Python GLMNET
This is a Python wrapper for the fortran library used in the R package glmnet. While the library includes linear, logistic, Cox, Poisson, and multiple-response Gaussian, only linear and logistic are implemented in this package.
The API follows the conventions of Scikit-Learn, so it is expected to work with tools from that ecosystem.
Installation
requirements
python-glmnet requires Python version >= 3.6, scikit-learn, numpy,
and scipy. Installation from source or via pip requires a Fortran compiler.
conda
conda install -c conda-forge glmnetpip
pip install glmnetsource
glmnet depends on numpy, scikit-learn and scipy.
A working Fortran compiler is also required to build the package.
For Mac users, brew install gcc will take care of this requirement.
git clone [email protected]:civisanalytics/python-glmnet.git
cd python-glmnet
python setup.py installUsage
General
By default, LogitNet and ElasticNet fit a series of models using
the lasso penalty (伪 = 1) and up to 100 values for 位 (determined by the
algorithm). In addition, after computing the path of 位 values,
performance metrics for each value of 位 are computed using 3-fold cross
validation. The value of 位 corresponding to the best performing model is
saved as the lambda_max_ attribute and the largest value of 位 such
that the model performance is within cut_point * standard_error of
the best scoring model is saved as the lambda_best_ attribute.
The predict and predict_proba methods accept an optional
parameter lamb which is used to select which model(s) will be used
to make predictions. If lamb is omitted, lambda_best_ is used.
Both models will accept dense or sparse arrays.
Regularized Logistic Regression
from glmnet import LogitNet
m = LogitNet()
m = m.fit(x, y)Prediction is similar to Scikit-Learn:
# predict labels
p = m.predict(x)
# or probability estimates
p = m.predict_proba(x)Regularized Linear Regression
from glmnet import ElasticNet
m = ElasticNet()
m = m.fit(x, y)Predict:
p = m.predict(x)