tab transformer pytorch

Tab Transformator

Implementasi Tab Transformer, jaringan perhatian untuk data tabular, di Pytorch. Arsitektur sederhana ini hampir sama dengan kinerja GBDT.

Pembaruan: Amazon AI mengklaim telah mengalahkan GBDT dengan Perhatian pada kumpulan data tabel dunia nyata (memprediksi biaya pengiriman).

Memasang

$ pip install tab-transformer-pytorch

Penggunaan

 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) 

Transformator FT

Makalah dari Yandex ini menyempurnakan Tab Transformer dengan menggunakan skema yang lebih sederhana untuk menyematkan nilai numerik berkelanjutan seperti yang ditunjukkan pada diagram di atas, milik postingan reddit ini.

Disertakan dalam repositori ini hanya untuk perbandingan mudah dengan 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) 

Pelatihan Tanpa Pengawasan

Untuk menjalani jenis pelatihan tanpa pengawasan yang dijelaskan dalam makalah, Anda dapat terlebih dahulu mengonversi token kategori Anda ke id unik yang sesuai, lalu menggunakan Electra pada model.transformer .

Semua yang harus dilakukan

• pertimbangkan https://arxiv.org/abs/2203.05556

Kutipan

 @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 }
}

 @article { Zhu2024HyperConnections ,
    title   = { Hyper-Connections } ,
    author  = { Defa Zhu and Hongzhi Huang and Zihao Huang and Yutao Zeng and Yunyao Mao and Banggu Wu and Qiyang Min and Xun Zhou } ,
    journal = { ArXiv } ,
    year    = { 2024 } ,
    volume  = { abs/2409.19606 } ,
    url     = { https://api.semanticscholar.org/CorpusID:272987528 }
}