jyansir / t2g-former Goto Github PK

您好，首先十分感谢您非常优秀的工作和整洁易懂的代码，方便的话可以补充一下dataset部分吗？

Dear authors, I'm interested in your work, I want to know how to organize the data format to run t2g-former.
I note that you mentioned FT-Transformer, did you use the same format of dataset as FT-Transformer?
Looking forward to your reply!

可以分享一下data目录吗，我是一个初学者没有理解数据集的处理方式

Hi there, after reading your papers and reproduce of your codes on dataset Churn Modelling, I have some questions.
Since kaggle have listed all features and their meanings, I think the classification of numerical and categorical features in the paper is not reasonable enough. I think these features should be categorical features instead of numerical features:

• Gender ({0, 1} for {male, female})
• NumOfProducts (The products they own. Integer values {1,2,3,4})
• HasCrCard (Do they have a credit card or not {0,1})
• IsActiveMember (How active member they are {0,1})
• Tenure (The time of bond with company. Integer values {0,1,2,3,4,5,6,7,8,9,10})

By the way, I downloaded the datasets from the link you provided, in dataset Churn, the info.json mistakenly wrote "n_num_features" as 10 (the correct one should be 9).