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diff --git a/python-pygam.spec b/python-pygam.spec new file mode 100644 index 0000000..b072e1c --- /dev/null +++ b/python-pygam.spec @@ -0,0 +1,321 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pygam +Version: 0.9.0 +Release: 1 +Summary: please add a summary manually as the author left a blank one +License: Apache-2.0 +URL: https://pypi.org/project/pygam/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/39/26/6e6756bc2398648bc26322d94aa02668319297884e0eea79dd9a5ecdc703/pygam-0.9.0.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-progressbar2 +Requires: python3-scipy + +%description +## Installation +```pip install pygam``` +### scikit-sparse +To speed up optimization on large models with constraints, it helps to have `scikit-sparse` installed because it contains a slightly faster, sparse version of Cholesky factorization. The import from `scikit-sparse` references `nose`, so you'll need that too. +The easiest way is to use Conda: +```conda install -c conda-forge scikit-sparse nose``` +[scikit-sparse docs](http://pythonhosted.org/scikit-sparse/overview.html#download) +## Contributing - HELP REQUESTED +Contributions are most welcome! +You can help pyGAM in many ways including: +- Working on a [known bug](https://github.com/dswah/pyGAM/labels/bug). +- Trying it out and reporting bugs or what was difficult. +- Helping improve the documentation. +- Writing new [distributions](https://github.com/dswah/pyGAM/blob/master/pygam/distributions.py), and [link functions](https://github.com/dswah/pyGAM/blob/master/pygam/links.py). +- If you need some ideas, please take a look at the [issues](https://github.com/dswah/pyGAM/issues). +To start: +- **fork the project** and cut a new branch +- Now **install** the testing **dependencies** +``` +conda install pytest numpy pandas scipy pytest-cov cython +pip install --upgrade pip +pip install -r requirements.txt +``` +It helps to add a **sym-link** of the forked project to your **python path**. To do this, you should **install [flit](http://flit.readthedocs.io/en/latest/index.html)**: +- ```pip install flit``` +- Then from main project folder (ie `.../pyGAM`) do: +```flit install -s``` +Make some changes and write a test... +- **Test** your contribution (eg from the `.../pyGAM`): +```py.test -s``` +- When you are happy with your changes, make a **pull request** into the `master` branch of the main project. +## About +Generalized Additive Models (GAMs) are smooth semi-parametric models of the form: +=\beta_0+f_1(X_1)+f_2(X_2)+\dots+f_p(X_p)) +where `X.T = [X_1, X_2, ..., X_p]` are independent variables, `y` is the dependent variable, and `g()` is the link function that relates our predictor variables to the expected value of the dependent variable. +The feature functions `f_i()` are built using **penalized B splines**, which allow us to **automatically model non-linear relationships** without having to manually try out many different transformations on each variable. +<img src=imgs/pygam_basis.png> +GAMs extend generalized linear models by allowing non-linear functions of features while maintaining additivity. Since the model is additive, it is easy to examine the effect of each `X_i` on `Y` individually while holding all other predictors constant. +The result is a very flexible model, where it is easy to incorporate prior knowledge and control overfitting. +## Citing pyGAM +Please consider citing pyGAM if it has helped you in your research or work: +Daniel Servén, & Charlie Brummitt. (2018, March 27). pyGAM: Generalized Additive Models in Python. Zenodo. [DOI: 10.5281/zenodo.1208723](http://doi.org/10.5281/zenodo.1208723) +BibTex: +``` +@misc{daniel\_serven\_2018_1208723, + author = {Daniel Servén and + Charlie Brummitt}, + title = {pyGAM: Generalized Additive Models in Python}, + month = mar, + year = 2018, + doi = {10.5281/zenodo.1208723}, + url = {https://doi.org/10.5281/zenodo.1208723} +} +``` +## References +1. Simon N. Wood, 2006 +Generalized Additive Models: an introduction with R +0. Hastie, Tibshirani, Friedman +The Elements of Statistical Learning +http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf +0. James, Witten, Hastie and Tibshirani +An Introduction to Statistical Learning +http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf +0. Paul Eilers & Brian Marx, 1996 +Flexible Smoothing with B-splines and Penalties +http://www.stat.washington.edu/courses/stat527/s13/readings/EilersMarx_StatSci_1996.pdf +0. Kim Larsen, 2015 +GAM: The Predictive Modeling Silver Bullet +http://multithreaded.stitchfix.com/assets/files/gam.pdf +0. Deva Ramanan, 2008 +UCI Machine Learning: Notes on IRLS +http://www.ics.uci.edu/~dramanan/teaching/ics273a_winter08/homework/irls_notes.pdf +0. Paul Eilers & Brian Marx, 2015 +International Biometric Society: A Crash Course on P-splines +http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf +0. Keiding, Niels, 1991 +Age-specific incidence and prevalence: a statistical perspective +<!---http://www.cs.princeton.edu/courses/archive/fall11/cos323/notes/cos323_f11_lecture09_svd.pdf---> +<!---http://www.stats.uwo.ca/faculty/braun/ss3859/notes/Chapter4/ch4.pdf---> +<!---http://www.stat.berkeley.edu/~census/mlesan.pdf---> +<!---http://web.mit.edu/hyperbook/Patrikalakis-Maekawa-Cho/node17.html---> <!--- this helped me get spline gradients---> +<!---https://scikit-sparse.readthedocs.io/en/latest/overview.html#developers---> +<!---https://vincentarelbundock.github.io/Rdatasets/datasets.html---> <!--- R Datasets!---> + +%package -n python3-pygam +Summary: please add a summary manually as the author left a blank one +Provides: python-pygam +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pygam +## Installation +```pip install pygam``` +### scikit-sparse +To speed up optimization on large models with constraints, it helps to have `scikit-sparse` installed because it contains a slightly faster, sparse version of Cholesky factorization. The import from `scikit-sparse` references `nose`, so you'll need that too. +The easiest way is to use Conda: +```conda install -c conda-forge scikit-sparse nose``` +[scikit-sparse docs](http://pythonhosted.org/scikit-sparse/overview.html#download) +## Contributing - HELP REQUESTED +Contributions are most welcome! +You can help pyGAM in many ways including: +- Working on a [known bug](https://github.com/dswah/pyGAM/labels/bug). +- Trying it out and reporting bugs or what was difficult. +- Helping improve the documentation. +- Writing new [distributions](https://github.com/dswah/pyGAM/blob/master/pygam/distributions.py), and [link functions](https://github.com/dswah/pyGAM/blob/master/pygam/links.py). +- If you need some ideas, please take a look at the [issues](https://github.com/dswah/pyGAM/issues). +To start: +- **fork the project** and cut a new branch +- Now **install** the testing **dependencies** +``` +conda install pytest numpy pandas scipy pytest-cov cython +pip install --upgrade pip +pip install -r requirements.txt +``` +It helps to add a **sym-link** of the forked project to your **python path**. To do this, you should **install [flit](http://flit.readthedocs.io/en/latest/index.html)**: +- ```pip install flit``` +- Then from main project folder (ie `.../pyGAM`) do: +```flit install -s``` +Make some changes and write a test... +- **Test** your contribution (eg from the `.../pyGAM`): +```py.test -s``` +- When you are happy with your changes, make a **pull request** into the `master` branch of the main project. +## About +Generalized Additive Models (GAMs) are smooth semi-parametric models of the form: +=\beta_0+f_1(X_1)+f_2(X_2)+\dots+f_p(X_p)) +where `X.T = [X_1, X_2, ..., X_p]` are independent variables, `y` is the dependent variable, and `g()` is the link function that relates our predictor variables to the expected value of the dependent variable. +The feature functions `f_i()` are built using **penalized B splines**, which allow us to **automatically model non-linear relationships** without having to manually try out many different transformations on each variable. +<img src=imgs/pygam_basis.png> +GAMs extend generalized linear models by allowing non-linear functions of features while maintaining additivity. Since the model is additive, it is easy to examine the effect of each `X_i` on `Y` individually while holding all other predictors constant. +The result is a very flexible model, where it is easy to incorporate prior knowledge and control overfitting. +## Citing pyGAM +Please consider citing pyGAM if it has helped you in your research or work: +Daniel Servén, & Charlie Brummitt. (2018, March 27). pyGAM: Generalized Additive Models in Python. Zenodo. [DOI: 10.5281/zenodo.1208723](http://doi.org/10.5281/zenodo.1208723) +BibTex: +``` +@misc{daniel\_serven\_2018_1208723, + author = {Daniel Servén and + Charlie Brummitt}, + title = {pyGAM: Generalized Additive Models in Python}, + month = mar, + year = 2018, + doi = {10.5281/zenodo.1208723}, + url = {https://doi.org/10.5281/zenodo.1208723} +} +``` +## References +1. Simon N. Wood, 2006 +Generalized Additive Models: an introduction with R +0. Hastie, Tibshirani, Friedman +The Elements of Statistical Learning +http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf +0. James, Witten, Hastie and Tibshirani +An Introduction to Statistical Learning +http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf +0. Paul Eilers & Brian Marx, 1996 +Flexible Smoothing with B-splines and Penalties +http://www.stat.washington.edu/courses/stat527/s13/readings/EilersMarx_StatSci_1996.pdf +0. Kim Larsen, 2015 +GAM: The Predictive Modeling Silver Bullet +http://multithreaded.stitchfix.com/assets/files/gam.pdf +0. Deva Ramanan, 2008 +UCI Machine Learning: Notes on IRLS +http://www.ics.uci.edu/~dramanan/teaching/ics273a_winter08/homework/irls_notes.pdf +0. Paul Eilers & Brian Marx, 2015 +International Biometric Society: A Crash Course on P-splines +http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf +0. Keiding, Niels, 1991 +Age-specific incidence and prevalence: a statistical perspective +<!---http://www.cs.princeton.edu/courses/archive/fall11/cos323/notes/cos323_f11_lecture09_svd.pdf---> +<!---http://www.stats.uwo.ca/faculty/braun/ss3859/notes/Chapter4/ch4.pdf---> +<!---http://www.stat.berkeley.edu/~census/mlesan.pdf---> +<!---http://web.mit.edu/hyperbook/Patrikalakis-Maekawa-Cho/node17.html---> <!--- this helped me get spline gradients---> +<!---https://scikit-sparse.readthedocs.io/en/latest/overview.html#developers---> +<!---https://vincentarelbundock.github.io/Rdatasets/datasets.html---> <!--- R Datasets!---> + +%package help +Summary: Development documents and examples for pygam +Provides: python3-pygam-doc +%description help +## Installation +```pip install pygam``` +### scikit-sparse +To speed up optimization on large models with constraints, it helps to have `scikit-sparse` installed because it contains a slightly faster, sparse version of Cholesky factorization. The import from `scikit-sparse` references `nose`, so you'll need that too. +The easiest way is to use Conda: +```conda install -c conda-forge scikit-sparse nose``` +[scikit-sparse docs](http://pythonhosted.org/scikit-sparse/overview.html#download) +## Contributing - HELP REQUESTED +Contributions are most welcome! +You can help pyGAM in many ways including: +- Working on a [known bug](https://github.com/dswah/pyGAM/labels/bug). +- Trying it out and reporting bugs or what was difficult. +- Helping improve the documentation. +- Writing new [distributions](https://github.com/dswah/pyGAM/blob/master/pygam/distributions.py), and [link functions](https://github.com/dswah/pyGAM/blob/master/pygam/links.py). +- If you need some ideas, please take a look at the [issues](https://github.com/dswah/pyGAM/issues). +To start: +- **fork the project** and cut a new branch +- Now **install** the testing **dependencies** +``` +conda install pytest numpy pandas scipy pytest-cov cython +pip install --upgrade pip +pip install -r requirements.txt +``` +It helps to add a **sym-link** of the forked project to your **python path**. To do this, you should **install [flit](http://flit.readthedocs.io/en/latest/index.html)**: +- ```pip install flit``` +- Then from main project folder (ie `.../pyGAM`) do: +```flit install -s``` +Make some changes and write a test... +- **Test** your contribution (eg from the `.../pyGAM`): +```py.test -s``` +- When you are happy with your changes, make a **pull request** into the `master` branch of the main project. +## About +Generalized Additive Models (GAMs) are smooth semi-parametric models of the form: +=\beta_0+f_1(X_1)+f_2(X_2)+\dots+f_p(X_p)) +where `X.T = [X_1, X_2, ..., X_p]` are independent variables, `y` is the dependent variable, and `g()` is the link function that relates our predictor variables to the expected value of the dependent variable. +The feature functions `f_i()` are built using **penalized B splines**, which allow us to **automatically model non-linear relationships** without having to manually try out many different transformations on each variable. +<img src=imgs/pygam_basis.png> +GAMs extend generalized linear models by allowing non-linear functions of features while maintaining additivity. Since the model is additive, it is easy to examine the effect of each `X_i` on `Y` individually while holding all other predictors constant. +The result is a very flexible model, where it is easy to incorporate prior knowledge and control overfitting. +## Citing pyGAM +Please consider citing pyGAM if it has helped you in your research or work: +Daniel Servén, & Charlie Brummitt. (2018, March 27). pyGAM: Generalized Additive Models in Python. Zenodo. [DOI: 10.5281/zenodo.1208723](http://doi.org/10.5281/zenodo.1208723) +BibTex: +``` +@misc{daniel\_serven\_2018_1208723, + author = {Daniel Servén and + Charlie Brummitt}, + title = {pyGAM: Generalized Additive Models in Python}, + month = mar, + year = 2018, + doi = {10.5281/zenodo.1208723}, + url = {https://doi.org/10.5281/zenodo.1208723} +} +``` +## References +1. Simon N. Wood, 2006 +Generalized Additive Models: an introduction with R +0. Hastie, Tibshirani, Friedman +The Elements of Statistical Learning +http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf +0. James, Witten, Hastie and Tibshirani +An Introduction to Statistical Learning +http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf +0. Paul Eilers & Brian Marx, 1996 +Flexible Smoothing with B-splines and Penalties +http://www.stat.washington.edu/courses/stat527/s13/readings/EilersMarx_StatSci_1996.pdf +0. Kim Larsen, 2015 +GAM: The Predictive Modeling Silver Bullet +http://multithreaded.stitchfix.com/assets/files/gam.pdf +0. Deva Ramanan, 2008 +UCI Machine Learning: Notes on IRLS +http://www.ics.uci.edu/~dramanan/teaching/ics273a_winter08/homework/irls_notes.pdf +0. Paul Eilers & Brian Marx, 2015 +International Biometric Society: A Crash Course on P-splines +http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf +0. Keiding, Niels, 1991 +Age-specific incidence and prevalence: a statistical perspective +<!---http://www.cs.princeton.edu/courses/archive/fall11/cos323/notes/cos323_f11_lecture09_svd.pdf---> +<!---http://www.stats.uwo.ca/faculty/braun/ss3859/notes/Chapter4/ch4.pdf---> +<!---http://www.stat.berkeley.edu/~census/mlesan.pdf---> +<!---http://web.mit.edu/hyperbook/Patrikalakis-Maekawa-Cho/node17.html---> <!--- this helped me get spline gradients---> +<!---https://scikit-sparse.readthedocs.io/en/latest/overview.html#developers---> +<!---https://vincentarelbundock.github.io/Rdatasets/datasets.html---> <!--- R Datasets!---> + +%prep +%autosetup -n pygam-0.9.0 + +%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-pygam -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.0-1 +- Package Spec generated |