python-glmnet 2.6.1
Python wrapper for glmnet
Requires Python: >=3.10

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

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Fork of `python-glmnet <https://github.com/replicahq/python-glmnet>`_ with support for more recent
Python versions.

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

requirements
~~~~~~~~~~~~

``python-glmnet`` requires Python version >= 3.9, ``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 python-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:replicahq/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)