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MIDA-pytorch

A pytorch implementation of "MIDA: Multiple Imputation using Denoising Autoencoders"

Summary

  1. Doing imputation with Overcomplete AutoEncoder for missing data
  2. Using complete data for training
  3. Dropout is used to generate artificial missings in the training session
  4. Experimenting with two missing methods(MCAR/MNAR)
  5. Simple but good

Requirements

  • python==3.6
  • numpy==1.14.2
  • pandas==0.22.0
  • scikit-learn==0.19.1
  • pytorch==1.0.0

Data

In the paper, 15 publicly available datasets used.
In this code, only 'Boston Housing' data is used among 15.
http://math.furman.edu/~dcs/courses/math47/R/library/mlbench/html/BostonHousing.html

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mida-pytorch's Issues

Categorical and Mixed data

Thank you for the implementation. How to handle if we have categorical data or mixed data in our dataset?

rmse calculates it based on the difference of the normalized values

Hello
I was testing the code, a first view works fine. But then I realized that the testing rmse calculates it based on the difference of the normalized values. So since the values ​​are small, the sum of differences of small things gives a small sum.
I modified the code so that it calculates it with the real values ​​(denormalized) and the rmse gives me quite a bad result.

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