HelenGuohx / logbert

log anomaly detection via BERT
MIT License
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cannot reproduce results #14

Open mmichaelzhang opened 3 years ago

mmichaelzhang commented 3 years ago

Hi, I tried to run your latest version code and here is the result I got on HDFS dataset:

best threshold: 0, best threshold ratio: 0.2 TP: 2999, TN: 546622, FP: 6746, FN: 7648 Precision: 30.77%, Recall: 28.17%, F1-measure: 29.41% elapsed_time: 919.4318611621857

It is far away from the reported score in the paper. Can you provide a version that can at least be close to the reported result?

HelenGuohx commented 3 years ago

Hi Michael,

Could you tell me which model you use? What steps you have run

Regards, Helen Guo

helen.guo

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On 10/21/2021 21:24, Michael Zhang wrote:

Hi, I tried to run your latest version code and here is the result I got on HDFS dataset:

best threshold: 0, best threshold ratio: 0.2 TP: 2999, TN: 546622, FP: 6746, FN: 7648 Precision: 30.77%, Recall: 28.17%, F1-measure: 29.41% elapsed_time: 919.4318611621857

It is far away from the reported score in the paper. Can you provide a version that can at least be close to the reported result?

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mmichaelzhang commented 3 years ago

I am running with logbert model.

I first used download_hdfs.sh in main/script/, whish is supposed to download necessary data, but the anomaly_label.csv did not automatically download, so I manually added it.

I then ran init.sh, which creates folders, followed by python data_process.py, python logbert.py vocab, logbert.py train and logbert.py predict.

Also, for the script of BGL dataset, it seems unable to generate parameters.txt, and I have to added it based on the script generated for HDFS.

nguyen-brat commented 1 year ago

I also run on HDFS dataset and logbert but got: TP: 8336, TN: 551206, FP: 2162, FN: 2311 Precision: 79.41%, Recall: 78.29%, F1-measure: 78.85% elapsed_time: 1320.057204246521

Can't reach 82% f1 like the original paper