Comments (1)
Thank you for your interest in our paper.
- I am not sure whether I understand your question correctly.
- In order to check which components are missing, you can use the mask vector (M).
- You can extract this as checking the nan component or 0 component (if you put 0 for missing values).
- After the training, the following part only replace the missing components to the imputed components: imputed_data = data_m * norm_data_x + (1-data_m) * imputed_data in line 167.
- GAN training
- GAN training is not easy and there is no explicit score that we can track for understanding the training process.
- People usually see the G and D losses and check whether they are well-balanced because balancing G/D is the key for GAN training.
- In GAIN, you can check the following three losses: MSE_loss, G_loss, and D_loss (see line 153 - 157).
- MSE_loss represents whether the model can recover the observed components. G/D losses are the same roles in original GAN training.
Hopefully, these answers would resolve your questions.
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
- 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
- alpha HOT 1
- original data HOT 1
- Hyperparameters training HOT 3
- 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
- No split training and testing sets? HOT 2
- Training Query HOT 1
- about minibatch HOT 1
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from gain.