shaido987 / riskloc Goto Github PK

The calculation of surprise value in adtributor seems not correct to me.

The JS divergense formula should be:

So, the code should be:

p = df['predict'] / F
q = df['real'] / A
m = (p + q) / 2
df['surprise'] = 0.5 * np.sum(p * np.log(p/m)) + 0.5 * np.sum(q * np.log(q/m))

what do you think? thanks.

大佬您好，PS方法采用RE（涟漪效应）来度量因的置信度，如何理解PS方法的原理

很多人的猜想类似于下面的：
如果属性值是因 ， 属性值的变化和属性值样本的变化符合涟漪效应；
如果属性值的变化和属性值样本的变化符合涟漪效应，则属性值是因

这种理解对么

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.

hi, I have ran the adtributor algorithm with the B0 dataset, but get 0 TP. Is there something wrong about the code.

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:

1. Is adjusting n_remove a way to do what I expect? If yes, is there some more reliable way than setting constants arbitrarily?
2. Does the distribution of the measurements range affect the performance of the algorithm?

I am looking forward to your reply.

Hi, I have a question about the input data, how is the data input to the different algorithms? Thanks a lot in advance

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 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

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