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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.
- autox_competition: mainly for tabular table data mining competitions
- autox_server: automl service for online deployment
- autox_interpreter: machine learning interpretable function
- What is AutoX?
- What does AutoX contain?
- Join-the-community
- How to contribute for AutoX
- Table of Contents
- Installation
- Quick Start
- Evaluation
- TODO
- Troubleshooting
1. git clone https://github.com/4paradigm/autox.git
2. cd autox
3. python setup.py 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/
see demo folder
| 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% |
| 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 |
| 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 |
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!
| error message | Solution |
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