Comments (6)
Hi @phanxuanphucnd, it is passed to check the accuracy of the model. If you see the forward pass of the model, it will use 1:t-1 responses to predict t^th response. This is done via mask in the model.
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hi @arghosh pls, help me !
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Hi @arghosh.
In my understanding, this seems to be true as we already know the labels of tests. Assume that, labels is unavailable. How does this work?
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So in KT framework, we predict future responses based on past performances. If the prior history is 0, the model will be bad. Suppose, you want to predict t^th response (which is unavailable) based on t-1 responses (these should be available), just put anything (0, or 1) for the t^th response.
I am assuming you are talking about challenge setup. I need to understand what your exact problem setup is. Depending on your problem, it might not be a KT problem. You can use some collaborative filtering based techniques.
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Yes, Thanks @arghosh !
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in akt.py code Line 159 => Line 208 =>Line 281 =>Line 326 it masks the current and future responses.
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Related Issues (16)
- Comparison to other models (SAKT, DKT, DKVMN) HOT 2
- What is PID assist2009_pid HOT 1
- kq_same? HOT 1
- Why don't all the models care repeated response sequences with different skill tagging? HOT 1
- "key_padding_mask" in attention mechanism not be implemented? HOT 2
- Masking or slicing allowing the model to use previous interactions HOT 4
- A bug of Rasch Model-Based Embeddings, Please check it! HOT 4
- statics data: some users occur in a same dataset more than once or occur in both training and validation/testing data
- hyper-parameters of AKT for achieving the best mean test AUC
- dropout in attention function
- Padding Problem
- target response issue in AKT model
- How to draw the Fig.2?
- Issue while running AKT with individual snp data
- Loss of Model Performance after Removing 'with torch.no_grad()' at Line 304 in akt.py
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