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Generalized Additive Models in Python.
<img src=imgs/pygam_tensor.png>
pip install pygam
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
Contributions are most welcome!
You can help pyGAM in many ways including:
To start:
conda install cython
pip install --upgrade pip
pip install poetry
poetry install --with dev
Make some changes and write a test...
.../pyGAM
):
py.test -s
master
branch of the main project.Generalized Additive Models (GAMs) are smooth semi-parametric models of the form:
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.
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
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}
}
Simon N. Wood, 2006
Generalized Additive Models: an introduction with R
Hastie, Tibshirani, Friedman
The Elements of Statistical Learning
http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf
James, Witten, Hastie and Tibshirani
An Introduction to Statistical Learning
http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf
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
Kim Larsen, 2015
GAM: The Predictive Modeling Silver Bullet
http://multithreaded.stitchfix.com/assets/files/gam.pdf
Deva Ramanan, 2008
UCI Machine Learning: Notes on IRLS
http://www.ics.uci.edu/~dramanan/teaching/ics273a_winter08/homework/irls_notes.pdf
Paul Eilers & Brian Marx, 2015
International Biometric Society: A Crash Course on P-splines
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
Keiding, Niels, 1991
Age-specific incidence and prevalence: a statistical perspective
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We found that pygam demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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