Wrong Label on method SOGAAL and MOGAAL

[luisfelipe18]

On the original paper from Xiangnan He, the inliers are marked as 1 and outliers marked as 0. I checked their original code and the code of pyod line by line and are almost the same.
On the line https://github.com/yzhao062/pyod/blob/94c27ef3841de4b0e5a732f9c720092a24397633/pyod/models/mo_gaal.py#L79 you state 0 for inliers and 1 for outliers , but in the original paper at page 3, section 3.1, third and fourth line says 0 outlier, 1 inlier. https://arxiv.org/pdf/1809.10816.pdf

[yzhao062]

This is a good point and potentially a bug. I did not implement this algorithm by myself and need some probing. In the worst case, it is incorrect and we need to flip the score and also the score.

[luisfelipe18]

in the line 11, from principalDf["resultado"] = principalDf["resultado"].map({0:"b",1:"r"}), I am mapping 0 to "b" and 1 to "r".

As you can see, the lower proportion is "b" which are equivalents to 0. (This 0 and 1 comes from model.predict . Since docs says 0 must be a inlier and it appears in lower proportion I started to think that something is wrong.

[anranhui]

I also encountered this error, sogaal original 1 is normal data, but in pyod, 0 is normal data

[zhaoxing-zstar]

I compare the results between SO_GAAL and other algorithms, and I think the score for SO_GAAL should be flipped (0 for outlier, 1 for normal). Maybe overriding the _process_decision_score method would work.

[yzhao062]

I suspect that guys...
This is the sogaal example with a simple synthetic data.

if I flip the score by -1, the performance looks incorrect.

Maybe I miss some points?

[zhaoxing-zstar]

I suspect that guys... This is the sogaal example with a simple synthetic data.

if I flip the score by -1, the performance looks incorrect.

Maybe I miss some points?

My fault, you're right.

[anranhui]

In principle, so Gaal should be such a process The outliers are separated by positive and negative samples. However, too many training rounds of the generator may cause the negative samples to be too close to the positive samples, so the performance will be reduced. To solve this problem, I have further studied and found another way to better isolate outliers. At present, I am writing a paper

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------------------ 原始邮件 ------------------ 发件人: "yzhao062/pyod" ***@***.***>; 发送时间: 2022年4月20日(星期三) 上午10:31 ***@***.***>; ***@***.******@***.***>; 主题: Re: [yzhao062/pyod] Wrong Label on method SOGAAL and MOGAAL (#237) I suspect that guys... This is the sogaal example with a simple synthetic data. if I flip the score by -1, the performance looks incorrect. Maybe I miss some points? — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: ***@***.***>

[anranhui]

In principle, so Gaal should be such a process，The outlier is actually x, and there is a problem with the legend The outliers are separated by positive and negative samples. However, too many training rounds of the generator may cause the negative samples to be too close to the positive samples, so the performance will be reduced. To solve this problem, I have further studied and found another way to better isolate outliers. At present, I am writing a paper

…

------------------ 原始邮件 ------------------ 发件人: "yzhao062/pyod" ***@***.***>; 发送时间: 2022年4月20日(星期三) 上午10:31 ***@***.***>; ***@***.******@***.***>; 主题: Re: [yzhao062/pyod] Wrong Label on method SOGAAL and MOGAAL (#237) I suspect that guys... This is the sogaal example with a simple synthetic data. if I flip the score by -1, the performance looks incorrect. Maybe I miss some points? — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: ***@***.***>

[yzhao062]

looking forward to knowing more about the progress. good luck with the paper.