| Name | pygam JSON |
| Version |
0.9.1
JSON |
| download |
| home_page | |
| Summary | |
| upload_time | 2024-02-13 11:26:09 |
| maintainer | |
| docs_url | None |
| author | Daniel Servén Marín |
| requires_python | >=3.8.1,<3.13 |
| license | Apache-2.0 |
| keywords |
|
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No requirements were recorded.
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[](https://pygam.readthedocs.io/en/latest/?badge=latest)
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[](https://doi.org/10.5281/zenodo.1208723)
# pyGAM
Generalized Additive Models in Python.
<img src=imgs/pygam_tensor.png>
## Documentation
- [Official pyGAM Documentation: Read the Docs](https://pygam.readthedocs.io/en/latest/?badge=latest)
- [Building interpretable models with Generalized additive models in Python](https://medium.com/just-another-data-scientist/building-interpretable-models-with-generalized-additive-models-in-python-c4404eaf5515)
<!-----
[pyGAM: Getting started with Generalized Additive Models in Python](https://medium.com/@jpoberhauser/pygam-getting-started-with-generalized-additive-models-in-python-457df5b4705f)
----->
## 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 cython
pip install --upgrade pip
pip install poetry
poetry install --with dev
```
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!--->
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The import from `scikit-sparse` references `nose`, so you'll need that too.\n\nThe easiest way is to use Conda: \n```conda install -c conda-forge scikit-sparse nose```\n\n[scikit-sparse docs](http://pythonhosted.org/scikit-sparse/overview.html#download)\n\n## Contributing - HELP REQUESTED\nContributions are most welcome!\n\nYou can help pyGAM in many ways including:\n\n- Working on a [known bug](https://github.com/dswah/pyGAM/labels/bug).\n- Trying it out and reporting bugs or what was difficult.\n- Helping improve the documentation.\n- 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).\n- If you need some ideas, please take a look at the [issues](https://github.com/dswah/pyGAM/issues).\n\n\nTo start:\n- **fork the project** and cut a new branch\n- Now **install** the testing **dependencies**\n\n```\nconda install cython\npip install --upgrade pip\npip install poetry\npoetry install --with dev\n```\n\nMake some changes and write a test...\n- **Test** your contribution (eg from the `.../pyGAM`):\n```py.test -s```\n- When you are happy with your changes, make a **pull request** into the `master` branch of the main project.\n\n\n## About\nGeneralized Additive Models (GAMs) are smooth semi-parametric models of the form:\n\n=\\beta_0+f_1(X_1)+f_2(X_2)+\\dots+f_p(X_p))\n\nwhere `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.\n\nThe 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.\n\n<img src=imgs/pygam_basis.png>\n\nGAMs 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.\n\nThe result is a very flexible model, where it is easy to incorporate prior knowledge and control overfitting.\n\n## Citing pyGAM\nPlease consider citing pyGAM if it has helped you in your research or work:\n\nDaniel Serv\u00e9n, & Charlie Brummitt. (2018, March 27). pyGAM: Generalized Additive Models in Python. Zenodo. [DOI: 10.5281/zenodo.1208723](http://doi.org/10.5281/zenodo.1208723)\n\nBibTex:\n```\n@misc{daniel\\_serven\\_2018_1208723,\n author = {Daniel Serv\u00e9n and\n Charlie Brummitt},\n title = {pyGAM: Generalized Additive Models in Python},\n month = mar,\n year = 2018,\n doi = {10.5281/zenodo.1208723},\n url = {https://doi.org/10.5281/zenodo.1208723}\n}\n```\n\n## References\n1. Simon N. Wood, 2006 \nGeneralized Additive Models: an introduction with R\n\n0. 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