glum v3.0

.github/workflows/conda-build.yml

@@ -20,13 +20,13 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - { conda_build_yml: linux_64_python3.9.____cpython,   os: ubuntu-latest,  conda-build-args: '' }
-          - { conda_build_yml: linux_64_python3.12.____cpython,  os: ubuntu-latest,  conda-build-args: '' }
-          - { conda_build_yml: osx_64_python3.9.____cpython,     os: macos-latest,   conda-build-args: '' }
-          - { conda_build_yml: osx_64_python3.12.____cpython,    os: macos-latest,   conda-build-args: '' }
-          - { conda_build_yml: osx_arm64_python3.10.____cpython, os: macos-latest,   conda-build-args: ' --no-test' }
-          - { conda_build_yml: win_64_python3.9.____cpython,     os: windows-latest, conda-build-args: '' }
-          - { conda_build_yml: win_64_python3.12.____cpython,    os: windows-latest, conda-build-args: '' }
+          - { conda_build_yml: linux_64_python3.9.____cpython,   os: ubuntu-latest,  conda-build-args: ' -c conda-forge/label/tabmat_dev -c conda-forge' }
+          - { conda_build_yml: linux_64_python3.12.____cpython,  os: ubuntu-latest,  conda-build-args: ' -c conda-forge/label/tabmat_dev -c conda-forge' }
+          - { conda_build_yml: osx_64_python3.9.____cpython,     os: macos-latest,   conda-build-args: ' -c conda-forge/label/tabmat_dev -c conda-forge' }
+          - { conda_build_yml: osx_64_python3.12.____cpython,    os: macos-latest,   conda-build-args: ' -c conda-forge/label/tabmat_dev -c conda-forge' }
+          - { conda_build_yml: osx_arm64_python3.10.____cpython, os: macos-latest,   conda-build-args: ' -c conda-forge/label/tabmat_dev -c conda-forge --no-test' }
+          - { conda_build_yml: win_64_python3.9.____cpython,     os: windows-latest, conda-build-args: ' -c conda-forge/label/tabmat_dev -c conda-forge' }
+          - { conda_build_yml: win_64_python3.12.____cpython,    os: windows-latest, conda-build-args: ' -c conda-forge/label/tabmat_dev -c conda-forge' }
     steps:
       - name: Checkout branch
         uses: actions/checkout@v4

.github/workflows/daily.yml

@@ -48,7 +48,7 @@ jobs:
           pip install --extra-index-url https://pypi.fury.io/arrow-nightlies/ --prefer-binary --pre --no-deps pyarrow
           echo Install tabmat nightly
           micromamba remove -y --force tabmat
-          pip install --no-use-pep517 --no-deps git+https://github.com/Quantco/tabmat
+          pip install --no-use-pep517 --no-deps git+https://github.com/Quantco/tabmat@tabmat-v4
       - name: Install repository
         shell: bash -el {0}
         run: pip install --no-use-pep517 --no-deps --disable-pip-version-check -e .

CHANGELOG.rst

@@ -7,6 +7,28 @@
 Changelog
 =========
 
+3.0.0 - UNRELEASED
+------------------
+
+**Breaking changes:**
+
+- All arguments to :class:`~glum.GeneralizedLinearRegressorBase`, :class:`~glum.GeneralizedLinearRegressor`, and :class:`GeneralizedLinearRegressorCV` are now keyword-only.
+- All arguments to public methods of :class:`~glum.GeneralizedLinearRegressorBase`, :class:`~glum.GeneralizedLinearRegressor` or :class:`GeneralizedLinearRegressorCV` except `X`, `y`, `sample_weight`, and `offset` are now keyword-only.
+- :class:`~glum.GeneralizedLinearRegressor`'s default value for `alpha` is now `0`, i.e. no regularization.
+- :class:`~glum.GammaDistribution`, :class:`~glum.InverseGaussianDistribution`, :class:`~glum.NormalDistribution` and :class:`~glum.PoissonDistribution` no longer inherit from :class:`~glum.TweedieDistribution`.
+- The power parameter of :class:`~glum.TweedieLink` has been renamed from ``p`` to ``power``, in line with :class:`~glum.TweedieDistribution`.
+- :class:`~glum.TweedieLink` no longer instantiates :class:`~glum.IdentityLink` or :class:`~glum.LogLink` for ``power=0`` and ``power=1``, respectively. On the other hand, :class:`~glum.TweedieLink` is now compatible with ``power=0`` and ``power=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.
+- :class:`~glum.InverseGaussianDistribution` gains a :meth:`~glum.InverseGaussianDistribution.log_likelihood` method.
+
+
 2.7.0 - 2024-02-19
 ------------------
 
@@ -16,7 +38,7 @@ Changelog
 
 **Other changes:**
 
-- Require Python>=3.9 in line with `NEP 29 <https://numpy.org/neps/nep-0029-deprecation_policy.html#support-table>`_
+- 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.
@@ -73,6 +95,7 @@ Changelog
   :class:`~glum.GeneralizedLinearRegressor` and :class:`~glum.GeneralizedLinearRegressorCV`
   to ``'negative.binomial'``.
 
+
 2.4.1 - 2023-03-14
 ------------------
 

README.md

@@ -68,7 +68,7 @@ Why did we choose the name `glum`? We wanted a name that had the letters GLM and
 >>>
 >>> _ = model.fit(X=X, y=y)
 >>>
->>> # .report_diagnostics shows details about the steps taken by the iterative solver
+>>> # .report_diagnostics shows details about the steps taken by the iterative solver.
 >>> diags = model.get_formatted_diagnostics(full_report=True)
 >>> diags[['objective_fct']]
         objective_fct
@@ -79,6 +79,15 @@ n_iter
 3            0.443681
 4            0.443498
 5            0.443497
+>>>
+>>> # Models can also be built with formulas from formulaic.
+>>> model_formula = GeneralizedLinearRegressor(
+...     family='binomial',
+...     l1_ratio=1.0,
+...     alpha=0.001,
+...     formula="bedrooms + np.log(bathrooms + 1) + bs(sqft_living, 3) + C(waterfront)"
+... )
+>>> _ = model_formula.fit(X=house_data.data, y=y)
 
 ```
 

conda.recipe/meta.yaml

@@ -35,7 +35,8 @@ requirements:
     - pandas
     - scikit-learn >=0.23
     - scipy
-    - tabmat >=3.1.0, <4.0.0
+    - formulaic >=0.6
+    - tabmat >=4.0.0a3
 
 test:
   requires: