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evaluateLogParse.py
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#!/usr/bin/env python
#coding=utf-8
import sys
from globalConfig import *
sys.path.append(ALGORITHM_PATH)
import IPLoM as iplom
import LogSig as logsig
import LKE.LKE as lke
import Spell as spell
import Drain as drain
import MoLFI.MoLFI as molfi
### use subprocess to run ft_tree instead
# sys.path.append('ft_tree/')
# import ft_tree
# from matchTemplate import *
from RI_precision import *
from globalConfig import *
from numpy import *
import numpy as np
import time
import os
import shutil
import argparse
import subprocess
def createDir(path, removeflag=0):
if removeflag == 1:
shutil.rmtree(path)
isExists = os.path.exists(path)
if not isExists:
os.makedirs(path)
def evaluateMethods(dataset, algorithm, leaf_num = 30, logname='rawlog.log', choose='all', ratio=0.5, eval_flag=1):
if choose == 'all':
dataPath = os.path.join(DATA_PATH, '%s_all/'%dataset)
dataName = '%s_all'%dataset
else:
dataPath = os.path.join(DATA_PATH, '%s_%s_%0.2f/'%(dataset,choose,ratio))
dataName = '%s_%s_%0.2f'%(dataset,choose,ratio)
groupNum = int(GroupNum[dataset][choose]*0.5) # caution!
removeCol=[] # caution!
result = np.zeros((1,9))
#####LogSig##############
if algorithm == "LogSig":
print('dataset:',logname)
t1=time.time()
parserPara = logsig.Para(path=dataPath, logname=logname, groupNum=groupNum, removeCol=removeCol, rex=regL[dataset], savePath=RESULT_PATH+algorithm+'_results/'+dataName+'/')
myParser = logsig.LogSig(parserPara)
runningTime = myParser.mainProcess()
t2=time.time()
# print 'cur_result_path:','./results/LogSig_results/' + dataName+'/'
# createDir('./results/LogSig_results/' + dataName+'/' + dataName,1)
if eval_flag:
parameters = prePara(groundTruthDataPath=dataPath, logName=logname, geneDataPath=RESULT_PATH+algorithm+'_results/'+dataName+'/')
TP,FP,TN,FN,p,r,f,RI = process(parameters)
else: print('No evaluation')
print ('dataset:', logname)
print ('training time: %0.3f'%(t2-t1))
#####Spell################
if algorithm == "Spell":
print('dataset:', logname)
t1=time.time()
parser = spell.LogParser(indir=dataPath, outdir=RESULT_PATH+algorithm+'_results/'+dataName+'/', log_format='<Content>', tau=0.5, rex=regL[dataset])
parser.parse(logname)
t2=time.time()
if eval_flag:
parameters=prePara(groundTruthDataPath=dataPath ,logName = logname , geneDataPath=RESULT_PATH+algorithm+'_results/' + dataName+'/')
TP,FP,TN,FN,p,r,f,RI=process(parameters)
else: print('No evaluation')
print ('dataset:', logname)
print ('training time: %0.3f'%(t2-t1))
#####Drain################
if algorithm == "Drain":
print('dataset:', logname)
t1=time.time()
parser = drain.LogParser(indir=dataPath, outdir=RESULT_PATH+algorithm+'_results/'+dataName+'/', log_format='<Content>', st=0.5, depth=4, rex=regL[dataset])
parser.parse(logname)
t2=time.time()
if eval_flag:
parameters=prePara(groundTruthDataPath=dataPath ,logName = logname , geneDataPath=RESULT_PATH+algorithm+'_results/' + dataName+'/')
TP,FP,TN,FN,p,r,f,RI=process(parameters)
else: print('No evaluation')
print ('dataset:', logname)
print ('training time: %0.3f'%(t2-t1))
#####MoLFI################
if algorithm == "MoLFI":
print('dataset:', logname)
t1=time.time()
parser = molfi.LogParser(indir=dataPath, outdir=RESULT_PATH+algorithm+'_results/'+dataName+'/', log_format='<Content>', rex=regL[dataset])
parser.parse(logname)
t2=time.time()
if eval_flag:
parameters=prePara(groundTruthDataPath=dataPath ,logName = logname , geneDataPath=RESULT_PATH+algorithm+'_results/' + dataName+'/')
TP,FP,TN,FN,p,r,f,RI=process(parameters)
else: print('No evaluation')
print ('dataset:', logname)
print ('training time: %0.3f'%(t2-t1))
#####FT_tree##############
if algorithm == "FT_tree":
#training
t1=time.time()
log_path = dataPath+logname
createDir(RESULT_PATH+"FT_tree_results/"+dataName+'/',0)
template_path = RESULT_PATH+"FT_tree_results/"+dataName+'/' # + "logTemplate.txt"
## leaf_num = 5
#ft_tree.getLogsAndSave(log_path, template_path + "/logTemplate.txt" , leaf_num)
##matching
#matchTemplatesAndSave(log_path,template_path)
out_seq_path = os.path.join(template_path, "matchTemplates.seq")
templates = os.path.join(template_path, "logTemplates.txt")
fre_word_path = os.path.join(template_path, "output.fre")
middle_templates = os.path.join(template_path, "output.template_middle")
sub_args = [
os.path.join(ALGORITHM_PATH, "./ft_tree/main_train.py"),
"-train_log_path", log_path,
"-out_seq_path", out_seq_path,
"-templates", templates,
"-fre_word_path", fre_word_path,
"-middle_templates", middle_templates,
"-short_threshold", "1",
]
subprocess.run(sub_args, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
t2=time.time()
# fix the format
for i in glob(os.path.join(template_path, "template[0-9]*.txt")):
os.remove(i)
match_lines = open(out_seq_path, "r").readlines()
for i in range(len(match_lines)):
template_index_str = match_lines[i].strip()
assert int(template_index_str) > 0, "%d: %s"%(i, template_index_str)
template_file = os.path.join(template_path, "template%s.txt"%template_index_str)
with open(template_file, "a") as f:
f.write(str(i+1)+"\n")
#evaluation
if eval_flag:
parameters = prePara(groundTruthDataPath=dataPath, logName=logname, geneDataPath=RESULT_PATH+"FT_tree_results/"+dataName+'/')
TP,FP,TN,FN,p,r,f,RI=process(parameters)
else: print('No evaluation')
print ('dataset:', logname)
print ('training time: %0.3f'%(t2-t1))
#######LKE##############
if algorithm == "LKE":
print ('dataset:',logname, "LKE")
t1=time.time()
# parserPara = lke.Para(path=dataPath, dataName='', logname = logname, removeCol=removeCol, rex=regL, savePath='./results/'+algorithm+'_results/' + dataName+'/')
# print ('parserPara.path',parserPara.path)
# myParser = lke.LKE(parserPara)
# runningTime = myParser.mainProcess()
parser = lke.LogParser(log_format='<Content>', indir=dataPath, outdir=RESULT_PATH+algorithm+'_results/'+dataName+'/', rex=regL[dataset], split_threshold=3)
parser.parse(logname)
t2=time.time()
# print 'cur_result_path:','./results/LogSig_results/' + dataName+'/'
#createDir('./results/LKE_results/' + dataName+'/' + dataName,1)
if eval_flag:
parameters=prePara(groundTruthDataPath=dataPath ,logName = logname , geneDataPath=RESULT_PATH+algorithm+'_results/' + dataName+'/')
TP,FP,TN,FN,p,r,f,RI=process(parameters)
else: print('No evaluation')
print ('dataset:', logname)
print ('training time: %0.3f'%(t2-t1))
#######IPLoM############
if algorithm == "IPLoM":
print ('dataset:',logname, "IPLoM")
t1=time.time()
parserPara = iplom.Para(path=dataPath, logname = logname,removeCol=removeCol, rex=regL[dataset], savePath=RESULT_PATH+algorithm+'_results/' + dataName+'/')
print ('parserPara.path',parserPara.path)
myParser = iplom.IPLoM(parserPara)
runningTime = myParser.mainProcess()
t2=time.time()
# print 'cur_result_path:','./results/LogSig_results/' + dataName+'/'
#createDir('./results/LKE_results/' + dataName+'/' + dataName,1)
if eval_flag:
parameters=prePara(groundTruthDataPath=dataPath ,logName = logname , geneDataPath=RESULT_PATH+algorithm +'_results/' + dataName+'/')
TP,FP,TN,FN,p,r,f,RI=process(parameters)
else: print('No evaluation')
print ('dataset:', logname)
print ('training time: %0.3f'%(t2-t1))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-dataset', help = DATASET_STR, type = str, default = '2kBGL')
parser.add_argument('-algorithm', help = ALGORITHM_STR, type = str, default = 'LKE')
parser.add_argument('-choose', help = 'head, tail, all', type = str, default = 'head')
parser.add_argument('-ratio', help = 'the ratio of the head data', type = float, default = '0.5')
parser.add_argument('-eval', help = 'flag to evaluate', type = int, default = 1)
# parser.add_argument('-leaf_num', help = 'for ft-tree', type = int, default = 30)
args = parser.parse_args()
dataset = args.dataset
algorithm = args.algorithm
assert dataset in DATASET_LIST
assert algorithm in ALGORITHM_LIST
# leaf_num = args.leaf_num #对于ft-tree会用到
evaluateMethods(dataset, algorithm, choose=args.choose, ratio=args.ratio, eval_flag=args.eval)
print ('algorithm:',algorithm)
print ('Dataset',dataset)