Package: hypernets
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 toyangjian@zetyun.com. (Application deadline: TBD.)Hypernets: A General Automated Machine Learning FrameworkHypernets 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.OverviewConceptual ModelIllustration of the Search SpaceWhat's NEW !New feature:Multi-objectives optimization supportNew feature:Performance and model complexity measurement metricsNew feature:Distributed computingandGPU accelerationbase on computational abstraction layerInstallationCondaInstall Hypernets withcondafrom the channelconda-forge:condainstall-cconda-forgehypernetsPipInstall Hypernets with different options:Typical installation:pipinstallhypernetsTo run Hypernets in JupyterLab/Jupyter notebook, install with command:pipinstallhypernets[notebook]To run Hypernets in distributed Dask cluster, install with command:pipinstallhypernets[dask]To support dataset with simplified Chinese in feature generation,Installjiebapackage before running Hypernets.OR install Hypernets with command:pipinstallhypernets[zhcn]Install all above with one command:pipinstallhypernets[all]ToVerifyyour installation:python-mhypernets.examples.smoke_testingRelated LinksA Brief Tutorial for Developing AutoML Tools with HypernetsDocumentsOverviewQuickStartSearch SpaceSearcherHyperModelExperimentNeural Architecture SearchDefine A DNN Search SpaceDefine A CNN Search SpaceDefine An ENAS Micro Search SpaceHypernets related projectsHypernets: 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.CitationIf 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}
}DataCanvasHypernets is an open source project created byDataCanvas.