Generalized linear models are well-established tools for regression and classification and are widely applied across the sciences, economics, business, and finance. They are uniquely identifiable due to their convex loss and easy to interpret due to their point-wise non-linearities and well-defined noise models.
In the era of exploratory data analyses with a large number of predictor variables, it is important to regularize. Regularization prevents overfitting by penalizing the negative log likelihood and can be used to articulate prior knowledge about the parameters in a structured form.
Despite the attractiveness of regularized GLMs, the available tools in the Python data science eco-system are highly fragmented. More specifically,
- statsmodels provides a wide range of link functions but no regularization.
- scikit-learn provides elastic net regularization but only for linear models.
- lightning provides elastic net and group lasso regularization, but only for linear and logistic regression.
Pyglmnet is a response to this fragmentation. Here are some highlights.
-
Pyglmnet provides a wide range of noise models (and paired canonical link functions):
'gaussian'
,'binomial'
,'multinomial'
, 'poisson
', and'softplus'
. -
It supports a wide range of regularizers: ridge, lasso, elastic net, group lasso, and Tikhonov regularization.
-
Pyglmnet's API is designed to be compatible with scikit-learn, so you can deploy
Pipeline
tools such asGridSearchCV()
andcross_val_score()
. -
We follow the same approach and notations as in Friedman, J., Hastie, T., & Tibshirani, R. (2010) and the accompanying widely popular R package.
-
We have implemented a cyclical coordinate descent optimizer with Newton update, active sets, update caching, and warm restarts. This optimization approach is identical to the one used in R package.
-
A number of Python wrappers exist for the R glmnet package (e.g. here and here) but in contrast to these, Pyglmnet is a pure python implementation. Therefore, it is easy to modify and introduce additional noise models and regularizers in the future.
Here is table comparing pyglmnet
against
scikit-learn
's linear_model
, statsmodels
, and R
.
The numbers below are run time (in milliseconds) to fit a
distr | pyglmnet | scikit-learn | statsmodels | R |
---|---|---|---|---|
gaussian | 6.8 | 1.2 | 29.8 | 10.3 |
binomial | 16.3 | 4.5 | 89.3 | -- |
poisson | 5.8 | -- | 117.2 | 156.1 |
We provide a function called BenchMarkGLM()
in pyglmnet.datasets
if you would like to run these benchmarks yourself, but you need to take
care of the dependencies: scikit-learn
, Rpy2
, and statsmodels
yourself.
Now pip
installable!
$ pip install pyglmnet
Manual installation instructions below:
Clone the repository.
$ git clone http://github.com/glm-tools/pyglmnet
Install pyglmnet
using setup.py
as follows
$ python setup.py develop install
Here is an example on how to use the GLM
estimator.
import numpy as np
import scipy.sparse as sps
from sklearn