Changelog
3.1.0 - 2024-11-11
New features:
New argument
max_inner_iter
for classesGeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
to control the maximum number of iterations of the inner solver in the IRLS-CD algorithm.New fitted attributes
col_means_
andcol_stds_
for classesGeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
.GeneralizedLinearRegressor
now prints more informative logs when fitting withalpha_search=True
andverbose=True
.
Bug fixes:
Fixed a bug where
glum.GeneralizedLinearRegressor.fit()
would raise adtype
mismatch error if fit withalpha_search=True
.Use data type (
float64
orfloat32
) dependent precision in solvers.
3.0.2 - 2024-06-25
Bug fix:
Fixed
wald_test()
when usingterms
and no intercept.
Other changes:
Moved the development infrastructure to pixi.
Moved the linting and formatting to ruff.
Removed libblas MKL from the development environment.
Replaced deprecated ‘oldest-supported-numpy’ dependency with ‘numpy’ to support 2.0 release.
3.0.1 - 2024-05-23
Bug fix:
We now support scikit-learn 1.5.
3.0.0 - 2024-04-27
Breaking changes:
All arguments to
GeneralizedLinearRegressorBase
,GeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
are now keyword-only.All arguments to public methods of
GeneralizedLinearRegressorBase
,GeneralizedLinearRegressor
orGeneralizedLinearRegressorCV
exceptX
,y
,sample_weight
andoffset
are now keyword-only.GeneralizedLinearRegressor
’s default value foralpha
is now0
, i.e. no regularization.GammaDistribution
,InverseGaussianDistribution
,NormalDistribution
andPoissonDistribution
no longer inherit fromTweedieDistribution
.The power parameter of
TweedieLink
has been renamed fromp
topower
, in line withTweedieDistribution
.TweedieLink
no longer instantiatesIdentityLink
orLogLink
forpower=0
andpower=1
, respectively. On the other hand,TweedieLink
is now compatible withpower=0
andpower=1
.
New features:
Added a formula interface for specifying models.
Improved feature name handling. Feature names are now created for non-pandas input matrices too. Furthermore, the format of categorical features can be specified by the user.
Term names are now stored in the model’s attributes. This is useful for categorical features, where they refer to the whole variable, not just single levels.
Added more options for treating missing values in categorical columns. They can either raise a
ValueError
("fail"
), be treated as all-zero indicators ("zero"
) or represented as a new category ("convert"
).meth:GeneralizedLinearRegressor.wald_test can now perform tests based on a formula string and term names.
InverseGaussianDistribution
gains alog_likelihood()
method.
2.7.0 - 2024-02-19
Bug fix:
Added cython compiler directive legacy_implicit_noexcept = True to fix performance regression with cython 3.
Other changes:
Require Python>=3.9 in line with NEP 29 <https://numpy.org/neps/nep-0029-deprecation_policy.html#support-table>.
Build and test with Python 3.12 in CI.
Added line search stopping criterion for tiny loss improvements based on gradient information.
Added warnings about breaking changes in future versions.
2.6.0 - 2023-09-05
New features:
Added the complementary log-log (
cloglog
) link function.Added the option to store the covariance matrix after estimating it. In this case, the covariance matrix does not have to be recomputed when calling inference methods.
Added methods for performing Wald tests based on a restriction matrix, feature names or term names.
Added a method for creating a coefficient table with confidence intervals and p-values.
Bug fix:
Fixed
covariance_matrix()
mutating feature names when called with a data frame. See here.
Other changes:
When computing the covariance matrix, check whether the design matrix is ill-conditioned for all types of input. Furthermore, do it in a more efficient way.
Pin
tabmat<4.0.0
(the new release will bring breaking changes).
2.5.2 - 2023-06-02
Bug fix
Fix the
glm_benchmarks_analyze
command line tool. See here.Fixed a bug in
GeneralizedLinearRegressor
when fit on a data set with a constant column andwarm_start=True
. See here.
Other changes:
Remove dev dependency on
dask_ml
.We now pin
llvm-openmp=11
when creating the wheel for macOS in line with what scikit-learn does.
2.5.1 - 2023-05-19
Other changes:
Better error message when the number of input features is different between the fit and predict methods.
Bug fix:
We fixed a bug in the computation of
log_likelihood()
. Previously, this method just returnedNone
.
2.5.0 - 2023-04-28
New feature:
Added Negative Binomial distribution by setting the
'family'
parameter ofGeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
to'negative.binomial'
.
2.4.1 - 2023-03-14
Bug fixes:
Fixed an issue with
_score_matrix()
which failed when called with a tabmat matrix input.
Other changes:
Removes unused scikit-learn cython imports.
2.4.0 - 2023-01-31
Other changes:
LogitLink
has been made public.Apple Silicon wheels are now uploaded to PyPI.
2.3.0 - 2023-01-06
Bug fixes:
A data frame with dense and sparse columns was transformed to a dense matrix instead of a split matrix by
_set_up_and_check_fit_args()
. Fixed by callingtabmat.from_pandas
on any data frame.
New features:
The following classes and functions have been made public:
BinomialDistribution
,ExponentialDispersionModel
,GammaDistribution
,GeneralizedHyperbolicSecant
,InverseGaussianDistribution
,NormalDistribution
,PoissonDistribution
,IdentityLink
,Link
,LogLink
,TweedieLink
,get_family()
andget_link()
.The distribution and link classes now feature a more lenient equality check instead of the default identity check, so that, e.g.,
TweedieDistribution(1) == TweedieDistribution(1)
now returnsTrue
.
2.2.1 - 2022-11-25
Other changes:
Fixing pypi upload issue. Version 2.2.0 will not be available through the standard distribution channels.
2.2.0 - 2022-11-25
New features:
Add an argument to GeneralizedLinearRegressorBase to drop the first category in a Categorical column using [implementation in tabmat](https://github.com/Quantco/tabmat/pull/168)
One may now request the Tweedie loss by setting the
'family'
parameter ofGeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
to'tweedie'
.
Bug fixes:
Setting bounds for constant columns was not working (bounds were internally modified to 0). A similar issue was preventing inequalities from working with constant columns. This is now fixed.
Other changes:
No more builds for 32-bit systems with python >= 3.8. This is due to scipy not supporting it anymore.
2.1.2 - 2022-07-01
Other changes:
Next attempt to build wheel for PyPI without
--march=native
.
2.1.1 - 2022-07-01
Other changes:
We are now building the wheel for PyPI without
--march=native
to make it more portable across architectures.
2.1.0 - 2022-06-27
New features:
Added
aic()
,aicc()
andbic()
attributes to theGeneralizedLinearRegressor
. 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.std_errors()
andcovariance_matrix()
ofGeneralizedLinearRegressor
now accept data frames with categorical data.
Bug fixes:
The
score()
method ofGeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
now accepts offsets.Fixed the calculation of the information matrix for the Binomial distribution with logit link, which affected nonrobust standard errors.
Other:
The CI now runs daily unit tests against the nightly builds of numpy, pandas and scikit-learn.
The minimally required version of tabmat is now 3.1.0.
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 installingglum
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.GeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
lose thefit_dispersion
parameter. Please use thedispersion()
method of the appropriate family instance instead.All functions now use
sample_weight
as a keyword instead ofweights
, in line with scikit-learn.All functions now use
dispersion
as a keyword instead ofphi
.Several methods
GeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
that should have been private have had an underscore prefixed on their names:tear_down_from_fit()
,_set_up_for_fit()
,_set_up_and_check_fit_args()
,_get_start_coef()
,_solve()
and_solve_regularization_path()
.glum.GeneralizedLinearRegressor.report_diagnostics()
andglum.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.
ExponentialDispersionModel
gains adispersion()
method.BinomialDistribution
andTweedieDistribution
gain alog_likelihood()
method.The
fit()
method ofGeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
now saves the column types of pandas data frames.GeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
gain two properties:family_instance
andlink_instance
.std_errors()
andcovariance_matrix()
have been added and support non-robust, robust (HC-1), and clustered covariance matrices.GeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
now acceptfamily='gaussian'
as an alternative tofamily='normal'
.
Bug fix:
The
score()
method ofGeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
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 includesoffset
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
linear_predictor()
andpredict()
methods ofGeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
honor the offset whenalpha
isNone
.
1.5.0 - 2021-07-15
New features:
The
linear_predictor()
andpredict()
methods ofGeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
gain analpha
parameter (in complement toalpha_index
). Moreover, they are now able to predict for multiple penalties.
Other:
Methods of
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 thenumpy
version inhost
and not fixed to1.16
.
1.4.3 - 2021-06-25
Bug fix:
copy_X = False
will now raise a value error whenX
has dtypeint32
orint64
. Previously, it would only raise for dtypeint64
.
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 topredict()
.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
andalphas
arguments forGeneralizedLinearRegressor
.alpha
now also accepts array like inputs.alphas
is now deprecated but can still be used for backward compatibility. Thealphas
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:
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
andpredict
. These will be converted into aCategoricalMatrix
.
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 ofGeneralizedLinearRegressor
andGeneralizedLinearRegressorCV
toalpha_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.