A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.
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Hypernets is a general AutoML framework, based on which it can implement automatic optimization tools for various machine learning frameworks and libraries, including deep learning frameworks such as tensorflow, keras, pytorch, and machine learning libraries like sklearn, lightgbm, xgboost, etc. It also adopted various state-of-the-art optimization algorithms, including but not limited to evolution algorithm, monte carlo tree search for single objective optimization and multi-objective optimization algorithms such as MOEA/D,NSGA-II,R-NSGA-II. We introduced an abstract search space representation, taking into account the requirements of hyperparameter optimization and neural architecture search(NAS), making Hypernets a general framework that can adapt to various automated machine learning needs. As an abstraction computing layer, tabular toolbox, has successfully implemented in various tabular data types: pandas, dask, cudf, etc.
Install Hypernets with conda
from the channel conda-forge:
conda install -c conda-forge hypernets
Install Hypernets with different options:
pip install hypernets
pip install hypernets[notebook]
pip install hypernets[dask]
jieba
package before running Hypernets.pip install hypernets[zhcn]
pip install hypernets[all]
To Verify your installation:
python -m hypernets.examples.smoke_testing
If you use Hypernets in your research, please cite us as follows:
Jian Yang, Xuefeng Li, Haifeng Wu. Hypernets: A General Automated Machine Learning Framework. https://github.com/DataCanvasIO/Hypernets. 2020. Version 0.2.x.
BibTex:
@misc{hypernets,
author={Jian Yang, Xuefeng Li, Haifeng Wu},
title={{Hypernets}: { A General Automated Machine Learning Framework}},
howpublished={https://github.com/DataCanvasIO/Hypernets},
note={Version 0.2.x},
year={2020}
}
Hypernets is an open source project created by DataCanvas.