Good day!
Thank you very much for your excellent work!
I was able to reproduce the results when using the preprocessed data provided in this repository. However, I encountered difficulties when trying to reproduce the full pipeline starting from the raw GAIA dataset to model training.
For raw data preparation, I used the code from the following repository:
https://github.com/tangpan360/TVDiag-data-analysis
Then I ran main.py with reconstruct=True. In this setting, the training completes successfully, but the final prediction metrics are much lower than expected. Here are my results:
2026-03-10 01:01:59 [INFO]: [Root localization] HR@1: 13.845%, HR@2: 27.476%, HR@3: 40.362%, HR@4: 55.272%, HR@5: 66.880%, avg@3: 0.272, MRR@3: 0.250 2026-03-10 01:01:59 [INFO]: [Failure type classification] precision: 45.393%, recall: 46.006%, f1-score: 45.698% 2026-03-10 01:01:59 [INFO]: The average test time is 0.00485848415646944[s] 2026-03-10 01:01:59 [INFO]: The total test time is 4.562116622924805[s]
Has anyone experienced a similar issue when reproducing the pipeline from the raw GAIA dataset?
I would greatly appreciate any suggestions on whether there might be an additional preprocessing step, configuration detail, or dataset version difference (I am using release 1 of the GAIA dataset) that could affect the results.
Thank you in advance for your help!
Good day!
Thank you very much for your excellent work!
I was able to reproduce the results when using the preprocessed data provided in this repository. However, I encountered difficulties when trying to reproduce the full pipeline starting from the raw GAIA dataset to model training.
For raw data preparation, I used the code from the following repository:
https://github.com/tangpan360/TVDiag-data-analysis
Then I ran main.py with reconstruct=True. In this setting, the training completes successfully, but the final prediction metrics are much lower than expected. Here are my results:
2026-03-10 01:01:59 [INFO]: [Root localization] HR@1: 13.845%, HR@2: 27.476%, HR@3: 40.362%, HR@4: 55.272%, HR@5: 66.880%, avg@3: 0.272, MRR@3: 0.250 2026-03-10 01:01:59 [INFO]: [Failure type classification] precision: 45.393%, recall: 46.006%, f1-score: 45.698% 2026-03-10 01:01:59 [INFO]: The average test time is 0.00485848415646944[s] 2026-03-10 01:01:59 [INFO]: The total test time is 4.562116622924805[s]Has anyone experienced a similar issue when reproducing the pipeline from the raw GAIA dataset?
I would greatly appreciate any suggestions on whether there might be an additional preprocessing step, configuration detail, or dataset version difference (I am using release 1 of the GAIA dataset) that could affect the results.
Thank you in advance for your help!