Need Help Regarding TabNetEncoder output and TabNetPretraining

[Hazqeel09]

Hye, Optimox. As you answered previously, I can use TabNetEncoder to produce custom sizes of embedding. Upon testing the layer, I realised it produced three outputs, I dont know which one is more important.

The paper that I tried to copy also used unsupervised training. TabNetPretraining layer also produced three different outputs. I checked the outputs' meaning on GitHub but I also doesnt know which one is important and how to use that to pass to TabNetEncoder.

The paper that I'm trying to duplicate is here (https://ieeexplore.ieee.org/document/9658729). Basically, the methods that I need to duplicate from the paper are:

• Unsupervised learning using TabNet
• Encode the value using TabNet
• Get feature importance

I already managed the transformer part, but I'm still stuck at the TabNet part.

Lastly, what is the difference between forward and forward mask?

Thank you.

[Optimox]

Hello,

The encoder outputs two things, the different steps outputs and an auxiliary value useful for computting the loss

tabnet/pytorch_tabnet/tab_network.py

Line 188 in bcae5f4

return steps_output, M_loss

You can see here how the auxiliary value for the loss is used :

tabnet/pytorch_tabnet/abstract_model.py

Line 509 in bcae5f4

loss = loss - self.lambda_sparse * M_loss

The forward_mask simply outputs the mask instead of the loss auxiliary so that it can be used for inference to get feature importances: see the explain method here

tabnet/pytorch_tabnet/abstract_model.py

Line 303 in bcae5f4

def explain(self, X, normalize=False):

What you can do:

• simply use TabNetPretrainer to pretrain your encoder, use the standard library
• extract the tabnet encoder weight by accessing the encoder clf.network.tabnet.encoder, create the encoder inside your pipeline and load the weights.
• access the feature importance the same way the library do (you'll have to go inside the source code and copy paste some parts)
• train your end to end pipeline with your BERT model

[Hazqeel09]

I think I managed to get the model to learn and provide output but can you help to check if I'm doing it correctly?
Thank you

[Optimox]

It does not make sense to load a state dict at each forward step, the first two lines should probably be inside the init method.

I don't think it's a good idea to reshape the output of tabnet's encoder to a 8x8 image. What kind of model is your NetRelu ?

You should probably use flattened representations of both model and concatenate them before passing them to a simple Linear head.

[Hazqeel09]

I see your point, will move the code to be inside init method.

I reshape the output because the architecture I'm trying to copy reshape tabnet output to 1x8x8 and bert sentence embedding to 12x8x8

This is my NetRelu that also imitates the paper