Rethinking Data Augmentation for Tabular Data in Deep Learning

Under review.

The official implementation of the paper "Rethinking Data Augmentation for Tabular Data in Deep Learning" (link).

In this README, we provide information about the environment we used and instructions on how to run the code to reproduce our experiments.

Feel free to report issues.

Environment

The experiments in our paper were conducted using the following environment:

• Operating System: Ubuntu 22.04.1 LTS
• CUDA compiler version: 11.7
• Python 3.10.6

Installation

Use poetry to create an python environment and activate it.

poetry install
poetry shell

Running Experiments

Before starting the experiment you need to download Adult data from https://www.kaggle.com/datasets/wenruliu/adult-income-dataset. Please place the downloaded adult.csv under datasets/Adult/raw/. You can change the location of the datasets/ directory by changing the data_dir in conf/config.yaml.

Supervised Learning

For example, to run supervised learning with MTR on the Adult data set, use the following command:

python main.py train_mode=supervised data=Adult model=fttrans/mask_token  seed="range(1,30)" model.params.mask_ratio=0.1,0.2,0.3,0.4,0.5,0.6,0.7 model.params.bias_after_mask=false -m 

If you want to replicate our experiment, use the following command:

python script/sl/run_all.py 41143,44,41145,287,4538,45062,45060,45012,CAHousing,1461,Adult,41166,1597 1 10

Self Supervised Learning

For example, to run self-supervised learning with MTR on the Adult data set, use the following command:

python main.py train_mode=self_sl data=Adult model=fttrans/mask_token model.trainer=FTTransMaskTokenSSLTrainer seed="range(1,30)" model.params.mask_ratio=0.1,0.2,0.3,0.4,0.5,0.6,0.7 model.params.bias_after_mask=false -m

If you want to replicate our experiment, use the following command:

python script/self_sl/run_all.py 41143,44,41145,287,4538,45062,45060,45012,CAHousing,1461,Adult,41166,1597 0.25 10