Awesome
<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.
Update: Amazon AI claims to have beaten GBDT with Attention on <a href="https://arxiv.org/abs/2311.11694">a real-world tabular dataset (predicting shipping cost)</a>.
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) # (1, 1)
FT Transformer
<img src="./tab-vs-ft.png" width="500px"></img>
<a href="https://arxiv.org/abs/2106.11959v2">This paper</a> from Yandex improves on Tab Transformer by using a simpler scheme for embedding the continuous numerical values as shown in the diagram above, courtesy of <a href="https://www.reddit.com/r/MachineLearning/comments/yhdqlj/project_improving_deep_learning_for_tabular_data/">this reddit post</a>.
Included in this repository just for convenient comparison to Tab Transformer
import torch
from tab_transformer_pytorch import FTTransformer
model = FTTransformer(
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
)
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_numer = torch.randn(1, 10) # numerical value
pred = model(x_categ, x_numer) # (1, 1)
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
.
Todo
- consider https://arxiv.org/abs/2203.05556
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}
}
@article{Gorishniy2021RevisitingDL,
title = {Revisiting Deep Learning Models for Tabular Data},
author = {Yu. V. Gorishniy and Ivan Rubachev and Valentin Khrulkov and Artem Babenko},
journal = {ArXiv},
year = {2021},
volume = {abs/2106.11959}
}