_ _ _ _ _

generalized-additive-models

Generalized Additive Models (GAMs) in Python.

About

GAMs are uniquely placed on the interpretability vs. precitive power continuum. In many applications they perform almost as well as more complex models, but are extremely interpretable.

• GAMs extend linear regression by allowing non-linear relationships between features and the target.
• The model is still additive, but link functions and multivariate splines facilitate a broad class of models.
• While GAMs are likely outperformed by non-additive models (e.g. boosted trees), GAMs are extremely interpretable.

Read more about GAMs:

• Predictive Modeling Silver Bullet
• Generalized Additive Models: An Introduction with R

A GAM is a statistical model in which the target variable depends on unknown smooth functions of the features, and interest focuses on inference about these smooth functions.

An exponential family distribution is specified for the target Y (.e.g Normal, Binomial or Poisson) along with a link function g (for example the identity or log functions) relating the expected value of Y to the predictor variables.

Installation

Install using pip:

pip install generalized-additive-models

Example

from sklearn.datasets import load_diabetes
from sklearn.model_selection import cross_val_score
from generalized_additive_models import GAM, Spline, Categorical

# Load data
data = load_diabetes(as_frame=True)
df, y = data.data, data.target

# Create model
terms = Spline("bp") + Spline("bmi", constraint="increasing") + Categorical("sex")
gam = GAM(terms)

# Cross validate
scores = cross_val_score(gam, df, y, scoring="r2")
print(scores) # array([0.26, 0.4 , 0.41, 0.35, 0.42])

Go to Read the Docs to see full documentation.

Contributing and development

Contributions are very welcome. You can correct spelling mistakes, write documentation, clean up code, implement new features, etc.

Some guidelines for development:

• Code must comply with the standard. See the GitHub action pipeline for more information.
• If possible, use existing algorithms from numpy, scipy and scikit-learn.
• Write tests, especically regression tests if a bug is fixed.
• Take backward compatibility seriously. API changes require good reason.

Installation for local development:

pip install -e '.[dev,lint,doc]'

Create documentation locally:

sudo apt install pandoc
sphinx-build docs _built_docs/html -W -a -E --keep-going
sphinx-autobuild docs _built_docs/html -v -j "auto" --watch generalized_additive_models

Once the version has been incremented, the commit must be tagged and pushed in order to publish to PyPi:

git tag -a v0.1.0 -m "Version 0.1.0" b22724c
git push origin v0.1.0

Citing

TODO