v0.8.0

• 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 customgrid object to aGridSearch 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 thanobject, 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.