%global _empty_manifest_terminate_build 0 Name: python-blackboxopt Version: 4.14.0 Release: 1 Summary: A common interface for blackbox optimization algorithms along with useful helpers like parallel optimization loops, analysis and visualization scripts. License: Apache-2.0 URL: https://github.com/boschresearch/blackboxopt Source0: https://mirrors.nju.edu.cn/pypi/web/packages/65/1d/db7e8756c0dbcf559a74de7e3011d67f3d6815a64d335369cbce364d5459/blackboxopt-4.14.0.tar.gz BuildArch: noarch Requires: python3-parameterspace Requires: python3-numpy Requires: python3-plotly Requires: python3-scipy Requires: python3-statsmodels Requires: python3-dask Requires: python3-distributed Requires: python3-pandas Requires: python3-botorch Requires: python3-pymoo %description # Blackbox Optimization [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE) [![CI/CD](https://github.com/boschresearch/blackboxopt/workflows/ci-cd-pipeline/badge.svg)](https://github.com/boschresearch/blackboxopt/actions?query=workflow%3Aci-cd-pipeline+branch%3Amain) Various blackbox optimization algorithms with a common interface along with useful helpers like parallel optimization loops, analysis and visualization scripts. Random search is provided as an example optimizer along with tests for the interface. New optimizers can require `blackboxopt` as a dependency, which is just the light-weight interface definition. If you want all optimizer implementations that come with this package, install `blackboxopt[all]` Alternatively, you can get individual optimizers with e.g. `blackboxopt[bohb]` This software is a research prototype. The software is not ready for production use. It has neither been developed nor tested for a specific use case. However, the license conditions of the applicable Open Source licenses allow you to adapt the software to your needs. Before using it in a safety relevant setting, make sure that the software fulfills your requirements and adjust it according to any applicable safety standards (e.g. ISO 26262). ## Documentation **Visit [boschresearch.github.io/blackboxopt](https://boschresearch.github.io/blackboxopt/)** ## Development Install poetry ``` pip install poetry ``` Install the `blackboxopt` package from source by running the following from the root directory of _this_ repository ``` poetry install ``` (Optional) Install [pre-commit](https://pre-commit.com) hooks to check code standards before committing changes: ``` poetry run pre-commit install ``` ## Test Make sure to install all extras before running tests ``` poetry install -E testing poetry run pytest tests/ ``` For HTML test coverage reports run ``` poetry run pytest tests/ --cov --cov-report html:htmlcov ``` ### Custom Optimizers When you develop an optimizer based on the interface defined as part of `blackboxopt.base`, you can use `blackboxopt.testing` to directly test whether your implementation follows the specification by adding a test like this to your test suite. ```python from blackboxopt.testing import ALL_REFERENCE_TESTS @pytest.mark.parametrize("reference_test", ALL_REFERENCE_TESTS) def test_all_reference_tests(reference_test): reference_test(CustomOptimizer, custom_optimizer_init_kwargs) ``` ## Building Documentation Make sure to install _all_ necessary dependencies: ``` poetry install --extras=all ``` The documentation can be built from the repository root as follows: ``` poetry run mkdocs build --clean --no-directory-urls ``` For serving it locally while working on the documentation run: ``` poetry run mkdocs serve ``` ## Architectural Decision Records ### Create evaluation result from specification In the context of initializing an evaluation result from a specification, facing the concern that having a constructor with a specification argument while the specification attributes end up as toplevel attributes and not summarized under a specification attribute we decided for unpacking the evaluation specification like a dictionary into the result constructor to prevent the said cognitive dissonance, accepting that the unpacking operator can feel unintuitive and that users might tend to matching the attributes explictly to the init arguments. ### Report multiple evaluations In the context of many optimizers just sequentally reporting the individual evaluations when multiple evaluations are reported at once and thus not leveraging any batch reporting benefits, facing the concern that representing that common behaviour in the optimizer base class requires the definition of an abstract report single and an abstract report multi method for which the report single does not need to be implemented if the report multi is, we decided to refactor the arising redundancy into a function `call_functions_with_evaluations_and_collect_errors`, accepting that this increases the cognitive load when reading the code. ## License `blackboxopt` is open-sourced under the Apache-2.0 license. See the [LICENSE](LICENSE) file for details. For a list of other open source components included in `blackboxopt`, see the file [3rd-party-licenses.txt](3rd-party-licenses.txt). %package -n python3-blackboxopt Summary: A common interface for blackbox optimization algorithms along with useful helpers like parallel optimization loops, analysis and visualization scripts. Provides: python-blackboxopt BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-blackboxopt # Blackbox Optimization [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE) [![CI/CD](https://github.com/boschresearch/blackboxopt/workflows/ci-cd-pipeline/badge.svg)](https://github.com/boschresearch/blackboxopt/actions?query=workflow%3Aci-cd-pipeline+branch%3Amain) Various blackbox optimization algorithms with a common interface along with useful helpers like parallel optimization loops, analysis and visualization scripts. Random search is provided as an example optimizer along with tests for the interface. New optimizers can require `blackboxopt` as a dependency, which is just the light-weight interface definition. If you want all optimizer implementations that come with this package, install `blackboxopt[all]` Alternatively, you can get individual optimizers with e.g. `blackboxopt[bohb]` This software is a research prototype. The software is not ready for production use. It has neither been developed nor tested for a specific use case. However, the license conditions of the applicable Open Source licenses allow you to adapt the software to your needs. Before using it in a safety relevant setting, make sure that the software fulfills your requirements and adjust it according to any applicable safety standards (e.g. ISO 26262). ## Documentation **Visit [boschresearch.github.io/blackboxopt](https://boschresearch.github.io/blackboxopt/)** ## Development Install poetry ``` pip install poetry ``` Install the `blackboxopt` package from source by running the following from the root directory of _this_ repository ``` poetry install ``` (Optional) Install [pre-commit](https://pre-commit.com) hooks to check code standards before committing changes: ``` poetry run pre-commit install ``` ## Test Make sure to install all extras before running tests ``` poetry install -E testing poetry run pytest tests/ ``` For HTML test coverage reports run ``` poetry run pytest tests/ --cov --cov-report html:htmlcov ``` ### Custom Optimizers When you develop an optimizer based on the interface defined as part of `blackboxopt.base`, you can use `blackboxopt.testing` to directly test whether your implementation follows the specification by adding a test like this to your test suite. ```python from blackboxopt.testing import ALL_REFERENCE_TESTS @pytest.mark.parametrize("reference_test", ALL_REFERENCE_TESTS) def test_all_reference_tests(reference_test): reference_test(CustomOptimizer, custom_optimizer_init_kwargs) ``` ## Building Documentation Make sure to install _all_ necessary dependencies: ``` poetry install --extras=all ``` The documentation can be built from the repository root as follows: ``` poetry run mkdocs build --clean --no-directory-urls ``` For serving it locally while working on the documentation run: ``` poetry run mkdocs serve ``` ## Architectural Decision Records ### Create evaluation result from specification In the context of initializing an evaluation result from a specification, facing the concern that having a constructor with a specification argument while the specification attributes end up as toplevel attributes and not summarized under a specification attribute we decided for unpacking the evaluation specification like a dictionary into the result constructor to prevent the said cognitive dissonance, accepting that the unpacking operator can feel unintuitive and that users might tend to matching the attributes explictly to the init arguments. ### Report multiple evaluations In the context of many optimizers just sequentally reporting the individual evaluations when multiple evaluations are reported at once and thus not leveraging any batch reporting benefits, facing the concern that representing that common behaviour in the optimizer base class requires the definition of an abstract report single and an abstract report multi method for which the report single does not need to be implemented if the report multi is, we decided to refactor the arising redundancy into a function `call_functions_with_evaluations_and_collect_errors`, accepting that this increases the cognitive load when reading the code. ## License `blackboxopt` is open-sourced under the Apache-2.0 license. See the [LICENSE](LICENSE) file for details. For a list of other open source components included in `blackboxopt`, see the file [3rd-party-licenses.txt](3rd-party-licenses.txt). %package help Summary: Development documents and examples for blackboxopt Provides: python3-blackboxopt-doc %description help # Blackbox Optimization [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE) [![CI/CD](https://github.com/boschresearch/blackboxopt/workflows/ci-cd-pipeline/badge.svg)](https://github.com/boschresearch/blackboxopt/actions?query=workflow%3Aci-cd-pipeline+branch%3Amain) Various blackbox optimization algorithms with a common interface along with useful helpers like parallel optimization loops, analysis and visualization scripts. Random search is provided as an example optimizer along with tests for the interface. New optimizers can require `blackboxopt` as a dependency, which is just the light-weight interface definition. If you want all optimizer implementations that come with this package, install `blackboxopt[all]` Alternatively, you can get individual optimizers with e.g. `blackboxopt[bohb]` This software is a research prototype. The software is not ready for production use. It has neither been developed nor tested for a specific use case. However, the license conditions of the applicable Open Source licenses allow you to adapt the software to your needs. Before using it in a safety relevant setting, make sure that the software fulfills your requirements and adjust it according to any applicable safety standards (e.g. ISO 26262). ## Documentation **Visit [boschresearch.github.io/blackboxopt](https://boschresearch.github.io/blackboxopt/)** ## Development Install poetry ``` pip install poetry ``` Install the `blackboxopt` package from source by running the following from the root directory of _this_ repository ``` poetry install ``` (Optional) Install [pre-commit](https://pre-commit.com) hooks to check code standards before committing changes: ``` poetry run pre-commit install ``` ## Test Make sure to install all extras before running tests ``` poetry install -E testing poetry run pytest tests/ ``` For HTML test coverage reports run ``` poetry run pytest tests/ --cov --cov-report html:htmlcov ``` ### Custom Optimizers When you develop an optimizer based on the interface defined as part of `blackboxopt.base`, you can use `blackboxopt.testing` to directly test whether your implementation follows the specification by adding a test like this to your test suite. ```python from blackboxopt.testing import ALL_REFERENCE_TESTS @pytest.mark.parametrize("reference_test", ALL_REFERENCE_TESTS) def test_all_reference_tests(reference_test): reference_test(CustomOptimizer, custom_optimizer_init_kwargs) ``` ## Building Documentation Make sure to install _all_ necessary dependencies: ``` poetry install --extras=all ``` The documentation can be built from the repository root as follows: ``` poetry run mkdocs build --clean --no-directory-urls ``` For serving it locally while working on the documentation run: ``` poetry run mkdocs serve ``` ## Architectural Decision Records ### Create evaluation result from specification In the context of initializing an evaluation result from a specification, facing the concern that having a constructor with a specification argument while the specification attributes end up as toplevel attributes and not summarized under a specification attribute we decided for unpacking the evaluation specification like a dictionary into the result constructor to prevent the said cognitive dissonance, accepting that the unpacking operator can feel unintuitive and that users might tend to matching the attributes explictly to the init arguments. ### Report multiple evaluations In the context of many optimizers just sequentally reporting the individual evaluations when multiple evaluations are reported at once and thus not leveraging any batch reporting benefits, facing the concern that representing that common behaviour in the optimizer base class requires the definition of an abstract report single and an abstract report multi method for which the report single does not need to be implemented if the report multi is, we decided to refactor the arising redundancy into a function `call_functions_with_evaluations_and_collect_errors`, accepting that this increases the cognitive load when reading the code. ## License `blackboxopt` is open-sourced under the Apache-2.0 license. See the [LICENSE](LICENSE) file for details. For a list of other open source components included in `blackboxopt`, see the file [3rd-party-licenses.txt](3rd-party-licenses.txt). %prep %autosetup -n blackboxopt-4.14.0 %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-blackboxopt -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 4.14.0-1 - Package Spec generated