%global _empty_manifest_terminate_build 0 Name: python-dame-flame Version: 0.51 Release: 1 Summary: Causal Inference Covariate Matching License: MIT URL: https://github.com/almost-matching-exactly/DAME-FLAME-Python-Package Source0: https://mirrors.nju.edu.cn/pypi/web/packages/03/fb/a83870884fb83be6f2dfa3b28b6b5be475f913e01b91d4fc247853c64799/dame_flame-0.51.tar.gz BuildArch: noarch Requires: python3-scikit-learn Requires: python3-scipy Requires: python3-pandas Requires: python3-numpy %description ## Documentation [here](https://almost-matching-exactly.github.io/DAME-FLAME-Python-Package/) DAME-FLAME is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. It implements the Dynamic Almost Matching Exactly (DAME) and Fast, Large-Scale Almost Matching Exactly (FLAME) algorithms, which match treatment and control units on subsets of the covariates. The resulting matched groups are interpretable, because the matches are made on covariates, and high-quality, because machine learning is used to determine which covariates are important to match on. ### Installation #### Dependencies `dame-flame` requires Python version (>=3.6.5). Install from [here](https://www.python.org/downloads/) if needed. - pandas>=0.11.0 - numpy>= 1.16.5 - scikit-learn>=0.23.2 If your python version does not have these packages, install from [here](https://packaging.python.org/tutorials/installing-packages/). To run the examples in the examples folder (these are not part of the package), Jupyter Notebooks or Jupyter Lab (available [here](https://jupyter.org/install)) and Matplotlib (>=2.0.0) is also required. #### User Installation Download from PyPi via $ pip install dame-flame %package -n python3-dame-flame Summary: Causal Inference Covariate Matching Provides: python-dame-flame BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-dame-flame ## Documentation [here](https://almost-matching-exactly.github.io/DAME-FLAME-Python-Package/) DAME-FLAME is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. It implements the Dynamic Almost Matching Exactly (DAME) and Fast, Large-Scale Almost Matching Exactly (FLAME) algorithms, which match treatment and control units on subsets of the covariates. The resulting matched groups are interpretable, because the matches are made on covariates, and high-quality, because machine learning is used to determine which covariates are important to match on. ### Installation #### Dependencies `dame-flame` requires Python version (>=3.6.5). Install from [here](https://www.python.org/downloads/) if needed. - pandas>=0.11.0 - numpy>= 1.16.5 - scikit-learn>=0.23.2 If your python version does not have these packages, install from [here](https://packaging.python.org/tutorials/installing-packages/). To run the examples in the examples folder (these are not part of the package), Jupyter Notebooks or Jupyter Lab (available [here](https://jupyter.org/install)) and Matplotlib (>=2.0.0) is also required. #### User Installation Download from PyPi via $ pip install dame-flame %package help Summary: Development documents and examples for dame-flame Provides: python3-dame-flame-doc %description help ## Documentation [here](https://almost-matching-exactly.github.io/DAME-FLAME-Python-Package/) DAME-FLAME is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. It implements the Dynamic Almost Matching Exactly (DAME) and Fast, Large-Scale Almost Matching Exactly (FLAME) algorithms, which match treatment and control units on subsets of the covariates. The resulting matched groups are interpretable, because the matches are made on covariates, and high-quality, because machine learning is used to determine which covariates are important to match on. ### Installation #### Dependencies `dame-flame` requires Python version (>=3.6.5). Install from [here](https://www.python.org/downloads/) if needed. - pandas>=0.11.0 - numpy>= 1.16.5 - scikit-learn>=0.23.2 If your python version does not have these packages, install from [here](https://packaging.python.org/tutorials/installing-packages/). To run the examples in the examples folder (these are not part of the package), Jupyter Notebooks or Jupyter Lab (available [here](https://jupyter.org/install)) and Matplotlib (>=2.0.0) is also required. #### User Installation Download from PyPi via $ pip install dame-flame %prep %autosetup -n dame-flame-0.51 %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-dame-flame -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed Apr 12 2023 Python_Bot - 0.51-1 - Package Spec generated