CHANGELOG.rst

Changelog

Unreleased

Bug fix:

• The :meth:`score` method of :class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV` now accepts offsets.

Other:

• The CI now runs daily unit tests against the nightly builds of numpy, pandas and scikit-learn.
• oldest-supported-numpy is used for build.

New feature:

• Added :meth:`aic`, :meth:`aicc` and :meth:`bic` attributes to the :class:`~glum.GeneralizedLinearRegressor`. These attributes provide the information criteria based on the training data and the effective degrees of freedom of the maximum likelihood estimate for the model's parameters.

2.0.3 - 2021-11-05

Other:

• We are now specifying the run time dependencies in setup.py, so that missing dependencies are automatically installed from PyPI when installing glum via pip.

2.0.2 - 2021-11-03

Bug fix:

• Fixed the sign of the log likelihood of the Gaussian distribution (not used for fitting coefficients).
• Fixed the wide benchmarks which had duplicated columns (categorical and numerical).

Other:

• The CI now builds the wheels and upload to pypi with every new release.
• Renamed functions checking for qc.matrix compliance to refer to tabmat.

2.0.1 - 2021-10-11

Bug fix:

• Fixed pyproject.toml. We now support installing through pip and pep517.

2.0.0 - 2021-10-08

Breaking changes:

• Renamed the package to glum!! Hurray! Celebration.
• :class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV` lose the fit_dispersion parameter. Please use the :meth:`dispersion` method of the appropriate family instance instead.
• All functions now use sample_weight as a keyword instead of weights, in line with scikit-learn.
• All functions now use dispersion as a keyword instead of phi.
• Several methods :class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV` that should have been private have had an underscore prefixed on their names: :meth:`tear_down_from_fit`, :meth:`_set_up_for_fit`, :meth:`_set_up_and_check_fit_args`, :meth:`_get_start_coef`, :meth:`_solve` and :meth:`_solve_regularization_path`.
• :meth:`glum.GeneralizedLinearRegressor.report_diagnostics` and :meth:`glum.GeneralizedLinearRegressor.get_formatted_diagnostics` are now public.

New features:

• P1 and P2 now accepts 1d array with the same number of elements as the unexpanded design matrix. In this case, the penalty associated with a categorical feature will be expanded to as many elements as there are levels, all with the same value.
• :class:`ExponentialDispersionModel` gains a :meth:`dispersion` method.
• :class:`BinomialDistribution` and :class:`TweedieDistribution` gain a :meth:`log_likelihood` method.
• The :meth:`fit` method of :class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV` now saves the column types of pandas data frames.
• :class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV` gain two properties: family_instance and link_instance.
• :meth:`~glum.GeneralizedLinearRegressor.std_errors` and :meth:`~glum.GeneralizedLinearRegressor.covariance_matrix` have been added and support non-robust, robust (HC-1), and clustered covariance matrices.
• :class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV` now accept family='gaussian' as an alternative to family='normal'.

Bug fix:

• The :meth:`score` method of :class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV` now accepts data frames.
• Upgraded the code to use tabmat 3.0.0.

Other:

• A major overhaul of the documentation. Everything is better!
• The methods of the link classes will now return scalars when given scalar inputs. Under certain circumstances, they'd return zero-dimensional arrays.
• There is a new benchmark available glm_benchmarks_run based on the Boston housing dataset. See here.
• glm_benchmarks_analyze now includes offset in the index. See here.
• glmnet_python was removed from the benchmarks suite.
• The innermost coordinate descent was optimized. This speeds up coordinate descent dominated problems like LASSO by about 1.5-2x. See here.

1.5.1 - 2021-07-22

Bug fix:

• Have the :meth:`linear_predictor` and :meth:`predict` methods of :class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV` honor the offset when alpha is None.

1.5.0 - 2021-07-15

New features:

• The :meth:`linear_predictor` and :meth:`predict` methods of :class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV` gain an alpha parameter (in complement to alpha_index). Moreover, they are now able to predict for multiple penalties.

Other:

• Methods of :class:`~glum._link.Link` now consistently return NumPy arrays, whereas they used to preserve pandas series in special cases.
• Don't list sparse_dot_mkl as a runtime requirement from the conda recipe.
• The minimal numpy pin should be dependent on the numpy version in host and not fixed to 1.16.

1.4.3 - 2021-06-25

Bug fix:

• copy_X = False will now raise a value error when X has dtype int32 or int64. Previously, it would only raise for dtype int64.

1.4.2 - 2021-06-15

Tutorials and documentation improvements:

• Adding tutorials to the documentation.
• Additional documentation improvements.

Bug fix:

• Verbose progress bar now working again.

Other:

• Small improvement in documentation for the alpha_index argument to :meth:`~glum.GeneralizedLinearRegressor.predict`.
• Pinned pre-commit hooks versions.

1.4.1 - 2021-05-01

We now have Windows builds!

1.4.0 - 2021-04-13

Deprecations:

• Fusing the alpha and alphas arguments for :class:`~glum.GeneralizedLinearRegressor`. alpha now also accepts array like inputs. alphas is now deprecated but can still be used for backward compatibility. The alphas argument will be removed with the next major version.

Bug fix:

• We removed entry points to functions in glum_benchmarks from the conda package.

1.3.1 - 2021-04-12

Bug fix:

• :func:`glum._distribution.unit_variance_derivative` is evaluating a proper numexpr expression again (regression in 1.3.0).

1.3.0 - 2021-04-12

New features:

• We added a new solver based on scipy.optimize.minimize(method='trust-constr').
• We added support for linear inequality constraints of type A_ineq.dot(coef_) <= b_ineq.

1.2.0 - 2021-02-04

We removed glum_benchmarks from the conda package.

1.1.1 - 2021-01-11

Maintenance release to get a fresh build for OSX.

1.1.0 - 2020-11-23

New feature:

• Direct support for pandas categorical types in fit and predict. These will be converted into a :class:`CategoricalMatrix`.

1.0.1 - 2020-11-12

This is a maintenance release to be compatible with tabmat>=1.0.0.

1.0.0 - 2020-11-11

Other:

• Renamed alpha_level attribute of :class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV` to alpha_index.
• Clarified behavior of scale_predictors.

0.0.15 - 2020-11-11

Other:

• Pin tabmat<1.0.0 as we are expecting a breaking change with version 1.0.0.

0.0.14 - 2020-08-06

New features:

• Add Tweedie Link.
• Allow infinite bounds.

Bug fixes:

• Unstandardize regularization path.
• No copying in predict.

Other:

• Various memory and performance improvements.
• Update pre-commit hooks.

0.0.13 - 2020-07-23

See git history.

0.0.12 - 2020-07-07

See git history.

0.0.11 - 2020-07-02

See git history.

0.0.10 - 2020-06-30

See git history.

0.0.9 - 2020-06-26

See git history.

0.0.8 - 2020-06-24

See git history.

0.0.7 - 2020-06-17

See git history.

0.0.6 - 2020-06-16

See git history.

0.0.5 - 2020-06-10

See git history.

0.0.4 - 2020-06-08

See git history.

0.0.3 - 2020-06-08

See git history.