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|
%global _empty_manifest_terminate_build 0
Name: python-emukit
Version: 0.4.10
Release: 1
Summary: Toolkit for decision making under uncertainty.
License: Apache License 2.0
URL: https://github.com/emukit/emukit
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ae/47/4ec0eeffa5d3b03637538037c16cda7ca222c430207e95bc5244258f0dc4/emukit-0.4.10.tar.gz
BuildArch: noarch
%description
# Emukit
[](https://github.com/EmuKit/emukit/actions?query=workflow%3ATests) |
[](https://emukit.readthedocs.io/en/latest/?badge=latest) |
[](https://codecov.io/gh/emukit/emukit) |
[](https://github.com/emukit/emukit/blob/main/LICENSE)
[Website](https://emukit.github.io/) |
[Documentation](https://emukit.readthedocs.io/) |
[Contribution Guide](CONTRIBUTING.md)
Emukit is a highly adaptable Python toolkit for enriching decision making under uncertainty. This is particularly pertinent to complex systems where data is scarce or difficult to acquire. In these scenarios, propagating well-calibrated uncertainty estimates within a design loop or computational pipeline ensures that constrained resources are used effectively.
The main features currently available in Emukit are:
* **Multi-fidelity emulation:** build surrogate models when data is obtained from multiple information sources that have different fidelity and/or cost;
* **Bayesian optimisation:** optimise physical experiments and tune parameters of machine learning algorithms;
* **Experimental design/Active learning:** design the most informative experiments and perform active learning with machine learning models;
* **Sensitivity analysis:** analyse the influence of inputs on the outputs of a given system;
* **Bayesian quadrature:** efficiently compute the integrals of functions that are expensive to evaluate.
Emukit is agnostic to the underlying modelling framework, which means you can use any tool of your choice in the Python ecosystem to build the machine learning model, and still be able to use Emukit.
## Installation
To install emukit, simply run
```
pip install emukit
```
For other install options, see our [documentation](https://emukit.readthedocs.io/en/latest/installation.html).
### Dependencies / Prerequisites
Emukit's primary dependencies are Numpy and GPy.
See [requirements](requirements/requirements.txt).
## Getting started
For examples see our [tutorial notebooks](http://nbviewer.jupyter.org/github/emukit/emukit/blob/main/notebooks/index.ipynb).
## Documentation
To learn more about Emukit, refer to our [documentation](https://emukit.readthedocs.io).
To learn about emulation as a concept, check out the [Emukit playground](https://github.com/amzn/Emukit-playground) project.
## Citing the library
If you are using emukit, we would appreciate if you could cite our paper about Emukit in your research:
@inproceedings{emukit2019,
author = {Paleyes, Andrei and Pullin, Mark and Mahsereci, Maren and McCollum, Cliff and Lawrence, Neil and González, Javier},
title = {Emulation of physical processes with Emukit},
booktitle = {Second Workshop on Machine Learning and the Physical Sciences, NeurIPS},
year = {2019}
}
The paper itself can be found on [arXiv](https://arxiv.org/abs/2110.13293).
## License
Emukit is licensed under Apache 2.0. Please refer to [LICENSE](LICENSE) and [NOTICE](NOTICE) for further license information.
%package -n python3-emukit
Summary: Toolkit for decision making under uncertainty.
Provides: python-emukit
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-emukit
# Emukit
[](https://github.com/EmuKit/emukit/actions?query=workflow%3ATests) |
[](https://emukit.readthedocs.io/en/latest/?badge=latest) |
[](https://codecov.io/gh/emukit/emukit) |
[](https://github.com/emukit/emukit/blob/main/LICENSE)
[Website](https://emukit.github.io/) |
[Documentation](https://emukit.readthedocs.io/) |
[Contribution Guide](CONTRIBUTING.md)
Emukit is a highly adaptable Python toolkit for enriching decision making under uncertainty. This is particularly pertinent to complex systems where data is scarce or difficult to acquire. In these scenarios, propagating well-calibrated uncertainty estimates within a design loop or computational pipeline ensures that constrained resources are used effectively.
The main features currently available in Emukit are:
* **Multi-fidelity emulation:** build surrogate models when data is obtained from multiple information sources that have different fidelity and/or cost;
* **Bayesian optimisation:** optimise physical experiments and tune parameters of machine learning algorithms;
* **Experimental design/Active learning:** design the most informative experiments and perform active learning with machine learning models;
* **Sensitivity analysis:** analyse the influence of inputs on the outputs of a given system;
* **Bayesian quadrature:** efficiently compute the integrals of functions that are expensive to evaluate.
Emukit is agnostic to the underlying modelling framework, which means you can use any tool of your choice in the Python ecosystem to build the machine learning model, and still be able to use Emukit.
## Installation
To install emukit, simply run
```
pip install emukit
```
For other install options, see our [documentation](https://emukit.readthedocs.io/en/latest/installation.html).
### Dependencies / Prerequisites
Emukit's primary dependencies are Numpy and GPy.
See [requirements](requirements/requirements.txt).
## Getting started
For examples see our [tutorial notebooks](http://nbviewer.jupyter.org/github/emukit/emukit/blob/main/notebooks/index.ipynb).
## Documentation
To learn more about Emukit, refer to our [documentation](https://emukit.readthedocs.io).
To learn about emulation as a concept, check out the [Emukit playground](https://github.com/amzn/Emukit-playground) project.
## Citing the library
If you are using emukit, we would appreciate if you could cite our paper about Emukit in your research:
@inproceedings{emukit2019,
author = {Paleyes, Andrei and Pullin, Mark and Mahsereci, Maren and McCollum, Cliff and Lawrence, Neil and González, Javier},
title = {Emulation of physical processes with Emukit},
booktitle = {Second Workshop on Machine Learning and the Physical Sciences, NeurIPS},
year = {2019}
}
The paper itself can be found on [arXiv](https://arxiv.org/abs/2110.13293).
## License
Emukit is licensed under Apache 2.0. Please refer to [LICENSE](LICENSE) and [NOTICE](NOTICE) for further license information.
%package help
Summary: Development documents and examples for emukit
Provides: python3-emukit-doc
%description help
# Emukit
[](https://github.com/EmuKit/emukit/actions?query=workflow%3ATests) |
[](https://emukit.readthedocs.io/en/latest/?badge=latest) |
[](https://codecov.io/gh/emukit/emukit) |
[](https://github.com/emukit/emukit/blob/main/LICENSE)
[Website](https://emukit.github.io/) |
[Documentation](https://emukit.readthedocs.io/) |
[Contribution Guide](CONTRIBUTING.md)
Emukit is a highly adaptable Python toolkit for enriching decision making under uncertainty. This is particularly pertinent to complex systems where data is scarce or difficult to acquire. In these scenarios, propagating well-calibrated uncertainty estimates within a design loop or computational pipeline ensures that constrained resources are used effectively.
The main features currently available in Emukit are:
* **Multi-fidelity emulation:** build surrogate models when data is obtained from multiple information sources that have different fidelity and/or cost;
* **Bayesian optimisation:** optimise physical experiments and tune parameters of machine learning algorithms;
* **Experimental design/Active learning:** design the most informative experiments and perform active learning with machine learning models;
* **Sensitivity analysis:** analyse the influence of inputs on the outputs of a given system;
* **Bayesian quadrature:** efficiently compute the integrals of functions that are expensive to evaluate.
Emukit is agnostic to the underlying modelling framework, which means you can use any tool of your choice in the Python ecosystem to build the machine learning model, and still be able to use Emukit.
## Installation
To install emukit, simply run
```
pip install emukit
```
For other install options, see our [documentation](https://emukit.readthedocs.io/en/latest/installation.html).
### Dependencies / Prerequisites
Emukit's primary dependencies are Numpy and GPy.
See [requirements](requirements/requirements.txt).
## Getting started
For examples see our [tutorial notebooks](http://nbviewer.jupyter.org/github/emukit/emukit/blob/main/notebooks/index.ipynb).
## Documentation
To learn more about Emukit, refer to our [documentation](https://emukit.readthedocs.io).
To learn about emulation as a concept, check out the [Emukit playground](https://github.com/amzn/Emukit-playground) project.
## Citing the library
If you are using emukit, we would appreciate if you could cite our paper about Emukit in your research:
@inproceedings{emukit2019,
author = {Paleyes, Andrei and Pullin, Mark and Mahsereci, Maren and McCollum, Cliff and Lawrence, Neil and González, Javier},
title = {Emulation of physical processes with Emukit},
booktitle = {Second Workshop on Machine Learning and the Physical Sciences, NeurIPS},
year = {2019}
}
The paper itself can be found on [arXiv](https://arxiv.org/abs/2110.13293).
## License
Emukit is licensed under Apache 2.0. Please refer to [LICENSE](LICENSE) and [NOTICE](NOTICE) for further license information.
%prep
%autosetup -n emukit-0.4.10
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-emukit -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.10-1
- Package Spec generated
|
