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Implementation of RiskLoc, a method for localizing multi-dimensional root causes.
这个数据集获取不了 All datasets are available at Tsinghua Cloud: https://cloud.tsinghua.edu.cn/d/aa4102a5d1614e57bc36/
可以提供一下网盘链接嘛
hi, I have ran the adtributor algorithm with the B0 dataset, but get 0 TP. Is there something wrong about the code.
Hi,
I try to understand the algorithm squeeze and someone recommend your project. But I can't understand the detail of how each function works, especially the meaning of the input of def squeeze(df, attributes, delta_threshold=0.9, debug=False), line 124 in squeeze.py. Could you provide a demo in the future? Thank you.
Best,
YYL
Thanks for your excellent work! It really helped me a lot.
Recently, I have also been focusing on issues in this area (Multi-dimensional Root Causes Analysis), and as you mentioned:
In practice, I found that the most difficult step is to get accurate forecasting values for all leaf elements. Since these are usually quite fine-grained, they don't actually have much data and any forecasts are often inaccurate. This can skew the results.
I've also found it very difficult to get the forecast value of all leaves, in particular, certain combinations have only a small number of values or are almost 0, is there any suitable forecasting method worth recommending in this case?
Or have you tried using the RiskLoc algorithm in a real industrial scenario, and if so, can you share what forecasting method you used in this case?
Hi, I have a question about the input data, how is the data input to the different algorithms? Thanks a lot in advance
Hi,
I hope you are well.
When I used riskloc in my dataset, I noticed that it can precisely found the root cause. However, my purpose is to find those anomalies that occur more frequently, so I would consider those rare root causes I found would be some outliers. Then I tried to increase the value of "n_remove" , but still not got my expected result.
Also, when I decrease the "n_remove" to 1, the "cutoff" value shifted a lot, and the output return null. When I do the same thing in another dataset, the result was not affected. I compared the distributions of measurements of 2 datasets, the first one is more like normal distribution, the second one is like long-tailed distribution.
Here are my questions:
I am looking forward to your reply.
Hello:
I have a question about the method of anomaly injection(scale_anomaly) in generate_dataset.py, why should a relatively large value be taken in row*(1-r) and 0, which will cause the predicted value of some abnormal combination to be 0, so in It will be filtered out when using squeeze.
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