Fairlearn

What's Changed
* Setting up the landing page for version release and prep for v0.11.0 by alliesaizan in https://github.com/fairlearn/fairlearn/pull/1328
* ENH Adding Equal Opportunity to fairness metrics by faildeny in https://github.com/fairlearn/fairlearn/pull/1325
* DOC Add contributor to intersectionality notebook by hildeweerts in https://github.com/fairlearn/fairlearn/pull/1330
* MNT add pyarrow dependency by adrinjalali in https://github.com/fairlearn/fairlearn/pull/1335
* MNT fix a few pandas warnings by adrinjalali in https://github.com/fairlearn/fairlearn/pull/1336
* Fixed missing parenthesis in the Equalized odds formula by lucacorbucci in https://github.com/fairlearn/fairlearn/pull/1337
* MNT update sparse to sparse_output by hildeweerts in https://github.com/fairlearn/fairlearn/pull/1345
* MNT delete trailing note in version guide by hildeweerts in https://github.com/fairlearn/fairlearn/pull/1346
* FIX more pandas issues by adrinjalali in https://github.com/fairlearn/fairlearn/pull/1339
* MNT Build fixes by riedgar-ms in https://github.com/fairlearn/fairlearn/pull/1351
* [Doc] Small fixes by riedgar-ms in https://github.com/fairlearn/fairlearn/pull/1354
* Release upper bound on scikit-learn by riedgar-ms in https://github.com/fairlearn/fairlearn/pull/1356
* DOC Changing the dataset from Adult to Diabetes Hospital in Getting Started by alliesaizan in https://github.com/fairlearn/fairlearn/pull/1310
* [Build] Fix issues by riedgar-ms in https://github.com/fairlearn/fairlearn/pull/1361
* MNT Fix bug: FloatTransformer output dimension was set incorrectly. by SeanMcCarren in https://github.com/fairlearn/fairlearn/pull/1333
* DOC Fix typos in the documentation by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1362
* MNT Fix failing estimator checks related to the newest sklearn changes by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1369
* MNT Replace flake8 with ruff for linting by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1365
* MNT DOC Fix (some) doc build warnings and add/improve documentation by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1363
* MNT Update the version of black to 24.4.2 by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1370
* MNT Update .git-blame-ignore-revs with linting changes by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1371
* DOC Docs improvements, structure and rephrasing by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1372
* MNT remove lightgbm pin by adrinjalali in https://github.com/fairlearn/fairlearn/pull/1375
* MNT unpin pytest by adrinjalali in https://github.com/fairlearn/fairlearn/pull/1376
* DOC change OneHotEncoder.sparse to sparse_output by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1379
* MNT DOC Clear docs build from all warnings by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1378
* MNT Address (not always reproducible) docs build warnings by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1382
* MNT DOC Add example gallery to the API docs by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1389
* MNT Update code to fit the new numpy2.0.0 API by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1387
* MNT Remove FutureWarning for the deprecated default usage of ThresholdOptimizer.predict_method by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1388
* FIX Add classes_ attribute to AdversarialFairnessClassifier by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1384
* MNT Add caching for branches in the doc and doctest CircleCI builds by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1392
* FIX all estimator checks related to: check_parameters_default_constructible by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1393
* FIX all estimator checks related to: check_supervised_y_no_nan by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1394
* DOC Update acs_income.rst by excepto64 in https://github.com/fairlearn/fairlearn/pull/1396
* FIX failing estimator checks: "check_classifiers_train" and "check_regressors_no_decision_function" by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1397
* Fix check_fit_non_negative failing estimator check (+3 others) by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1398
* DOC prepare changelog for release by adrinjalali in https://github.com/fairlearn/fairlearn/pull/1401
* Fixing failing estimator checks for the AdversarialFairnessRegressor by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1399
* Bump actions/download-artifact from 3 to 4.1.7 in /.github/workflows by dependabot in https://github.com/fairlearn/fairlearn/pull/1406
* Add test for `fetch_acs_income` by EdAbati in https://github.com/fairlearn/fairlearn/pull/1403
* Remove unused dataframe creation by EdAbati in https://github.com/fairlearn/fairlearn/pull/1404
* [Doc] Tweak text about community calls by riedgar-ms in https://github.com/fairlearn/fairlearn/pull/1407
* MNT Fix errors caused by three estimator checks by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1405
* MNT Fix all remaining estimator checks (except pickle related errors) by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1410
* DOC move adversarial examples by adrinjalali in https://github.com/fairlearn/fairlearn/pull/1380
* MNT fix version constraint for torch by adrinjalali in https://github.com/fairlearn/fairlearn/pull/1418
* DOC Add Tamara to the website by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1420
* ENH added agg argument to equalized odds difference and ratio to support "average odds" by IanEisenberg in https://github.com/fairlearn/fairlearn/pull/960
* DOC Add jupyterlite button to the examples by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1422
* Update maintainers by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1424
* Update value in __init__ as preparation for v0.11.0 release by TamaraAtanasoska in https://github.com/fairlearn/fairlearn/pull/1426

New Contributors
* faildeny made their first contribution in https://github.com/fairlearn/fairlearn/pull/1325
* lucacorbucci made their first contribution in https://github.com/fairlearn/fairlearn/pull/1337
* excepto64 made their first contribution in https://github.com/fairlearn/fairlearn/pull/1396
* EdAbati made their first contribution in https://github.com/fairlearn/fairlearn/pull/1403
* IanEisenberg made their first contribution in https://github.com/fairlearn/fairlearn/pull/960

**Full Changelog**: https://github.com/fairlearn/fairlearn/compare/v0.10.0...v0.11.0

- Added bootstrapping to [:class:`MetricFrame`](https://github.com/fairlearn/fairlearn/blob/release/v0.10.0/docs/user_guide/installation_and_version_guide/v0.10.0.rst#id1), along with a new section in the user guide

- Added intersectionality in mental health care example notebook.

- Added user guide for [:class:`fairlearn.postprocessing.ThresholdOptimizer`](https://github.com/fairlearn/fairlearn/blob/release/v0.10.0/docs/user_guide/installation_and_version_guide/v0.10.0.rst#id3)

* Added Credit Loan Decisions example notebook.
* Bugfix for `fairlearn.metrics.selection_rate` to handle `TypeError` when input is scalar.
* Added support for pickling classes in `fairlearn.reductions`.
* Bugfix for `sklearn.preprocessing.OneHotEncoder` in `fairlearn.adversarial` to ensure compatibility with scikit-learn 1.2.
* Set default value of `parser` of `sklearn.datasets.fetch_openml` in `fairlearn.datasets` to `liac-arff` to match behavior before scikit-learn 1.2.
* Replace calculations in `fairlearn.reductions` by matrix multiplication which can lead to substantial speed-ups for `fairlearn.reductions.ExponentiatedGradient` for simple estimators like logistic regression.
* Added `as_frame` (with default `True`) argument to `fairlearn.datasets.fetch_diabetes_hospital`.
* Changed `as_frame` default to `True` for all remaining datasets.
* Changed `fairlearn.metrics.MetricFrame` so that all results (including aggregations) are computed in the constructor and then cached.
* Bugfix for `fairlearn.adversarial` that changes the loss function `NLLLoss` to `CrossEntropyLoss`.
* Various documentation tweaks and enhancements, including restructuring of the user guide and breaking out information in the API reference to separate pages.

* Relaxed checks made on `X` in `_validate_and_reformat_input()` since that is the concern of the underlying estimator and not Fairlearn.
* Add support for Python 3.9 and 3.10, remove support for Python 3.6 and 3.7
* Added error handling in `MetricFrame`. Methods `group_max`,`group_min`, `difference` and `ratio` now accept `errors` as a parameter, which could either be `raise` or `coerce`.
* Fixed a bug whereby passing a custom`grid` object to a`GridSearch` reduction would result in a `KeyError` if the column names were not ordered integers.
* `fairlearn.preprocessing.CorrelationRemover` now exposes `n_features_in_` and `feature_names_in_`.
* Added the ACSIncome dataset and corresponding documentation.
* Add `sphinxcontrib-bibtex` extension to manage citations in documentation using bibtex.
* Added support for explicitly specifying optimization objective in `fairlearn.reductions.ExponentiatedGradient`. Added support for cost sensitive classification in `fairlearn.reductions.ErrorRate`.
* Internal performance improvements for `fairlearn.metrics.MetricFrame`. Some results may now have a more appropriate type than`object`, but otherwise the only visible difference should be a substantial speed increase.
* Added `fairlearn.metrics.plot_model_comparison` to create scatter plots for comparing multiple models along two metrics.
* Added adversarial mitigation approaches `fairlearn.adversarial.AdversarialFairnessClassifier` and `fairlearn.adversarial.AdversarialFairnessRegressor`.

* Added new `count()` metric, so that the number of data points in each
group is noted when using `MetricFrame`
* Changed `MetricFrame` constructor API, so `metric` argument is now `metrics` and
all positional arguments are now keyword arguments. Previous call format still works
(until v0.10.0), but issues a deprecation warning.
* `postprocessing.ThresholdOptimizer` now accepts `predict_method` as a
parameter which allows users to define which estimator method should be used
to get the prediction values: `"predict_proba" and "decision_function"` for
soft values and `"predict"` for hard values from classifiers.
* Removed `fairlearn.widgets` module including the `FairlearnDashboard`.
Instead, the `fairlearn.metrics.MetricFrame` supports plotting as explained
in the corresponding [user guide section](https://fairlearn.org/main/user_guide/assessment.html#plotting-grouped-metrics).
* Added return value (`self`) to `fairlearn.reductions.ExponentiatedGradient`.

* Bugfix for `_merge_columns()` when using multiple sensitive features with
long names. This previously caused groups to get merged if the concatenation
of their string representations was identical until the cutoff limit.