glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models.
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.
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 git@github.com:civisanalytics/python-glmnet.git
cd python-glmnet
python setup.py install
By default, LogitNet
and ElasticNet
fit a series of models using the lasso
penalty (α = 1) and up to 100 values for λ (determined by the algorithm). In
addition, after computing the path of λ values, performance metrics for each
value of λ are computed using 3-fold cross validation. The value of λ
corresponding to the best performing model is saved as the lambda_max_
attribute and the largest value of λ 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.
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)
from glmnet import ElasticNet
m = ElasticNet()
m = m.fit(x, y)
Predict:
p = m.predict(x)