All notable changes to this project will be documented in this file. This project adheres to Semantic Versioning.
- #65
Remove
six
dependency entirely.
- #57 Mark the Fortran code as linguist-vendored so that GitHub classifies this project as Python (#37).
- #62 Update the cross-validation for users to be able to define groups of observations, which is equivalent with foldid of cvglmnet in R.
- #64
- Python version support: Add v3.8, and drop v3.4 + v3.5.
- Maintenance: Drop versioneer; update and pin dependencies for development.
- #55 Include all Fortran source code in source tarball; exclude autogenerated C.
- #29 Provide understandable error messages for more glmnet solver errors.
- #31
Expose
max_features
parameter inElasticNet
andLogitNet
. - #34
Use sample weights in
LogitNet
. - #41
Add
lower_limits
andupper_limits
parameters toElasticNet
andLogitNet
, allowing users to restrict the range of fitted coefficients.
- #44 Change CircleCI configuration file from v1 to v2, switch to pytest, and test in Python versions 3.4 - 3.7.
- #36 Convert README to .rst format for better display on PyPI (#35).
- #54
Use
setuptools
insetup.py
and update author in metadata.
- #24 Use shuffled splits (controlled by input seed) for cross validation (#23).
- #47
Remove inappropriate
__init__.py
from the root path (#46). - #51 Satisfy scikit-learn estimator checks. Includes: Allow one-sample predictions; allow list inputs for sample weights; Ensure scikit-learn Estimator compatibility.
- #53
Return correct dimensions for 1-row predictions, with or without lambda
path, in both
LogitNet
andElasticNet
(#52, #30, #25).
- #10 the parameter
n_folds
in the constructors ofLogitNet
andElasticNet
has been changed ton_splits
for consistency with Scikit-Learn.
- #6 expose relative penalty
- #10 update Scikit-Learn to 0.18
- #3 ensure license and readme are included in sdist
- #8 fix readme encoding
- #14 fix reference to
lambda_best_
in docs - #16 fix import path for UndefinedMetricWarning
- Initial release