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evaluation error #3

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huiyegit opened this issue Sep 27, 2021 · 4 comments
Open

evaluation error #3

huiyegit opened this issue Sep 27, 2021 · 4 comments

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@huiyegit
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huiyegit commented Sep 27, 2021

Thank you for sharing your source code. I had an issue when I ran the source code. Could you please tell me what is the problem?

image

@ytc272098215
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ytc272098215 commented Sep 28, 2021

Thank you for sharing your source code. I had an issue when I ran the source code. Could you please tell me what is the problem?

image

In the normal running procedures, the code (line 93) before this error should get a 4*4 confusion matrix (since there are 4 categories). I have no idea as well why this weird error occurs: it seems that a 3*3 confusion matrix was obtained. Please try to check the setting of the number of categories, the shape of the obtained confusion matrix, and whether pandas automatically sets one row/column as index?

@ytc272098215
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Thank you for sharing your source code. I had an issue when I ran the source code. Could you please tell me what is the problem?
image

In the normal running procedures, the code (line 93) before this error should get a 44 confusion matrix (since there are 4 categories). I have no idea as well why this weird error occurs: it seems that a 33 confusion matrix was obtained. Please try to check the setting of the number of categories, the shape of the obtained confusion matrix, and whether pandas automatically sets one row/column as index?

It seems that this error was caused by the change of the data link that I rely on. I have found the data in my local machine and uploaded it to the release(raw_data.zip). Please check if the error still occurs.
https://github.com/BUPT-GAMMA/CompareNet_FakeNewsDetection/releases/tag/dataset

@huiyegit
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huiyegit commented Oct 9, 2021

Thank you for your reply. The error has been solved, but the results are different from the ones in the paper. Is there anything wrong?

Selection_018

@alvinsun724
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Hi, I also have the same issue regarding the result:
image

Could you explain a little?
Thanks

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