# tab-transformer-pytorch

**Repository Path**: frontxiang/tab-transformer-pytorch

## Basic Information

- **Project Name**: tab-transformer-pytorch
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-16
- **Last Updated**: 2021-09-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<img src="./tab.png" width="250px"></img>

## Tab Transformer

Implementation of <a href="https://arxiv.org/abs/2012.06678">Tab Transformer</a>, attention network for tabular data, in Pytorch. This simple architecture came within a hair's breadth of GBDT's performance.

## Install

```bash
$ pip install tab-transformer-pytorch
```

## Usage

```python
import torch
import torch.nn as nn
from tab_transformer_pytorch import TabTransformer

cont_mean_std = torch.randn(10, 2)

model = TabTransformer(
    categories = (10, 5, 6, 5, 8),      # tuple containing the number of unique values within each category
    num_continuous = 10,                # number of continuous values
    dim = 32,                           # dimension, paper set at 32
    dim_out = 1,                        # binary prediction, but could be anything
    depth = 6,                          # depth, paper recommended 6
    heads = 8,                          # heads, paper recommends 8
    attn_dropout = 0.1,                 # post-attention dropout
    ff_dropout = 0.1,                   # feed forward dropout
    mlp_hidden_mults = (4, 2),          # relative multiples of each hidden dimension of the last mlp to logits
    mlp_act = nn.ReLU(),                # activation for final mlp, defaults to relu, but could be anything else (selu etc)
    continuous_mean_std = cont_mean_std # (optional) - normalize the continuous values before layer norm
)

x_categ = torch.randint(0, 5, (1, 5))     # category values, from 0 - max number of categories, in the order as passed into the constructor above
x_cont = torch.randn(1, 10)               # assume continuous values are already normalized individually

pred = model(x_categ, x_cont)
```

## Unsupervised Training

To undergo the type of unsupervised training described in the paper, you can first convert your categories tokens to the appropriate unique ids, and then use <a href="https://github.com/lucidrains/electra-pytorch">Electra</a> on `model.transformer`.

## Citations

```bibtex
@misc{huang2020tabtransformer,
    title={TabTransformer: Tabular Data Modeling Using Contextual Embeddings}, 
    author={Xin Huang and Ashish Khetan and Milan Cvitkovic and Zohar Karnin},
    year={2020},
    eprint={2012.06678},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```