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@@ -0,0 +1 @@ +/nevergrad-0.6.0.tar.gz diff --git a/python-nevergrad.spec b/python-nevergrad.spec new file mode 100644 index 0000000..99d2cd4 --- /dev/null +++ b/python-nevergrad.spec @@ -0,0 +1,457 @@ +%global _empty_manifest_terminate_build 0 +Name: python-nevergrad +Version: 0.6.0 +Release: 1 +Summary: A Python toolbox for performing gradient-free optimization +License: MIT +URL: https://github.com/facebookresearch/nevergrad +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/06/ea/2e1f13a237258c30444aa3573040ef81723f4442c58de4af476700e62797/nevergrad-0.6.0.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-cma +Requires: python3-bayesian-optimization +Requires: python3-typing-extensions +Requires: python3-pandas +Requires: python3-black +Requires: python3-mypy +Requires: python3-pytest +Requires: python3-pytest-cov +Requires: python3-pylint +Requires: python3-wheel +Requires: python3-setuptools +Requires: python3-sphinx +Requires: python3-sphinx-rtd-theme +Requires: python3-recommonmark +Requires: python3-twine +Requires: python3-autodocsumm +Requires: python3-pandas +Requires: python3-pyparsing +Requires: python3-docutils +Requires: python3-requests +Requires: python3-xlwt +Requires: python3-xlrd +Requires: python3-opencv-python +Requires: python3-matplotlib +Requires: python3-gym +Requires: python3-gym-anm +Requires: python3-pygame +Requires: python3-torch +Requires: python3-hiplot +Requires: python3-fcmaes +Requires: python3-openpyxl +Requires: python3-pyproj +Requires: python3-Pillow +Requires: python3-tqdm +Requires: python3-torchvision +Requires: python3-pyomo +Requires: python3-mixsimulator +Requires: python3-hyperopt +Requires: python3-IOHexperimenter +Requires: python3-cdt +Requires: python3-tensorflow-estimator +Requires: python3-scikit-learn +Requires: python3-scikit-image +Requires: python3-tensorflow +Requires: python3-image-quality +Requires: python3-keras +Requires: python3-pymoo +Requires: python3-Keras-Preprocessing +Requires: python3-silence-tensorflow +Requires: python3-tensorflow-probability +Requires: python3-bayes-optim +Requires: python3-nlopt +Requires: python3-pybullet +Requires: python3-box2d-py +Requires: python3-glfw +Requires: python3-mujoco +Requires: python3-olymp +Requires: python3-requests +Requires: python3-xlwt +Requires: python3-xlrd +Requires: python3-opencv-python +Requires: python3-matplotlib +Requires: python3-gym +Requires: python3-gym-anm +Requires: python3-pygame +Requires: python3-torch +Requires: python3-hiplot +Requires: python3-fcmaes +Requires: python3-pandas +Requires: python3-openpyxl +Requires: python3-pyproj +Requires: python3-Pillow +Requires: python3-tqdm +Requires: python3-torchvision +Requires: python3-pyomo +Requires: python3-mixsimulator +Requires: python3-hyperopt +Requires: python3-IOHexperimenter +Requires: python3-cdt +Requires: python3-tensorflow-estimator +Requires: python3-scikit-learn +Requires: python3-scikit-image +Requires: python3-tensorflow +Requires: python3-image-quality +Requires: python3-keras +Requires: python3-pymoo +Requires: python3-Keras-Preprocessing +Requires: python3-silence-tensorflow +Requires: python3-tensorflow-probability +Requires: python3-bayes-optim +Requires: python3-nlopt +Requires: python3-pybullet +Requires: python3-box2d-py +Requires: python3-glfw +Requires: python3-mujoco +Requires: python3-olymp +Requires: python3-black +Requires: python3-mypy +Requires: python3-pytest +Requires: python3-pytest-cov +Requires: python3-pylint +Requires: python3-wheel +Requires: python3-setuptools +Requires: python3-sphinx +Requires: python3-sphinx-rtd-theme +Requires: python3-recommonmark +Requires: python3-twine +Requires: python3-autodocsumm +Requires: python3-pandas +Requires: python3-pyparsing +Requires: python3-docutils + +%description +[](https://opensource.fb.com/support-ukraine) [](https://circleci.com/gh/facebookresearch/nevergrad/tree/main) + +# Nevergrad - A gradient-free optimization platform + + + + +`nevergrad` is a Python 3.6+ library. It can be installed with: + +``` +pip install nevergrad +``` + +More installation options, including windows installation, and complete instructions are available in the "Getting started" section of the [**documentation**](https://facebookresearch.github.io/nevergrad/). + +You can join Nevergrad users Facebook group [here](https://www.facebook.com/groups/nevergradusers/). + +Minimizing a function using an optimizer (here `NGOpt`) is straightforward: + +```python +import nevergrad as ng + +def square(x): + return sum((x - .5)**2) + +optimizer = ng.optimizers.NGOpt(parametrization=2, budget=100) +recommendation = optimizer.minimize(square) +print(recommendation.value) # recommended value +>>> [0.49971112 0.5002944] +``` + +`nevergrad` can also support bounded continuous variables as well as discrete variables, and mixture of those. +To do this, one can specify the input space: + +```python +import nevergrad as ng + +def fake_training(learning_rate: float, batch_size: int, architecture: str) -> float: + # optimal for learning_rate=0.2, batch_size=4, architecture="conv" + return (learning_rate - 0.2)**2 + (batch_size - 4)**2 + (0 if architecture == "conv" else 10) + +# Instrumentation class is used for functions with multiple inputs +# (positional and/or keywords) +parametrization = ng.p.Instrumentation( + # a log-distributed scalar between 0.001 and 1.0 + learning_rate=ng.p.Log(lower=0.001, upper=1.0), + # an integer from 1 to 12 + batch_size=ng.p.Scalar(lower=1, upper=12).set_integer_casting(), + # either "conv" or "fc" + architecture=ng.p.Choice(["conv", "fc"]) +) + +optimizer = ng.optimizers.NGOpt(parametrization=parametrization, budget=100) +recommendation = optimizer.minimize(fake_training) + +# show the recommended keyword arguments of the function +print(recommendation.kwargs) +>>> {'learning_rate': 0.1998, 'batch_size': 4, 'architecture': 'conv'} +``` + +Learn more on parametrization in the [**documentation**](https://facebookresearch.github.io/nevergrad/)! + + + +*Convergence of a population of points to the minima with two-points DE.* + + +## Documentation + +Check out our [**documentation**](https://facebookresearch.github.io/nevergrad/)! It's still a work in progress, don't hesitate to submit issues and/or PR to update it and make it clearer! + + +## Citing + +```bibtex +@misc{nevergrad, + author = {J. Rapin and O. Teytaud}, + title = {{Nevergrad - A gradient-free optimization platform}}, + year = {2018}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://GitHub.com/FacebookResearch/Nevergrad}}, +} +``` + +## License + +`nevergrad` is released under the MIT license. See [LICENSE](https://github.com/facebookresearch/nevergrad/blob/0.6.0/LICENSE) for additional details about it. +See also our [Terms of Use](https://opensource.facebook.com/legal/terms) and [Privacy Policy](https://opensource.facebook.com/legal/privacy). + + + + +%package -n python3-nevergrad +Summary: A Python toolbox for performing gradient-free optimization +Provides: python-nevergrad +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-nevergrad +[](https://opensource.fb.com/support-ukraine) [](https://circleci.com/gh/facebookresearch/nevergrad/tree/main) + +# Nevergrad - A gradient-free optimization platform + + + + +`nevergrad` is a Python 3.6+ library. It can be installed with: + +``` +pip install nevergrad +``` + +More installation options, including windows installation, and complete instructions are available in the "Getting started" section of the [**documentation**](https://facebookresearch.github.io/nevergrad/). + +You can join Nevergrad users Facebook group [here](https://www.facebook.com/groups/nevergradusers/). + +Minimizing a function using an optimizer (here `NGOpt`) is straightforward: + +```python +import nevergrad as ng + +def square(x): + return sum((x - .5)**2) + +optimizer = ng.optimizers.NGOpt(parametrization=2, budget=100) +recommendation = optimizer.minimize(square) +print(recommendation.value) # recommended value +>>> [0.49971112 0.5002944] +``` + +`nevergrad` can also support bounded continuous variables as well as discrete variables, and mixture of those. +To do this, one can specify the input space: + +```python +import nevergrad as ng + +def fake_training(learning_rate: float, batch_size: int, architecture: str) -> float: + # optimal for learning_rate=0.2, batch_size=4, architecture="conv" + return (learning_rate - 0.2)**2 + (batch_size - 4)**2 + (0 if architecture == "conv" else 10) + +# Instrumentation class is used for functions with multiple inputs +# (positional and/or keywords) +parametrization = ng.p.Instrumentation( + # a log-distributed scalar between 0.001 and 1.0 + learning_rate=ng.p.Log(lower=0.001, upper=1.0), + # an integer from 1 to 12 + batch_size=ng.p.Scalar(lower=1, upper=12).set_integer_casting(), + # either "conv" or "fc" + architecture=ng.p.Choice(["conv", "fc"]) +) + +optimizer = ng.optimizers.NGOpt(parametrization=parametrization, budget=100) +recommendation = optimizer.minimize(fake_training) + +# show the recommended keyword arguments of the function +print(recommendation.kwargs) +>>> {'learning_rate': 0.1998, 'batch_size': 4, 'architecture': 'conv'} +``` + +Learn more on parametrization in the [**documentation**](https://facebookresearch.github.io/nevergrad/)! + + + +*Convergence of a population of points to the minima with two-points DE.* + + +## Documentation + +Check out our [**documentation**](https://facebookresearch.github.io/nevergrad/)! It's still a work in progress, don't hesitate to submit issues and/or PR to update it and make it clearer! + + +## Citing + +```bibtex +@misc{nevergrad, + author = {J. Rapin and O. Teytaud}, + title = {{Nevergrad - A gradient-free optimization platform}}, + year = {2018}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://GitHub.com/FacebookResearch/Nevergrad}}, +} +``` + +## License + +`nevergrad` is released under the MIT license. See [LICENSE](https://github.com/facebookresearch/nevergrad/blob/0.6.0/LICENSE) for additional details about it. +See also our [Terms of Use](https://opensource.facebook.com/legal/terms) and [Privacy Policy](https://opensource.facebook.com/legal/privacy). + + + + +%package help +Summary: Development documents and examples for nevergrad +Provides: python3-nevergrad-doc +%description help +[](https://opensource.fb.com/support-ukraine) [](https://circleci.com/gh/facebookresearch/nevergrad/tree/main) + +# Nevergrad - A gradient-free optimization platform + + + + +`nevergrad` is a Python 3.6+ library. It can be installed with: + +``` +pip install nevergrad +``` + +More installation options, including windows installation, and complete instructions are available in the "Getting started" section of the [**documentation**](https://facebookresearch.github.io/nevergrad/). + +You can join Nevergrad users Facebook group [here](https://www.facebook.com/groups/nevergradusers/). + +Minimizing a function using an optimizer (here `NGOpt`) is straightforward: + +```python +import nevergrad as ng + +def square(x): + return sum((x - .5)**2) + +optimizer = ng.optimizers.NGOpt(parametrization=2, budget=100) +recommendation = optimizer.minimize(square) +print(recommendation.value) # recommended value +>>> [0.49971112 0.5002944] +``` + +`nevergrad` can also support bounded continuous variables as well as discrete variables, and mixture of those. +To do this, one can specify the input space: + +```python +import nevergrad as ng + +def fake_training(learning_rate: float, batch_size: int, architecture: str) -> float: + # optimal for learning_rate=0.2, batch_size=4, architecture="conv" + return (learning_rate - 0.2)**2 + (batch_size - 4)**2 + (0 if architecture == "conv" else 10) + +# Instrumentation class is used for functions with multiple inputs +# (positional and/or keywords) +parametrization = ng.p.Instrumentation( + # a log-distributed scalar between 0.001 and 1.0 + learning_rate=ng.p.Log(lower=0.001, upper=1.0), + # an integer from 1 to 12 + batch_size=ng.p.Scalar(lower=1, upper=12).set_integer_casting(), + # either "conv" or "fc" + architecture=ng.p.Choice(["conv", "fc"]) +) + +optimizer = ng.optimizers.NGOpt(parametrization=parametrization, budget=100) +recommendation = optimizer.minimize(fake_training) + +# show the recommended keyword arguments of the function +print(recommendation.kwargs) +>>> {'learning_rate': 0.1998, 'batch_size': 4, 'architecture': 'conv'} +``` + +Learn more on parametrization in the [**documentation**](https://facebookresearch.github.io/nevergrad/)! + + + +*Convergence of a population of points to the minima with two-points DE.* + + +## Documentation + +Check out our [**documentation**](https://facebookresearch.github.io/nevergrad/)! It's still a work in progress, don't hesitate to submit issues and/or PR to update it and make it clearer! + + +## Citing + +```bibtex +@misc{nevergrad, + author = {J. Rapin and O. Teytaud}, + title = {{Nevergrad - A gradient-free optimization platform}}, + year = {2018}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://GitHub.com/FacebookResearch/Nevergrad}}, +} +``` + +## License + +`nevergrad` is released under the MIT license. See [LICENSE](https://github.com/facebookresearch/nevergrad/blob/0.6.0/LICENSE) for additional details about it. +See also our [Terms of Use](https://opensource.facebook.com/legal/terms) and [Privacy Policy](https://opensource.facebook.com/legal/privacy). + + + + +%prep +%autosetup -n nevergrad-0.6.0 + +%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-nevergrad -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.0-1 +- Package Spec generated @@ -0,0 +1 @@ +404b64a20e0501d40d7e7abe1302ebdb nevergrad-0.6.0.tar.gz |