# python-glmnet

**Repository Path**: mirrors_lepy/python-glmnet

## Basic Information

- **Project Name**: python-glmnet
- **Description**: A python port of the glmnet package for fitting generalized linear models via penalized maximum likelihood.
- **Primary Language**: Unknown
- **License**: GPL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-25
- **Last Updated**: 2025-07-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Python GLMNET
=============

This is a Python wrapper for the fortran library used in the R package
`glmnet <http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html>`__.
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 <http://scikit-learn.org/stable/>`__, so it is expected to
work with tools from that ecosystem.

Installation
------------

conda
~~~~~

.. code:: bash

    conda install -c conda-forge glmnet


pip
~~~

.. code:: bash

    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.

.. code:: bash

    git clone git@github.com: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
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    from glmnet import LogitNet

    m = LogitNet()
    m = m.fit(x, y)

Prediction is similar to Scikit-Learn:

.. code:: python

    # predict labels
    p = m.predict(x)
    # or probability estimates
    p = m.predict_proba(x)

Regularized Linear Regression
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    from glmnet import ElasticNet

    m = ElasticNet()
    m = m.fit(x, y)

Predict:

.. code:: python

    p = m.predict(x)