Comments (4)
Try this in
process_tbird.sh
--regex "$REGEX1" "$REGEX2" "$REGEX3" "$REGEX4"
data_process.py
parser.add_argument("--regex", nargs='*', help="regex to clean log messages", default='')
from logbert.
Hi,
I found the correct way when I checked the original Drain_demo.py. (https://github.com/logpai/logparser/blob/master/demo/Drain_demo.py)
Regular expression list for optional preprocessing (default: [])
regex = [
r'blk_(|-)[0-9]+' , # block id
r'(/|)([0-9]+.){3}[0-9]+(:[0-9]+|)(:|)', # IP
r'(?<=[^A-Za-z0-9])(-?+?\d+)(?=[^A-Za-z0-9])|[0-9]+$', # Numbers
]
st = 0.5 # Similarity threshold
depth = 4 # Depth of all leaf nodes
parser = Drain.LogParser(log_format, indir=input_dir, outdir=output_dir, depth=depth, st=st, rex=regex)
parser.parse(log_file)
--regex="$REGEX1 $REGEX2 $REGEX3 $REGEX4" is passed the value as 1 size of list.
Please check it.
from logbert.
Thank you for letting me know. I used nargs='*'
in argparse to receive multiple inputs as list (check this for usage ). But I should use --regex $REGEX1 $REGEX2 $REGEX3
instead of --regex="$REGEX1 $REGEX2 $REGEX3"
in shell scripts
from logbert.
Not covered REGEX3='(?<=Warning: we failed to resolve data source name )[\w\s]+'
argparse is processing it to
['(0x)[0-9a-fA-F]+', '\d+.\d+.\d+.\d+', '(?<=Warning:', 'we', 'failed', 'to', 'resolve', 'data', 'source', 'name', ')[\w\s]+', '\d+']
from logbert.
Related Issues (20)
- 看结果很多像是代码都没跑通,如果不是真的跑完的,请不要占坑位!还有很多机构在做bert for log之类的工作 HOT 1
- 关于logdeep中的semantics特征 HOT 1
- Question about logbert in logs Apache? HOT 1
- LAnoBERT : System Log Anomaly Detection based on BERT Masked Language Model HOT 2
- The experimental data of the paper cannot be reproduced HOT 9
- 您好,打扰一下 HOT 1
- 请教问题 HOT 2
- 关于log key中的<*>如何参与Bert的MLM训练 HOT 2
- 请教BGL训练问题
- os.mknod error
- best_center.pt not found HOT 2
- Can you please add a license file?
- indexing particular sequence in parsed structured log file
- Spelling error on requirements.txt
- Test Size not properly set HOT 5
- LogParser - drain parser is old version
- test_size=68 should be either positive and smaller than the number of samples 34 or a float in the (0, 1) range HOT 4
- HDFS deeplog Train stage is fast but test stage is slow HOT 2
- Pip install for logbert HOT 2
- HDFS/python logbert train HOT 3
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from logbert.