https://github.com/civisanalytics/python-glmnet

A python port of the glmnet package for fitting generalized linear models via penalized maximum likelihood.
https://github.com/civisanalytics/python-glmnet

elasticnet glm glmnet lasso python r

Last synced: 7 months ago
JSON representation

A python port of the glmnet package for fitting generalized linear models via penalized maximum likelihood.

• Host: GitHub
• URL: https://github.com/civisanalytics/python-glmnet
• Owner: civisanalytics
• License: other
• Archived: true
• Created: 2016-06-03T17:03:07.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2024-07-24T15:39:38.000Z (over 1 year ago)
• Last Synced: 2025-04-13T00:48:47.260Z (7 months ago)
• Topics: elasticnet, glm, glmnet, lasso, python, r
• Language: Python
• Homepage:
• Size: 188 KB
• Stars: 265
• Watchers: 74
• Forks: 58
• Open Issues: 17
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

• awesome-python-data-science - python-glmnet - glmnet package for fitting generalized linear models. (Machine Learning Frameworks)

README

          
Python GLMNET

=============

Maintenance Warning

-------------------

This package is not longer maintained.

Description

-----------

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

~~~~~

.. 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)