Changing only missing values? and scoring? about gain HOT 1 CLOSED

Thank you for your interest in our paper.

1. 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.

2. 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.