This repository contains the implementation of our Heterogeneous Incomplete Variational Autoendoder model (HI-VAE). It has been written in Python, using Tensorflow.
The details of this model are included in this paper. Please cite it if you use this code for your own research.
There are three different datasets considered in the experiments (Wine, Adult and Default Credit). Each dataset has each own folder, containing:
- data.csv: the dataset
- data_types.csv: a csv containing the types of that particular dataset. Every line is a different attribute containing three paramenters:
- type: real, pos (positive), cat (categorical), ord (ordinal), count
- dim: dimension of the variable
- nclass: number of categories (for cat and ord)
- Missingxx_y.csv: a csv containing the positions of the different missing values in the data. Each "y" mask was generated randomly, containing a "xx" % of missing values.
You can add your own datasets as long as they follow this structure.
- script_HIVAE.sh: A script with a simple example on how to run the models.
- main_scripts.py: Contains the main code for the HIVAE models.
- loglik_ models_ missing_normalize.py: In this file, the different likelihood models for the different types of variables considered (real, positive, count, categorical and ordinal) are included.
- model_ HIVAE_inputDropout.py: Contains the HI-VAE with input dropout encoder model.
- model_ HIVAE_factorized.py: Contains the HI-VAE with factorized encoder model
Alfredo Nazabal: anazabal@turing.ac.uk
First,
$ git clone https://github.com/probabilistic-learning/HI-VAE.git
$ pip install virtualenv
$ cd HI-VAE
$ virtualenv -p python3 _venv
$ source _venv/bin/activate
$ pip install -r pip_requirements.txt
$ chmod +x script_HIVAE.shThen, run
$ ./script_HIVAE.sh