TabUnite: A Universal Categorical Encoding Scheme for Mixed-Type Tabular Data

Official Implementation of the Paper - TabUnite: Efficient Encoding Schemes for Flow and Diffusion Tabular Generative Models.

Usage

tabunite_main: baseline experiments

tabunite_census-synthetic: census synthetic dataset

tabunite_syn_qual: qualitative synthetic dataset

tabunite_syn_quant: quantitative synthetic dataset

Environment

Clone this repository and navigate to it in your terminal.

Create environment. This environment can be used for everything apart from eval_quality:

conda create -n tabunite python=3.10
conda activate tabunite

Install pytorch via conda:

conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia

and other dependencies within the TabUnite directory:

pip install -r requirements.txt

pip install dgl -f https://data.dgl.ai/wheels/cu117/repo.html
pip install torch_geometric
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.1+cu117.html

Any other missing dependencies can be installed using pip. Once all the dependencies are installed, the scripts should run accordingly.

For eval_quality, create the following environment:

conda create -n tabunite_quality python=3.10
conda activate tabunite_quality

python -m pip install "pip<24.1"

pip install synthcity
pip install icecream
pip install category_encoders
pip install tomli
pip install tomli_w
pip install zero
pip install scipy
pip install click==8.1.7
pip install scipy==1.12.0

pip install --upgrade pip

Datasets

Baseline Datasets

Download baseline datasets and run them within the tabunite_main directory as follows:

python download_dataset.py
python process_dataset.py

The following datasets are included:

• Adult: https://archive.ics.uci.edu/dataset/2/adult
• Beijing PM2.5: https://archive.ics.uci.edu/dataset/381/beijing+pm2+5+data
• Default of Credit Card Clients: https://archive.ics.uci.edu/dataset/350/default+of+credit+card+clients
• MAGIC Gamma Telescope: https://archive.ics.uci.edu/dataset/159/magic+gamma+telescope
• Online Shoppers Purchasing Intention: https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+dataset
• Online News Popularity: https://archive.ics.uci.edu/dataset/332/online+news+popularity

We provide three additional baseline datasets from Kaggle:

• Banking - Marketing Targets: https://www.kaggle.com/datasets/prakharrathi25/banking-dataset-marketing-targets
• Cardiovascular Disease: https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset
• Stroke Prediction: https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset

As Kaggle requires user log-on, these three datasets should be downloaded manually.
To process each dataset:

cd data
mkdir [dataname]
# Move the downloaded Kaggle dataset .csv file into the newly-created folder
cd ..
python process_dataset.py [dataname]

Census Synthetic Dataset

For the Census Synthetic dataset, download the census.csv dataset from the UCI Machine Learning Repository where you rename USCensus1990.data.txt to census.csv. Save census.csv in tabunite_census-synthetic/data (tabunite_census-synthetic/data/census.csv). The dataname for this dataset to train, sample, and eval is denoted as: syn1.

Training, Sampling and Evaluation

Training:

python main.py --dataname [NAME_OF_DATASET] --method [NAME_OF_BASELINE_METHODS] --mode train

python main.py --dataname beijing --method i2bflow --mode train

Sampling:

python main.py --dataname [NAME_OF_DATASET] --method [NAME_OF_BASELINE_METHODS] --mode sample

python main.py --dataname beijing --method i2bflow --mode sample

Evaluation:

python eval/eval_mle.py --dataname [NAME_OF_DATASET] --model [NAME_OF_BASELINE_METHODS] --path [PATH_TO_SYNTHETIC_DATA]
python eval/eval_density_cde.py --dataname [NAME_OF_DATASET] --model [METHOD_NAME] --path [PATH_TO_SYNTHETIC_DATA]
python eval/eval_density_pwc.py --dataname [NAME_OF_DATASET] --model [METHOD_NAME] --path [PATH_TO_SYNTHETIC_DATA]
python eval/eval_quality.py --dataname [NAME_OF_DATASET] --model [METHOD_NAME] --path [PATH_TO_SYNTHETIC_DATA]
python eval/eval_detection.py --dataname [NAME_OF_DATASET] --model [METHOD_NAME] --path [PATH_TO_SYNTHETIC_DATA]

python eval/eval_mle.py --dataname beijing --model i2bflow --path synthetic/beijing/i2bflow.csv