A flexible tabular prediction model that handles variable-column input tables.
Project description
TransTab: A flexible tabular prediction model [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.
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:
@article{wang2022transtab,
title = {TransTab: Learning Transferable Tabular Transformers Across Tables},
author = {Wang, Zifeng and Sun, Jimeng},
journal={arXiv preprint arXiv:2205.09328},
year = {2022},
}
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