Skip to main content

An General Automated Machine Learning Framework

Project description

Python Versions Downloads PyPI Version

We Are Hiring!

Dear folks, we are offering challenging opportunities located in Beijing for both professionals and students who are keen on AutoML/NAS. Come be a part of DataCanvas! Please send your CV to yangjian@zetyun.com. (Application deadline: TBD.)

Hypernets: A General Automated Machine Learning Framework

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.

Overview

Conceptual Model

Illustration of the Search Space

What's NEW !

Installation

Conda

Install Hypernets with conda from the channel conda-forge:

conda install -c conda-forge hypernets

Pip

Install Hypernets with different options:

  • Typical installation:
pip install hypernets
  • To run Hypernets in JupyterLab/Jupyter notebook, install with command:
pip install hypernets[notebook]
  • To run Hypernets in distributed Dask cluster, install with command:
pip install hypernets[dask]
  • To support dataset with simplified Chinese in feature generation,
    • Install jieba package before running Hypernets.
    • OR install Hypernets with command:
pip install hypernets[zhcn]
  • Install all above with one command:
pip install hypernets[all]

To Verify your installation:

python -m hypernets.examples.smoke_testing

Related Links

Documents

Neural Architecture Search

Hypernets related projects

  • Hypernets: A general automated machine learning (AutoML) framework.
  • HyperGBM: A full pipeline AutoML tool integrated various GBM models.
  • HyperDT/DeepTables: An AutoDL tool for tabular data.
  • HyperTS: A full pipeline AutoML&AutoDL tool for time series datasets.
  • HyperKeras: An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras.
  • HyperBoard: A visualization tool for Hypernets.
  • Cooka: Lightweight interactive AutoML system.

DataCanvas AutoML Toolkit

Citation

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}
}

DataCanvas

Hypernets is an open source project created by DataCanvas.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hypernets-0.3.2.tar.gz (1.7 MB view details)

Uploaded Source

Built Distribution

hypernets-0.3.2-py3-none-any.whl (1.8 MB view details)

Uploaded Python 3

File details

Details for the file hypernets-0.3.2.tar.gz.

File metadata

  • Download URL: hypernets-0.3.2.tar.gz
  • Upload date:
  • Size: 1.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.17

File hashes

Hashes for hypernets-0.3.2.tar.gz
Algorithm Hash digest
SHA256 0e36a88f590260320bba89c33c0cb65932323db503e460f41aca50451f8e8f50
MD5 f7b82d99a8d1b2440d20b64603dfef3a
BLAKE2b-256 9af904b685c4b81c4c7c6d42251ccea0422822e4bbdb90294704f7b4696e9958

See more details on using hashes here.

File details

Details for the file hypernets-0.3.2-py3-none-any.whl.

File metadata

  • Download URL: hypernets-0.3.2-py3-none-any.whl
  • Upload date:
  • Size: 1.8 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.17

File hashes

Hashes for hypernets-0.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 2e7ef04b8419f724d9e77af621554d795f31e2aabfe26b9428ca342b7e901353
MD5 94fb63586605806521f88d6ce2558be9
BLAKE2b-256 a4fcc927105da5961bdb4c774fae27f9c8fad6e76d0f89251a85c7bfa1a7bcc2

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page