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+%global _empty_manifest_terminate_build 0
+Name:		python-smac
+Version:	2.0.0
+Release:	1
+Summary:	SMAC3, a Python implementation of 'Sequential Model-based Algorithm Configuration'.
+License:	BSD 3-Clause License
+URL:		https://www.automl.org/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/36/fe/9db1e219fa6e89653c64961f639fac2e7481c6550cdf01bc78731b0ae190/smac-2.0.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+# SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
+
+
+[![Tests](https://github.com/automl/SMAC3/actions/workflows/pytest.yml/badge.svg?branch=main)](https://github.com/automl/SMAC3/actions/workflows/pytest.yml)
+[![Documentation](https://github.com/automl/SMAC3/actions/workflows/docs.yml/badge.svg?branch=main)](https://github.com/automl/SMAC3/actions/workflows/docs.yml)
+[![codecov
+Status](https://codecov.io/gh/automl/SMAC3/branch/master/graph/badge.svg)](https://codecov.io/gh/automl/SMAC3)
+
+<img src="docs/images/logo.png" style="width: 50%;" />
+
+SMAC offers a robust and flexible framework for Bayesian Optimization to support users in determining well-performing 
+hyperparameter configurations for their (Machine Learning) algorithms, datasets and applications at hand. The main core 
+consists of Bayesian Optimization in combination with an aggressive racing mechanism to efficiently decide which of two configurations performs better.
+
+SMAC3 is written in Python3 and continuously tested with Python 3.8, 3.9, and 3.10. Its Random
+Forest is written in C++. In further texts, SMAC is representatively mentioned for SMAC3.
+
+> [Documentation](https://automl.github.io/SMAC3)
+
+> [Roadmap](https://github.com/orgs/automl/projects/5/views/2)
+
+
+## Important: Changes in v2.0
+
+With the next big major release of SMAC, we drastically boosted the user experience by improving the APIs and how the 
+pipelining is done (see [changelog](CHANGELOG.md)). All facades/intensifiers support multi-objective, multi-fidelity, 
+and multi-threading natively now! That includes having an ask-and-tell interface and continuing a run
+wherever you left off. pSMAC is removed because when specifying the number of workers, SMAC automatically uses 
+multi-threading for evaluating trials. When cleaning the code base, however, we removed the command-line 
+interface (calling a target function from a script is still supported), and runtime optimization. Also,
+python 3.7 is not supported anymore. If you depend on those functionalities, please keep using v1.4.
+
+We are excited to introduce the new major release and look forward to developing new features on the new code base. 
+We hope you enjoy this new user experience as much as we do. 🚀
+
+
+## Installation
+
+This instruction is for the installation on a Linux system, for Windows and Mac and further information see the [documentation](https://automl.github.io/SMAC3/main/1_installation.html).
+
+Create a new environment with python 3.10 and make sure swig is installed either on your system or
+inside the environment. We demonstrate the installation via anaconda in the following:
+
+Create and activate environment:
+```
+conda create -n SMAC python=3.10
+conda activate SMAC
+```
+
+Install swig:
+```
+conda install gxx_linux-64 gcc_linux-64 swig
+```
+
+Install SMAC via PyPI:
+```
+pip install smac
+```
+
+Or alternatively, clone the environment:
+```
+git clone https://github.com/automl/SMAC3.git && cd SMAC3
+pip install -e .[dev]
+```
+
+
+## Minimal Example
+
+```py
+from ConfigSpace import Configuration, ConfigurationSpace
+
+import numpy as np
+from smac import HyperparameterOptimizationFacade, Scenario
+from sklearn import datasets
+from sklearn.svm import SVC
+from sklearn.model_selection import cross_val_score
+
+iris = datasets.load_iris()
+
+
+def train(config: Configuration, seed: int = 0) -> float:
+    classifier = SVC(C=config["C"], random_state=seed)
+    scores = cross_val_score(classifier, iris.data, iris.target, cv=5)
+    return 1 - np.mean(scores)
+
+
+configspace = ConfigurationSpace({"C": (0.100, 1000.0)})
+
+# Scenario object specifying the optimization environment
+scenario = Scenario(configspace, deterministic=True, n_trials=200)
+
+# Use SMAC to find the best configuration/hyperparameters
+smac = HyperparameterOptimizationFacade(scenario, train)
+incumbent = smac.optimize()
+```
+
+More examples can be found in the [documentation](https://automl.github.io/SMAC3/main/examples/).
+
+## Visualization via DeepCAVE
+
+With DeepCAVE ([Repo](https://github.com/automl/DeepCAVE), [Paper](https://arxiv.org/abs/2206.03493)) you can visualize your SMAC runs. It is a visualization and analysis tool for AutoML (especially for the sub-problem
+hyperparameter optimization) runs.
+
+## License
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the 3-clause BSD license (please see the LICENSE file).
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+
+You should have received a copy of the 3-clause BSD license
+along with this program (see LICENSE file).
+If not, see [here](https://opensource.org/licenses/BSD-3-Clause).
+
+## Contacting us
+
+If you have trouble using SMAC, a concrete question or found a bug, please create an [issue](https://github.com/automl/SMAC3/issues). This is the easiest way to communicate about these things with us. 
+
+For all other inquiries, please write an email to smac[at]ai[dot]uni[dash]hannover[dot]de.
+
+## Miscellaneous
+
+SMAC3 is developed by the [AutoML Groups of the Universities of Hannover and
+Freiburg](http://www.automl.org/).
+
+If you have found a bug, please report to [issues](https://github.com/automl/SMAC3/issues). Moreover, we are 
+appreciating any kind of help. Find our guidelines for contributing to this package 
+[here](CONTRIBUTING.md).
+
+If you use SMAC in one of your research projects, please cite our 
+[JMLR paper](https://jmlr.org/papers/v23/21-0888.html):
+```
+@article{JMLR:v23:21-0888,
+  author  = {Marius Lindauer and Katharina Eggensperger and Matthias Feurer and André Biedenkapp and Difan Deng and Carolin Benjamins and Tim Ruhkopf and René Sass and Frank Hutter},
+  title   = {SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization},
+  journal = {Journal of Machine Learning Research},
+  year    = {2022},
+  volume  = {23},
+  number  = {54},
+  pages   = {1--9},
+  url     = {http://jmlr.org/papers/v23/21-0888.html}
+}
+```
+
+Copyright (C) 2016-2022  [AutoML Group](http://www.automl.org).
+
+
+
+
+%package -n python3-smac
+Summary:	SMAC3, a Python implementation of 'Sequential Model-based Algorithm Configuration'.
+Provides:	python-smac
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-smac
+# SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
+
+
+[![Tests](https://github.com/automl/SMAC3/actions/workflows/pytest.yml/badge.svg?branch=main)](https://github.com/automl/SMAC3/actions/workflows/pytest.yml)
+[![Documentation](https://github.com/automl/SMAC3/actions/workflows/docs.yml/badge.svg?branch=main)](https://github.com/automl/SMAC3/actions/workflows/docs.yml)
+[![codecov
+Status](https://codecov.io/gh/automl/SMAC3/branch/master/graph/badge.svg)](https://codecov.io/gh/automl/SMAC3)
+
+<img src="docs/images/logo.png" style="width: 50%;" />
+
+SMAC offers a robust and flexible framework for Bayesian Optimization to support users in determining well-performing 
+hyperparameter configurations for their (Machine Learning) algorithms, datasets and applications at hand. The main core 
+consists of Bayesian Optimization in combination with an aggressive racing mechanism to efficiently decide which of two configurations performs better.
+
+SMAC3 is written in Python3 and continuously tested with Python 3.8, 3.9, and 3.10. Its Random
+Forest is written in C++. In further texts, SMAC is representatively mentioned for SMAC3.
+
+> [Documentation](https://automl.github.io/SMAC3)
+
+> [Roadmap](https://github.com/orgs/automl/projects/5/views/2)
+
+
+## Important: Changes in v2.0
+
+With the next big major release of SMAC, we drastically boosted the user experience by improving the APIs and how the 
+pipelining is done (see [changelog](CHANGELOG.md)). All facades/intensifiers support multi-objective, multi-fidelity, 
+and multi-threading natively now! That includes having an ask-and-tell interface and continuing a run
+wherever you left off. pSMAC is removed because when specifying the number of workers, SMAC automatically uses 
+multi-threading for evaluating trials. When cleaning the code base, however, we removed the command-line 
+interface (calling a target function from a script is still supported), and runtime optimization. Also,
+python 3.7 is not supported anymore. If you depend on those functionalities, please keep using v1.4.
+
+We are excited to introduce the new major release and look forward to developing new features on the new code base. 
+We hope you enjoy this new user experience as much as we do. 🚀
+
+
+## Installation
+
+This instruction is for the installation on a Linux system, for Windows and Mac and further information see the [documentation](https://automl.github.io/SMAC3/main/1_installation.html).
+
+Create a new environment with python 3.10 and make sure swig is installed either on your system or
+inside the environment. We demonstrate the installation via anaconda in the following:
+
+Create and activate environment:
+```
+conda create -n SMAC python=3.10
+conda activate SMAC
+```
+
+Install swig:
+```
+conda install gxx_linux-64 gcc_linux-64 swig
+```
+
+Install SMAC via PyPI:
+```
+pip install smac
+```
+
+Or alternatively, clone the environment:
+```
+git clone https://github.com/automl/SMAC3.git && cd SMAC3
+pip install -e .[dev]
+```
+
+
+## Minimal Example
+
+```py
+from ConfigSpace import Configuration, ConfigurationSpace
+
+import numpy as np
+from smac import HyperparameterOptimizationFacade, Scenario
+from sklearn import datasets
+from sklearn.svm import SVC
+from sklearn.model_selection import cross_val_score
+
+iris = datasets.load_iris()
+
+
+def train(config: Configuration, seed: int = 0) -> float:
+    classifier = SVC(C=config["C"], random_state=seed)
+    scores = cross_val_score(classifier, iris.data, iris.target, cv=5)
+    return 1 - np.mean(scores)
+
+
+configspace = ConfigurationSpace({"C": (0.100, 1000.0)})
+
+# Scenario object specifying the optimization environment
+scenario = Scenario(configspace, deterministic=True, n_trials=200)
+
+# Use SMAC to find the best configuration/hyperparameters
+smac = HyperparameterOptimizationFacade(scenario, train)
+incumbent = smac.optimize()
+```
+
+More examples can be found in the [documentation](https://automl.github.io/SMAC3/main/examples/).
+
+## Visualization via DeepCAVE
+
+With DeepCAVE ([Repo](https://github.com/automl/DeepCAVE), [Paper](https://arxiv.org/abs/2206.03493)) you can visualize your SMAC runs. It is a visualization and analysis tool for AutoML (especially for the sub-problem
+hyperparameter optimization) runs.
+
+## License
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the 3-clause BSD license (please see the LICENSE file).
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+
+You should have received a copy of the 3-clause BSD license
+along with this program (see LICENSE file).
+If not, see [here](https://opensource.org/licenses/BSD-3-Clause).
+
+## Contacting us
+
+If you have trouble using SMAC, a concrete question or found a bug, please create an [issue](https://github.com/automl/SMAC3/issues). This is the easiest way to communicate about these things with us. 
+
+For all other inquiries, please write an email to smac[at]ai[dot]uni[dash]hannover[dot]de.
+
+## Miscellaneous
+
+SMAC3 is developed by the [AutoML Groups of the Universities of Hannover and
+Freiburg](http://www.automl.org/).
+
+If you have found a bug, please report to [issues](https://github.com/automl/SMAC3/issues). Moreover, we are 
+appreciating any kind of help. Find our guidelines for contributing to this package 
+[here](CONTRIBUTING.md).
+
+If you use SMAC in one of your research projects, please cite our 
+[JMLR paper](https://jmlr.org/papers/v23/21-0888.html):
+```
+@article{JMLR:v23:21-0888,
+  author  = {Marius Lindauer and Katharina Eggensperger and Matthias Feurer and André Biedenkapp and Difan Deng and Carolin Benjamins and Tim Ruhkopf and René Sass and Frank Hutter},
+  title   = {SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization},
+  journal = {Journal of Machine Learning Research},
+  year    = {2022},
+  volume  = {23},
+  number  = {54},
+  pages   = {1--9},
+  url     = {http://jmlr.org/papers/v23/21-0888.html}
+}
+```
+
+Copyright (C) 2016-2022  [AutoML Group](http://www.automl.org).
+
+
+
+
+%package help
+Summary:	Development documents and examples for smac
+Provides:	python3-smac-doc
+%description help
+# SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
+
+
+[![Tests](https://github.com/automl/SMAC3/actions/workflows/pytest.yml/badge.svg?branch=main)](https://github.com/automl/SMAC3/actions/workflows/pytest.yml)
+[![Documentation](https://github.com/automl/SMAC3/actions/workflows/docs.yml/badge.svg?branch=main)](https://github.com/automl/SMAC3/actions/workflows/docs.yml)
+[![codecov
+Status](https://codecov.io/gh/automl/SMAC3/branch/master/graph/badge.svg)](https://codecov.io/gh/automl/SMAC3)
+
+<img src="docs/images/logo.png" style="width: 50%;" />
+
+SMAC offers a robust and flexible framework for Bayesian Optimization to support users in determining well-performing 
+hyperparameter configurations for their (Machine Learning) algorithms, datasets and applications at hand. The main core 
+consists of Bayesian Optimization in combination with an aggressive racing mechanism to efficiently decide which of two configurations performs better.
+
+SMAC3 is written in Python3 and continuously tested with Python 3.8, 3.9, and 3.10. Its Random
+Forest is written in C++. In further texts, SMAC is representatively mentioned for SMAC3.
+
+> [Documentation](https://automl.github.io/SMAC3)
+
+> [Roadmap](https://github.com/orgs/automl/projects/5/views/2)
+
+
+## Important: Changes in v2.0
+
+With the next big major release of SMAC, we drastically boosted the user experience by improving the APIs and how the 
+pipelining is done (see [changelog](CHANGELOG.md)). All facades/intensifiers support multi-objective, multi-fidelity, 
+and multi-threading natively now! That includes having an ask-and-tell interface and continuing a run
+wherever you left off. pSMAC is removed because when specifying the number of workers, SMAC automatically uses 
+multi-threading for evaluating trials. When cleaning the code base, however, we removed the command-line 
+interface (calling a target function from a script is still supported), and runtime optimization. Also,
+python 3.7 is not supported anymore. If you depend on those functionalities, please keep using v1.4.
+
+We are excited to introduce the new major release and look forward to developing new features on the new code base. 
+We hope you enjoy this new user experience as much as we do. 🚀
+
+
+## Installation
+
+This instruction is for the installation on a Linux system, for Windows and Mac and further information see the [documentation](https://automl.github.io/SMAC3/main/1_installation.html).
+
+Create a new environment with python 3.10 and make sure swig is installed either on your system or
+inside the environment. We demonstrate the installation via anaconda in the following:
+
+Create and activate environment:
+```
+conda create -n SMAC python=3.10
+conda activate SMAC
+```
+
+Install swig:
+```
+conda install gxx_linux-64 gcc_linux-64 swig
+```
+
+Install SMAC via PyPI:
+```
+pip install smac
+```
+
+Or alternatively, clone the environment:
+```
+git clone https://github.com/automl/SMAC3.git && cd SMAC3
+pip install -e .[dev]
+```
+
+
+## Minimal Example
+
+```py
+from ConfigSpace import Configuration, ConfigurationSpace
+
+import numpy as np
+from smac import HyperparameterOptimizationFacade, Scenario
+from sklearn import datasets
+from sklearn.svm import SVC
+from sklearn.model_selection import cross_val_score
+
+iris = datasets.load_iris()
+
+
+def train(config: Configuration, seed: int = 0) -> float:
+    classifier = SVC(C=config["C"], random_state=seed)
+    scores = cross_val_score(classifier, iris.data, iris.target, cv=5)
+    return 1 - np.mean(scores)
+
+
+configspace = ConfigurationSpace({"C": (0.100, 1000.0)})
+
+# Scenario object specifying the optimization environment
+scenario = Scenario(configspace, deterministic=True, n_trials=200)
+
+# Use SMAC to find the best configuration/hyperparameters
+smac = HyperparameterOptimizationFacade(scenario, train)
+incumbent = smac.optimize()
+```
+
+More examples can be found in the [documentation](https://automl.github.io/SMAC3/main/examples/).
+
+## Visualization via DeepCAVE
+
+With DeepCAVE ([Repo](https://github.com/automl/DeepCAVE), [Paper](https://arxiv.org/abs/2206.03493)) you can visualize your SMAC runs. It is a visualization and analysis tool for AutoML (especially for the sub-problem
+hyperparameter optimization) runs.
+
+## License
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the 3-clause BSD license (please see the LICENSE file).
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+
+You should have received a copy of the 3-clause BSD license
+along with this program (see LICENSE file).
+If not, see [here](https://opensource.org/licenses/BSD-3-Clause).
+
+## Contacting us
+
+If you have trouble using SMAC, a concrete question or found a bug, please create an [issue](https://github.com/automl/SMAC3/issues). This is the easiest way to communicate about these things with us. 
+
+For all other inquiries, please write an email to smac[at]ai[dot]uni[dash]hannover[dot]de.
+
+## Miscellaneous
+
+SMAC3 is developed by the [AutoML Groups of the Universities of Hannover and
+Freiburg](http://www.automl.org/).
+
+If you have found a bug, please report to [issues](https://github.com/automl/SMAC3/issues). Moreover, we are 
+appreciating any kind of help. Find our guidelines for contributing to this package 
+[here](CONTRIBUTING.md).
+
+If you use SMAC in one of your research projects, please cite our 
+[JMLR paper](https://jmlr.org/papers/v23/21-0888.html):
+```
+@article{JMLR:v23:21-0888,
+  author  = {Marius Lindauer and Katharina Eggensperger and Matthias Feurer and André Biedenkapp and Difan Deng and Carolin Benjamins and Tim Ruhkopf and René Sass and Frank Hutter},
+  title   = {SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization},
+  journal = {Journal of Machine Learning Research},
+  year    = {2022},
+  volume  = {23},
+  number  = {54},
+  pages   = {1--9},
+  url     = {http://jmlr.org/papers/v23/21-0888.html}
+}
+```
+
+Copyright (C) 2016-2022  [AutoML Group](http://www.automl.org).
+
+
+
+
+%prep
+%autosetup -n smac-2.0.0
+
+%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-smac -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.0-1
+- Package Spec generated