glmnet 2.2.1

Description:

glmnet 2.2.1

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 glmnet


pip
pip install glmnet


source
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 install



Usage

General
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.


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)