Tabular Transformers for Modeling Multivariate Time Series

This repository provides the pytorch source code, and data for tabular transformers (TabFormer). Details are described in the paper Tabular Transformers for Modeling Multivariate Time Series, to be presented at ICASSP 2021.

Summary

Modules for hierarchical transformers for tabular data
A synthetic credit card transaction dataset
Modified Adaptive Softmax for handling masking
Modified DataCollatorForLanguageModeling for tabular data
The modules are built within transformers from HuggingFace ?. (HuggingFace is ❤️)

Requirements

Python (3.7)
Pytorch (1.6.0)
HuggingFace / Transformer (3.2.0)
scikit-learn (0.23.2)
Pandas (1.1.2)

(X) represents the versions which code is tested on.

These can be installed using yaml by running :

conda env create -f setup.yml

Credit Card Transaction Dataset

The synthetic credit card transaction dataset is provided in ./data/credit_card. There are 24M records with 12 fields. You would need git-lfs to access the data. If you are facing issue related to LFS bandwidth, you can use this direct link to access the data. You can then ignore git-lfs files by prefixing GIT_LFS_SKIP_SMUDGE=1 to the git clone .. command.

PRSA Dataset

For PRSA dataset, one have to download the PRSA dataset from Kaggle and place them in ./data/card directory.

Tabular BERT

To train a tabular BERT model on credit card transaction or PRSA dataset run :

$ python main.py --do_train --mlm --field_ce --lm_type bert 
                 --field_hs 64 --data_type [prsa/card] 
                 --output_dir [output_dir]

Tabular GPT2

To train a tabular GPT2 model on credit card transactions for a particular user-id :


$ python main.py --do_train --lm_type gpt2 --field_ce --flatten --data_type card 
                 --data_root [path_to_data] --user_ids [user-id] 
                 --output_dir [output_dir]

Description of some options (more can be found in args.py):

--data_type choices are prsa and card for Beijing PM2.5 dataset and credit-card transaction dataset respecitively.
--mlm for masked language model; option for transformer trainer for BERT
--field_hs hidden size for field level transformer
--lm_type choices from bert and gpt2
--user_ids option to pick only transacations from particular user ids.

Citation

@inproceedings{padhi2021tabular,
  title={Tabular transformers for modeling multivariate time series},
  author={Padhi, Inkit and Schiff, Yair and Melnyk, Igor and Rigotti, Mattia and Mroueh, Youssef and Dognin, Pierre and Ross, Jerret and Nair, Ravi and Altman, Erik},
  booktitle={ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={3565--3569},
  year={2021},
  organization={IEEE},
  url={https://ieeexplore.ieee.org/document/9414142}
}

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