#!/usr/bin/env python
import sys
sys.path.append('../')
from logparser import LogMine
input_dir = '../logs/HDFS/' # The input directory of log file
output_dir = 'LogMine_result/' # The output directory of parsing results
log_file = 'HDFS_2k.log' # The input log file name
log_format = '<Date> <Time> <Pid> <Level> <Component>: <Content>' # HDFS log format
levels = 2 # The levels of hierarchy of patterns
max_dist = 0.001 # The maximum distance between any log message in a cluster and the cluster representative
k = 1 # The message distance weight (default: 1)
regex = [] # Regular expression list for optional preprocessing (default: [])
parser = LogMine.LogParser(input_dir,
output_dir,
log_format,
rex=regex,
levels=levels,
max_dist=max_dist,
k=k)
parser.parse(log_file)
'max_dist': 0.004,
'k': 1,
'levels': 2
},
}
benchmark_result = []
for dataset, setting in benchmark_settings.iteritems():
print('\n=== Evaluation on %s ===' % dataset)
indir = os.path.join(input_dir, os.path.dirname(setting['log_file']))
log_file = os.path.basename(setting['log_file'])
parser = LogMine.LogParser(log_format=setting['log_format'],
indir=indir,
outdir=output_dir,
rex=setting['regex'],
max_dist=setting['max_dist'],
k=setting['k'],
levels=setting['levels'])
parser.parse(log_file)
F1_measure, accuracy = evaluator.evaluate(
groundtruth=os.path.join(indir, log_file + '_structured.csv'),
parsedresult=os.path.join(output_dir, log_file + '_structured.csv'))
benchmark_result.append([dataset, F1_measure, accuracy])
print('\n=== Overall evaluation results ===')
df_result = pd.DataFrame(benchmark_result,
columns=['Dataset', 'F1_measure', 'Accuracy'])
df_result.set_index('Dataset', inplace=True)
print(df_result)