str(datetime.datetime.now().strftime(
# '%Y-%m-%d')) + "_tkg_input.txt"
# f = open(file_name, "w")
# graph_number = 0
# node_number = 0
# for graph in event_graphs:
# # write head
# graph_head = "t # " + str(graph_number) + "\r\n"
# f.write(graph_head)
# node_map = {}
# node_content = ""
# edge_content = ""
# for key in graph.adjacency_list.keys():
# if key.event not in node_map:
# node_map[key.event] = node_number
# node_content += "v " + \
# str(node_number) + " " + str(key.event) + "\r\n"
# node_number += 1
# for event in graph.adjacency_list[key]:
# if event.event not in node_map:
# node_map[event.event] = node_number
# node_content += "v " + \
# str(node_number) + " " + str(event.event) + "\r\n"
# node_number += 1
# edge_content += "e " + \
# str(node_map[key.event]) + " " + \
# str(node_map[event.event]) + " 1\r\n"
# f.write(node_content)
# f.write(edge_content)
# graph_number += 1
# f.write("\r\n")
# f.close()
# return file_name
def get_pattern_support(event_graphs):
result_support_dict = {}
total_pair = set()
for event_graph in event_graphs:
for key, value in event_graph.support_dict.items():
if key in total_pair:
result_support_dict[key] += value
else:
result_support_dict[key] = value
total_pair = total_pair | event_graph.pair_set
result_support_dict = dict(sorted(
result_support_dict.items(), key=lambda x: x[1], reverse=True))
return result_support_dict