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diff --git a/python-causalimpact.spec b/python-causalimpact.spec new file mode 100644 index 0000000..76f1c47 --- /dev/null +++ b/python-causalimpact.spec @@ -0,0 +1,196 @@ +%global _empty_manifest_terminate_build 0 +Name: python-causalimpact +Version: 0.2.6 +Release: 1 +Summary: Python Package for causal inference using Bayesian structural time-series models +License: MIT +URL: https://github.com/jamalsenouci/causalimpact/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f3/d7/891fcea5579f47477b320c1c8e9f8625d0daa659ad7f09654dfee7eef3f4/causalimpact-0.2.6.tar.gz +BuildArch: noarch + +Requires: python3-pandas +Requires: python3-numpy +Requires: python3-statsmodels +Requires: python3-matplotlib +Requires: python3-pymc +Requires: python3-pytensor +Requires: python3-importlib-metadata +Requires: python3-setuptools +Requires: python3-pytest +Requires: python3-pytest-cov + +%description +## CausalImpact + +[](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml) +[](https://codecov.io/gh/jamalsenouci/causalimpact) + +[](https://deepsource.io/gh/jamalsenouci/causalimpact/?ref=repository-badge) + +#### A Python package for causal inference using Bayesian structural time-series models + +This is a port of the R package CausalImpact, see: https://github.com/google/CausalImpact. + +This package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred. + +As with all approaches to causal inference on non-experimental data, valid conclusions require strong assumptions. The CausalImpact package, in particular, assumes that the outcome time series can be explained in terms of a set of control time series that were themselves not affected by the intervention. Furthermore, the relation between treated series and control series is assumed to be stable during the post-intervention period. Understanding and checking these assumptions for any given application is critical for obtaining valid conclusions. + +#### Try it out in the browser + +[](https://mybinder.org/v2/gh/jamalsenouci/causalimpact/HEAD?labpath=GettingStarted.ipynb) + +#### Installation + +install the latest release via pip + +```bash +pip install causalimpact +``` + +#### Getting started + +[Documentation and examples](https://nbviewer.org/github/jamalsenouci/causalimpact/blob/master/GettingStarted.ipynb) + +#### Further resources + +- Manuscript: [Brodersen et al., Annals of Applied Statistics (2015)](http://research.google.com/pubs/pub41854.html) + +#### Bugs + +The issue tracker is at https://github.com/jamalsenouci/causalimpact/issues. Please report any bugs that you find. Or, even better, fork the repository on GitHub and create a pull request. + + +%package -n python3-causalimpact +Summary: Python Package for causal inference using Bayesian structural time-series models +Provides: python-causalimpact +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-causalimpact +## CausalImpact + +[](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml) +[](https://codecov.io/gh/jamalsenouci/causalimpact) + +[](https://deepsource.io/gh/jamalsenouci/causalimpact/?ref=repository-badge) + +#### A Python package for causal inference using Bayesian structural time-series models + +This is a port of the R package CausalImpact, see: https://github.com/google/CausalImpact. + +This package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred. + +As with all approaches to causal inference on non-experimental data, valid conclusions require strong assumptions. The CausalImpact package, in particular, assumes that the outcome time series can be explained in terms of a set of control time series that were themselves not affected by the intervention. Furthermore, the relation between treated series and control series is assumed to be stable during the post-intervention period. Understanding and checking these assumptions for any given application is critical for obtaining valid conclusions. + +#### Try it out in the browser + +[](https://mybinder.org/v2/gh/jamalsenouci/causalimpact/HEAD?labpath=GettingStarted.ipynb) + +#### Installation + +install the latest release via pip + +```bash +pip install causalimpact +``` + +#### Getting started + +[Documentation and examples](https://nbviewer.org/github/jamalsenouci/causalimpact/blob/master/GettingStarted.ipynb) + +#### Further resources + +- Manuscript: [Brodersen et al., Annals of Applied Statistics (2015)](http://research.google.com/pubs/pub41854.html) + +#### Bugs + +The issue tracker is at https://github.com/jamalsenouci/causalimpact/issues. Please report any bugs that you find. Or, even better, fork the repository on GitHub and create a pull request. + + +%package help +Summary: Development documents and examples for causalimpact +Provides: python3-causalimpact-doc +%description help +## CausalImpact + +[](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml) +[](https://codecov.io/gh/jamalsenouci/causalimpact) + +[](https://deepsource.io/gh/jamalsenouci/causalimpact/?ref=repository-badge) + +#### A Python package for causal inference using Bayesian structural time-series models + +This is a port of the R package CausalImpact, see: https://github.com/google/CausalImpact. + +This package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred. + +As with all approaches to causal inference on non-experimental data, valid conclusions require strong assumptions. The CausalImpact package, in particular, assumes that the outcome time series can be explained in terms of a set of control time series that were themselves not affected by the intervention. Furthermore, the relation between treated series and control series is assumed to be stable during the post-intervention period. Understanding and checking these assumptions for any given application is critical for obtaining valid conclusions. + +#### Try it out in the browser + +[](https://mybinder.org/v2/gh/jamalsenouci/causalimpact/HEAD?labpath=GettingStarted.ipynb) + +#### Installation + +install the latest release via pip + +```bash +pip install causalimpact +``` + +#### Getting started + +[Documentation and examples](https://nbviewer.org/github/jamalsenouci/causalimpact/blob/master/GettingStarted.ipynb) + +#### Further resources + +- Manuscript: [Brodersen et al., Annals of Applied Statistics (2015)](http://research.google.com/pubs/pub41854.html) + +#### Bugs + +The issue tracker is at https://github.com/jamalsenouci/causalimpact/issues. Please report any bugs that you find. Or, even better, fork the repository on GitHub and create a pull request. + + +%prep +%autosetup -n causalimpact-0.2.6 + +%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-causalimpact -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.6-1 +- Package Spec generated |