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|
%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
|
