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https://github.com/dswah/pyGAM
[HELP REQUESTED] Generalized Additive Models in Python
https://github.com/dswah/pyGAM
data-science gams interpretable-machine-learning machine-learning python scientific-computing
Last synced: 9 days ago
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[HELP REQUESTED] Generalized Additive Models in Python
- Host: GitHub
- URL: https://github.com/dswah/pyGAM
- Owner: dswah
- License: apache-2.0
- Created: 2017-01-19T03:54:22.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2024-05-06T21:09:08.000Z (6 months ago)
- Last Synced: 2024-05-17T07:02:28.675Z (6 months ago)
- Topics: data-science, gams, interpretable-machine-learning, machine-learning, python, scientific-computing
- Language: Python
- Homepage: https://pygam.readthedocs.io
- Size: 15.6 MB
- Stars: 839
- Watchers: 29
- Forks: 154
- Open Issues: 116
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-scientific-computing - GitHub
- awesome-datascience - pyGAM
README
[](https://pygam.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/pygam)
[](https://codecov.io/gh/dswah/pygam)
[](https://badge.fury.io/py/pygam)
[](https://badge.fury.io/py/pygam)
[](https://badge.fury.io/py/pygam)
[](https://doi.org/10.5281/zenodo.1208723)
# pyGAM
Generalized Additive Models in Python.
## 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)
## 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.
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