https://github.com/e3oroush/vae_missing_data

A Pytorch implementation example of Variation Auto Encoder
https://github.com/e3oroush/vae_missing_data

Last synced: 3 months ago
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A Pytorch implementation example of Variation Auto Encoder

• Host: GitHub
• URL: https://github.com/e3oroush/vae_missing_data
• Owner: e3oroush
• License: mit
• Created: 2020-02-28T09:02:46.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-02-28T11:04:07.000Z (almost 6 years ago)
• Last Synced: 2025-04-04T23:11:20.687Z (10 months ago)
• Language: Python
• Size: 680 KB
• Stars: 4
• Watchers: 1
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Variation Autoencoder with Missing values

A Pytorch implelmentation of VAE for handling missing data in test. [Here](https://medium.com/@ebrahim.soroush/variational-auto-encoder-with-missing-data-b975abd0a9c?sk=70b58740c1e214b599ff1c1bc2707ae5) is my medium post.

## Requirements:

* [pytorch](http://pytorch.org/)

* numpy

* matplotlib

* tqdm

## How to run

This is a sample code to working with a VAE in pytorch in with missing data. There are two experiments with two datasets: MNIST and synthetic timeseries data. In the following you can find how to run each experiment.

### Running with MNIST

To train VAE:

```bash

python main.py --epochs 100 --train_mode mnist

```

To test with partial data:

```bash

python tests.py --test_partial_data_mnist

```

An example of sampling output:  

![Samples output](./results/mnist/sampling-50.png)  

An example of reconstruction output:  

![Reconstruct output](./results/mnist/reconstruction-50.png)

An example of reconstruction output with partial data:  

![Reconstruct partial output](./results/mnist/partials-50.png)

### Running with synthetic timeseries

First we need to generate synthetic data for both train and test data

```bash

python tests.py --generate_timeseries_synthetic_train

python tests.py --generate_timeseries_synthetic_test

```

And then train VAE model:

```bash

python main.py --epochs 100 --train_mode synthetic_timeserie

```

And finally test with partial data

```bash

python tessts.py --test_partial_data_synthetic_ts

```

An example of reconstruction:  

![Reconstruct output](./results/synthetic_timeseries/timeseries-reconstruction-50.png)

An example of reconstruction with partial data:

![Reconstruct with prtial data output](./results/synthetic_timeseries/partials-timeseries-3.png)