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An General Automated Machine Learning Framework

Project description

Hypernets

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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. 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.

Overview

Conceptual Model

Illustration of the Search Space

Installation

Install Hypernets with pip command:

pip install hypernets

Optional, to run Hypernets in JupyterLab notebooks, install Hypernets and JupyterLab with command:

pip install hypernets[notebook]

Optional, to run Hypernets in distributed Dask cluster, install Hypernets with command:

pip install hypernets[dask]

Optional, to support dataset with simplified Chinese in feature generation, install jieba package before run Hypernets, or install Hypernets with command:

pip install hypernets[zhcn]

Optional, install all Hypernets components and dependencies with one command:

pip install hypernets[all]

Verify installation:

python -m hypernets.examples.smoke_testing

Related Links

Hypernets related projects

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

DataCanvas AutoML Toolkit

Documents

Neural Architecture Search

DataCanvas

Hypernets is an open source project created by DataCanvas.

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