Comments (8)
I've also tried the same thing with categorical variables and the model seems to suffer from mode collapse (within a categorical variable). Seeking some guidance on this.
from gain.
HI!
Thank you for providing the code to your paper.
I've realized that in the implementation, the loss function used for the reconstruction loss only considers continuous variables in contrast with the paper that considers both cases.
Do you happen to have the code for the categorical variables too?
from gain.
Don't have the code but these are the tips from the author.
On top of one-hot encoding, to make categorical variable work:
(1) Activation function: use softmax activation for each spanned categorical vector (instead of sigmoid activation)
(2) Change the MSE loss to the cross-entropy loss for the categorical variable.
from gain.
Thanks, HaoXiao2018.
This is exactly what I did for the categorical variables.
As can be seen in the paper, I used cross-entropy for categorical variable and mse for continuous variable.
But more important thing is that we need to use separate output layer (activation function with softmax).
from gain.
So, is it possible to have both categorical and continuous features in a dataset?
from gain.
Yes. It would be better if you modify the codes based on the comments from HaoXiao2018 above and apply GAIN for your mixed-type data.
from gain.
If I have categorical data, does that mean I also have to change the dimensions of all the network layers, etc as well?
from gain.
Network parameters should be updated for different datasets.
Even for the datasets which only have continuous variables, those hyper-parameters should be optimized to maximize the performance.
from gain.
Related Issues (20)
- How to decide Missingness Mechanism HOT 1
- Differences with the paper HOT 1
- Using GAIN in inductive mode HOT 1
- Changing only missing values? and scoring? HOT 1
- Why not both L_G and L_D relevant to V(D,G)? HOT 1
- Could you please provide Requirements.txt file HOT 1
- My dataset is 203454KB, I can't get the dataset after filling, because my dataset is too big? It gives some mistakes. HOT 1
- mixed (categorical and numerical) data HOT 3
- Model for the MNIST dataset HOT 1
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- hyperparameters HOT 3
- RMSE is not stable HOT 1
- RMSE HOT 1
- Hint matrix HOT 1
- Why isn't the loss calculated only with b_i=0 values of the Hints. HOT 2
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from gain.