Prepare for releasing 3.1.1

.github/workflows/ci.yml

@@ -66,5 +66,7 @@ jobs:
         run: |
           mkdir ../temp
           cp README.md ../temp
+          mkdir ../temp/data
+          cp data/housing.parquet ../temp/data
           cd ../temp
           pixi run --manifest-path ../glum/pixi.toml -e ${{ matrix.environment }} python -m doctest -v README.md

CHANGELOG.rst

@@ -7,10 +7,10 @@
 Changelog
 =========
 
-UNRELEASED
-----------
+3.1.1 - 2025-01-13
+------------------
 
-**Bug fix:
+**Bug fix:**
 
 - Fixed a bug where :meth:`~glum.TweedieDistribution._rowwise_gradient_hessian` and :meth:`~glum.TweedieDistribution._eta_mu_deviance` would call functions with wrong arguments in the ``p = 3`` case.
 - Fixed :class:`glum.InverseGaussianDistribution` not using the optimized gradient, Hessian and deviance implementations, as well as those derivatives having the wrong sign.
@@ -34,6 +34,7 @@ UNRELEASED
 - Fixed a bug where :meth:`glum.GeneralizedLinearRegressor.fit` would raise a ``dtype`` mismatch error if fit with ``alpha_search=True``.
 - Use data type (``float64`` or ``float32``) dependent precision in solvers.
 
+
 3.0.2 - 2024-06-25
 ------------------
 
@@ -48,6 +49,7 @@ UNRELEASED
 - 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
 ------------------
 

README.md

@@ -33,15 +33,18 @@ Why did we choose the name `glum`? We wanted a name that had the letters GLM and
 # A classic example predicting housing prices
 
 ```python
+>>> import pandas as pd
 >>> from sklearn.datasets import fetch_openml
 >>> from glum import GeneralizedLinearRegressor
 >>>
 >>> # This dataset contains house sale prices for King County, which includes
 >>> # Seattle. It includes homes sold between May 2014 and May 2015.
->>> house_data = fetch_openml(name="house_sales", version=3, as_frame=True)
+>>> # The full version of this dataset can be found at:
+>>> # https://www.openml.org/search?type=data&status=active&id=42092
+>>> house_data = pd.read_parquet("data/housing.parquet")
 >>>
 >>> # Use only select features
->>> X = house_data.data[
+>>> X = house_data[
 ...     [
 ...         "bedrooms",
 ...         "bathrooms",
@@ -59,7 +62,7 @@ Why did we choose the name `glum`? We wanted a name that had the letters GLM and
 >>>
 >>> # Model whether a house had an above or below median price via a Binomial
 >>> # distribution. We'll be doing L1-regularized logistic regression.
->>> price = house_data.target
+>>> price = house_data["price"]
 >>> y = (price < price.median()).values.astype(int)
 >>> model = GeneralizedLinearRegressor(
 ...     family='binomial',
@@ -88,7 +91,7 @@ n_iter
 ...     alpha=0.001,
 ...     formula="bedrooms + np.log(bathrooms + 1) + bs(sqft_living, 3) + C(waterfront)"
 ... )
->>> _ = model_formula.fit(X=house_data.data, y=y)
+>>> _ = model_formula.fit(X=house_data, y=y)
 
 ```
 

pixi.toml

@@ -13,8 +13,8 @@ store-benchmark-golden-master = "python tests/glm/test_benchmark_golden_master.p
 
 [feature.docs.tasks]
 make-docs = "cd docs && make html"
-serve-docs = { cmd = "python -m http.server --directory docs/_build/html", depends_on = ["make-docs"] }
-readthedocs = { cmd = "rm -rf $READTHEDOCS_OUTPUT/html && cp -r docs/_build/html $READTHEDOCS_OUTPUT/html", depends_on = ["make-docs"] }
+serve-docs = { cmd = "python -m http.server --directory docs/_build/html", depends-on = ["make-docs"] }
+readthedocs = { cmd = "rm -rf $READTHEDOCS_OUTPUT/html && cp -r docs/_build/html $READTHEDOCS_OUTPUT/html", depends-on = ["make-docs"] }
 
 [feature.benchmark.tasks]
 glm-benchmarks-run = "glm_benchmarks_run"