What-is-AutoX?

June 8, 2022 · View on GitHub

What-is-AutoX?

AutoX is an efficient AutoML tool, and it is designed for the tabular data modelling for real-world datasets. Its features include:

SOTA: AutoX outperforms other solutions in many competition datasets(see Evaluation).
Easy to use: The design of interfaces is similar to sklearn.
Generic & Universal: Supporting tabular data, including binary classification, multi-class classification and regression problems.
Auto: Fully automated pipeline without human-intervention.
Out of the box: Providing flexible modules which can be used alone.
Summary of magics: Organize and publish magics of competitions.

What-does-AutoX-contain?

autox_competition: mainly for tabular table data mining competitions
autox_server: automl service for online deployment
autox_interpreter: machine learning interpretable function

Join-the-community

How-to-contribute-to-AutoX?

how to contribute

Table-of-Contents

What is AutoX?
What does AutoX contain?
Join-the-community
How to contribute for AutoX
Table of Contents
Installation
Quick Start
Evaluation
TODO
Troubleshooting

Installation

github repository installation

1. git clone https://github.com/4paradigm/autox.git
2. cd autox
3. python setup.py install

pip install

## The pip installation package may not be updated in time. It is recommended to install the latest version using the github installation method.
!pip install automl-x -i https://www.pypi.org/simple/

Quick-Start

Community case

Car sales forecast

Competition case

see demo folder

Comparison to other AutoML frameworks

Percentage improvement under different tasks

data_type	Compare To AutoGluon	Compare To H2o
binary classification	20.44%	2.98%
regression	37.54%	39.66%
time-series	28.40%	32.46%

Evaluation

index	data_type	data_name(link)	metric	AutoX	AutoGluon	H2o
1	regression	zhidemai	mse	1.1231	1.9466	1.1927
2	regression	Tabular Playground Series - Aug 2021	rmse	7.87731	10.3944	7.8895
3	regression	House Prices	rmse	0.13043	0.13104	0.13161
4	binary classification	Titanic	accuracy	0.77751	0.78229	0.79186

Detailed dataset comparison

data_type	single-or-multi	data_name	metric	AutoX	AutoGluon	H2o
binary classification	single-table	Springleaf	auc	0.78865	0.61141	0.78186
binary classification-nlp	single-table	stumbleupon	auc	0.87177	0.81025	0.79039
binary classification	single-table	santander	auc	0.89196	0.64643	0.88775
binary classification	multi-table	IEEE	accuracy	0.920809	0.724925	0.907818
regression	single-table	ventilator	mae	0.755	8.434	4.221
regression	single-table	Allstate Claims Severity	mae	1137.07885	1173.35917	1163.12014
regression	single-table	zhidemai	mse	1.0034	1.9466	1.1927
regression	single-table	Tabular Playground Series - Aug 2021	rmse	7.87731	10.3944	7.8895
regression	single-table	House Prices	rmse	0.13043	0.13104	0.13161
regression	single-table	Restaurant Revenue	rmse	2133204.32146	31913829.59876	28958013.69639
regression	multi-table	Elo Merchant Category Recommendation	rmse	3.72228	3.80801	22.88899
regression-ts	single-table	Demand Forecasting	smape	13.79241	25.39182	18.89678
regression-ts	multi-table	Walmart Recruiting	wmae	4660.99174	5024.16179	5128.31622
regression-ts	multi-table	Rossmann Store Sales	RMSPE	0.13850	0.20453	0.35757
regression-cv	single-table	PetFinder	rmse	20.1327	23.1732	21.0586

AutoX Achievements

Enterprise support

Competition winning

2021 Alibaba Cloud Infrastructure Supply Chain Competition - Champion Scheme

TODO

After the function development is completed, release the corresponding demo

Multi-classification tasks

If there are other functions that you want AutoX to support, please submit an issue! Welcome to fill in the user survey questionnaire to make AutoX better!

Troubleshooting

error message	Solution