Python wrapper for glmnet
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.
python-glmnet requires Python version >= 3.4, scikit-learn, numpy, and scipy. Installation from source or via pip requires a Fortran compiler.
conda install -c conda-forge glmnet
pip install glmnet
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 firstname.lastname@example.org:civisanalytics/python-glmnet.git cd python-glmnet python setup.py install
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)
p = m.predict(x)
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|glmnet-2.1.1-cp36-cp36m-macosx_10_7_x86_64.whl (681.4 kB) Copy SHA256 hash SHA256||Wheel||cp36|
|glmnet-2.1.1.tar.gz (104.7 kB) Copy SHA256 hash SHA256||Source||None|