chris-santiago/vime

Reproducing the VIME framework for self- and semi-supervised learning to tabular domain.

VIME - PyTorch

This repo reproduces VIME framework for self- and semi-supervised learning to tabular domain.

Authors: Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar

Reference: Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar, "VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain," Neural Information Processing Systems (NeurIPS), 2020.

Original paper: https://proceedings.neurips.cc/paper/2020/hash/7d97667a3e056acab9aaf653807b4a03-Abstract.html

Original repo: https://github.com/jsyoon0823/VIME/tree/master

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About

Initial Implementation

This initial implementation follows the VIME self-supervised framework to train an encoder on unlabeled MNIST data, which is then used to train a semi-supervised MLP on a much smaller portion of labeled MNIST data. The final model is tested against the standard MNIST test set.

Block diagram of the proposed self-supervised learning framework on tabular data. Credit: Yoon et al.

The final model used only 10% of MNIST training set (n=6,000) as labeled data for the semi-supervised learning and reached 93% classification accuracy on the test set. None of the hyperparameters were optimized for this initial work.

Block diagram of the proposed semi-supervised learning framework on tabular data. Credit: Yoon et al.

Full configuration listed in outputs/vime-encoder/train_self/2023-05-26/10-09-22/.hydra/config.yaml for the self-supervised encoder and in outputs/vime-learner/train_semi/2023-05-26/10-32-51/.hydra/config.yaml for the semi-supervised learner.

Install

Clone this repository, create a new Conda environment and

git clone https://github.com/chris-santiago/vime.git
conda env create -f environment.yml
cd vime
pip install -e .

Use

Prerequisites

Task

This project uses Task as a task runner. Though the underlying Python commands can be executed without it, we recommend installing Task for ease of use. Details located in Taskfile.yml.

Current commands

> task -l
task: Available tasks for this project:
* check-config:       Check Hydra configuration
* train-multi:        Launch multiple training jobs
* train-self:         Train the VIME encoder module
* train-semi:         Train the VIME semi-SL module
* wandb:              Login to Weights & Biases

PDM

This project was built using this cookiecutter and is setup to use PDM for dependency management, though it's not required for package installation.

Hydra

This project uses Hydra for managing configuration CLI arguments. See vime/conf for full configuration details.

Weights and Biases

This project is set up to log experiment results with Weights and Biases. It expects an API key within a .env file in the root directory:

WANDB_KEY=<my-super-secret-key>

Users can configure different logger(s) within the conf/trainer/default.yaml file.

Training

• Run task train-self to train the self-supervised encoder. Once complete, check the outputs/vime-encoder/train_self/../checkpoints directory for path to saved checkpoint.
• Copy and paste this checkpoint into the semi-supervised model config, located at conf/model/learner.yaml under the nn.encoder_ckpt key.
• Run task train-semi to train the semi-supervised encoder.

All results will populate to their respective output directories:

├── outputs
│   ├── vime-encoder
│   │   └── train_self
│   │       └── 2023-05-26
│   │           └── 10-09-22
│   │               ├── .hydra
│   │               ├── checkpoints
│   │               └── wandb
│   └── vime-learner
│       └── train_semi
│           └── 2023-05-26
│               ├── 10-32-51
│               │   ├── .hydra
│               │   ├── checkpoints
│               │   └── wandb

Documentation

Documentation hosted on Github Pages: https://chris-santiago.github.io/vime/