TransTab: A flexible transferable tabular learning framework [arxiv]

Document is available at https://transtab.readthedocs.io/en/latest/index.html.

News!

• [08/31/22] 0.0.2 Support encode tabular inputs into embeddings directly. An example is provided here. Several bugs are fixed.

TODO

• Table embedding.

• Add support to direct process table with missing values.

• Add regression support.

Features

This repository provides the python package transtab for flexible tabular prediction model. The basic usage of transtab can be done in a couple of lines!

import transtab

# load dataset by specifying dataset name
allset, trainset, valset, testset, cat_cols, num_cols, bin_cols \
     = transtab.load_data('credit-g')

# build classifier
model = transtab.build_classifier(cat_cols, num_cols, bin_cols)

# start training
transtab.train(model, trainset, valset, **training_arguments)

# make predictions, df_x is a pd.DataFrame with shape (n, d)
# return the predictions ypred with shape (n, 1) if binary classification;
# (n, n_class) if multiclass classification.
ypred = transtab.predict(model, df_x)

It's easy, isn't it?

How to install

First, download the right pytorch version following the guide on https://pytorch.org/get-started/locally/.

Then try to install from pypi directly:

pip install transtab

or

pip install git+https://github.com/RyanWangZf/transtab.git

Please refer to for more guidance on installation and troubleshooting.

Transfer learning across tables

A novel feature of transtab is its ability to learn from multiple distinct tables. It is easy to trigger the training like

# load the pretrained transtab model
model = transtab.build_classifier(checkpoint='./ckpt')

# load a new tabular dataset
allset, trainset, valset, testset, cat_cols, num_cols, bin_cols \
     = transtab.load_data('credit-approval')

# update categorical/numerical/binary column map of the loaded model
model.update({'cat':cat_cols,'num':num_cols,'bin':bin_cols})

# then we just trigger the training on the new data
transtab.train(model, trainset, valset, **training_arguments)

Contrastive pretraining on multiple tables

We can also conduct contrastive pretraining on multiple distinct tables like

# load from multiple tabular datasets
dataname_list = ['credit-g', 'credit-approval']
allset, trainset, valset, testset, cat_cols, num_cols, bin_cols \
     = transtab.load_data(dataname_list)

# build contrastive learner, set supervised=True for supervised VPCL
model, collate_fn = transtab.build_contrastive_learner(
    cat_cols, num_cols, bin_cols, supervised=True)

# start contrastive pretraining training
transtab.train(model, trainset, valset, collate_fn=collate_fn, **training_arguments)

Citation

If you find this package useful, please consider citing the following paper:

@inproceedings{wang2022transtab,
  title={TransTab: Learning Transferable Tabular Transformers Across Tables},
  author={Wang, Zifeng and Sun, Jimeng},
  booktitle={Advances in Neural Information Processing Systems},
  year={2022}
}