civisanalytics/python-glmnet
Fork: 103 Star: 255 (更新于 1970-01-01 00:00:00)
license: NOASSERTION
Language: Python .
A python port of the glmnet package for fitting generalized linear models via penalized maximum likelihood.
最后发布版本: v2.2.1 ( 2020-06-30 21:27:43)
Python GLMNET
|CircleCI| |Conda| |PyPI| |PyVersions|
.. |CircleCI| image:: https://circleci.com/gh/civisanalytics/python-glmnet.svg?style=svg :target: https://circleci.com/gh/civisanalytics/python-glmnet :alt: Build status
.. |Conda| image:: https://anaconda.org/conda-forge/glmnet/badges/version.svg :target: https://anaconda.org/conda-forge/glmnet :alt: Latest version on conda forge
.. |PyPI| image:: https://img.shields.io/pypi/v/glmnet.svg :target: https://pypi.org/project/glmnet/ :alt: Latest version on PyPI
.. |PyVersions| image:: https://img.shields.io/pypi/pyversions/glmnet.svg :target: https://pypi.org/project/glmnet/ :alt: Supported python versions for 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
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)
最近版本更新:(数据更新于 1970-01-01 00:00:00)
2020-06-30 21:27:43 v2.2.1
2020-06-29 23:14:50 v2.2.0
2019-03-12 05:53:51 v2.1.1
2019-03-12 02:22:59 v2.1.0
主题(topics):
elasticnet, glm, glmnet, lasso, python, r
civisanalytics/python-glmnet同语言 Python最近更新仓库
2024-12-22 18:18:34 LeslieLeung/heimdallr
2024-12-22 09:03:32 ultralytics/ultralytics
2024-12-21 13:26:40 notepad-plus-plus/nppPluginList
2024-12-21 11:42:53 XiaoMi/ha_xiaomi_home
2024-12-21 04:33:22 comfyanonymous/ComfyUI
2024-12-20 18:47:56 home-assistant/core