# TransTab: A flexible transferable tabular learning framework [[arxiv]](https://arxiv.org/pdf/2205.09328.pdf)
[](https://badge.fury.io/py/transtab)
[](https://transtab.readthedocs.io/en/latest/?badge=latest)
[](https://opensource.org/licenses/BSD-2-Clause)
[](https://pepy.tech/project/transtab)
[](https://pepy.tech/project/transtab)
Document is available at https://transtab.readthedocs.io/en/latest/index.html.
Paper is available at https://arxiv.org/pdf/2205.09328.pdf.
5 min blog to understand TransTab at [realsunlab.medium.com](https://realsunlab.medium.com/transtab-learning-transferable-tabular-transformers-across-tables-1e34eec161b8)!
### News!
- [05/04/23] Check the version `0.0.5` of `TransTab`!
- [01/04/23] Check the version `0.0.3` of `TransTab`!
- [12/03/22] Check out our [[blog]](https://realsunlab.medium.com/transtab-learning-transferable-tabular-transformers-across-tables-1e34eec161b8) for a quick understanding of TransTab!
- [08/31/22] `0.0.2` Support encode tabular inputs into embeddings directly. An example is provided [here](examples/table_embedding.ipynb). Several bugs are fixed.
## TODO
- [x] 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!
```python
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:
```bash
pip install transtab
```
or
```bash
pip install git+https://github.com/RyanWangZf/transtab.git
```
Please refer to for [more guidance on installation](https://transtab.readthedocs.io/en/latest/install.html) 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
```python
# 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
```python
# 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:
```latex
@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}
}
```
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"description": "# TransTab: A flexible transferable tabular learning framework [[arxiv]](https://arxiv.org/pdf/2205.09328.pdf)\n\n\n[](https://badge.fury.io/py/transtab)\n[](https://transtab.readthedocs.io/en/latest/?badge=latest)\n[](https://opensource.org/licenses/BSD-2-Clause)\n\n\n[](https://pepy.tech/project/transtab)\n[](https://pepy.tech/project/transtab)\n\n\nDocument is available at https://transtab.readthedocs.io/en/latest/index.html.\n\nPaper is available at https://arxiv.org/pdf/2205.09328.pdf.\n\n5 min blog to understand TransTab at [realsunlab.medium.com](https://realsunlab.medium.com/transtab-learning-transferable-tabular-transformers-across-tables-1e34eec161b8)!\n\n### News!\n- [05/04/23] Check the version `0.0.5` of `TransTab`!\n\n- [01/04/23] Check the version `0.0.3` of `TransTab`!\n\n- [12/03/22] Check out our [[blog]](https://realsunlab.medium.com/transtab-learning-transferable-tabular-transformers-across-tables-1e34eec161b8) for a quick understanding of TransTab!\n\n- [08/31/22] `0.0.2` Support encode tabular inputs into embeddings directly. An example is provided [here](examples/table_embedding.ipynb). Several bugs are fixed.\n\n## TODO\n\n- [x] Table embedding.\n\n- [ ] Add support to direct process table with missing values.\n\n- [ ] Add regression support.\n\n### Features\nThis repository provides the python package `transtab` for flexible tabular prediction model. The basic usage of `transtab` can be done in a couple of lines!\n\n```python\nimport transtab\n\n# load dataset by specifying dataset name\nallset, trainset, valset, testset, cat_cols, num_cols, bin_cols \\\n = transtab.load_data('credit-g')\n\n# build classifier\nmodel = transtab.build_classifier(cat_cols, num_cols, bin_cols)\n\n# start training\ntranstab.train(model, trainset, valset, **training_arguments)\n\n# make predictions, df_x is a pd.DataFrame with shape (n, d)\n# return the predictions ypred with shape (n, 1) if binary classification;\n# (n, n_class) if multiclass classification.\nypred = transtab.predict(model, df_x)\n```\n\nIt's easy, isn't it?\n\n\n\n## How to install\n\nFirst, download the right ``pytorch`` version following the guide on https://pytorch.org/get-started/locally/.\n\nThen try to install from pypi directly:\n\n```bash\npip install transtab\n```\n\nor\n\n```bash\npip install git+https://github.com/RyanWangZf/transtab.git\n```\n\n\n\nPlease refer to for [more guidance on installation](https://transtab.readthedocs.io/en/latest/install.html) and troubleshooting.\n\n\n\n## Transfer learning across tables\n\nA novel feature of `transtab` is its ability to learn from multiple distinct tables. It is easy to trigger the training like\n\n```python\n# load the pretrained transtab model\nmodel = transtab.build_classifier(checkpoint='./ckpt')\n\n# load a new tabular dataset\nallset, trainset, valset, testset, cat_cols, num_cols, bin_cols \\\n = transtab.load_data('credit-approval')\n\n# update categorical/numerical/binary column map of the loaded model\nmodel.update({'cat':cat_cols,'num':num_cols,'bin':bin_cols})\n\n# then we just trigger the training on the new data\ntranstab.train(model, trainset, valset, **training_arguments)\n```\n\n\n\n## Contrastive pretraining on multiple tables\n\nWe can also conduct contrastive pretraining on multiple distinct tables like\n\n```python\n# load from multiple tabular datasets\ndataname_list = ['credit-g', 'credit-approval']\nallset, trainset, valset, testset, cat_cols, num_cols, bin_cols \\\n = transtab.load_data(dataname_list)\n\n# build contrastive learner, set supervised=True for supervised VPCL\nmodel, collate_fn = transtab.build_contrastive_learner(\n cat_cols, num_cols, bin_cols, supervised=True)\n\n# start contrastive pretraining training\ntranstab.train(model, trainset, valset, collate_fn=collate_fn, **training_arguments)\n```\n\n\n\n## Citation\n\nIf you find this package useful, please consider citing the following paper:\n\n```latex\n@inproceedings{wang2022transtab,\n title={TransTab: Learning Transferable Tabular Transformers Across Tables},\n author={Wang, Zifeng and Sun, Jimeng},\n booktitle={Advances in Neural Information Processing Systems},\n year={2022}\n}\n```\n",
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