YunJiao-Chen / tab-transformer-pytorch

Implementation of TabTransformer, attention network for tabular data, in Pytorch

Tab Transformer

Implementation of Tab Transformer, attention network for tabular data, in Pytorch. This simple architecture came within a hair's breadth of GBDT's performance.

Install

$ pip install tab-transformer-pytorch

Usage

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 Electra on model.transformer.

Citations

@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}
}