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• Add support for Python 3.11
  • The SMAC hyperoptimization backend is not currently supported on 3.11
• Add support for scikit-learn 1.3 and 1.4
• Update supported versions of numpy, scipy, pandas, xgboost, lightgbm, snapml, and tensorflow
  • AIF360 does not currently support numpy>=1.24, so testing for aif360 libraries is done using an older version of numpy
• Fix readthedocs build so our documentation is correctly updated
• Add label encoding to our wrapper for XGBClassifier, since it longer does this.
• Shuffle data in some batching tests to (mostly) avoid problems with encoding
• Remove support for Python 3.7 and scikit-learn < 1.0
• Remove autoai_libs wrappers and tests (they have been moved into the autoai_libs package).

Fix a bug where links from pipeline visualizations to operator documentation did not work.

• guard import of mystic
• add upper bound to tensorflow dependency
• remove protobuf installation version constraint.

• Relax some dependency upper bounds
• Add RandomUnderSampler from imblearn
• Add Orbis and Urbis Mystic implementations
• Add 7 more fairness datasets
• Documentation improvements
• Add wrapper for autoai_libs.nsfa transformer

• Updates to autoai_libs schemas to improve pretty-printing
• Update version of static checkers

• Improvements to CI
  • more static checking
  • only using trusted actions
• Improvements to aif360 schemas and derived operators
• Update SnapML schemas for new version
• loosen scipy release bounds

• suppress warnings from AIF360 when tempeh or fairlearn are not installed
• support np.bool_ in JSON schema validation and lale.lib.aif360
• eliminate duplicates in lale.lib.autogen to make it easier to maintain

• Rewrite import_from_sklearn_pipeline, reducing copying and calls to set_params
• update black version used for formatting code to 2023 standard
• Improvements to monoidal TargetEnoder implementation
• minor improvement to task graph code

• Add support for scikit-learn 1.2.0
• Add a monoidal version of TargetEncoder
• Simplify import_from_sklearn_pipeline and improve how it works with higher order operators
• Fix spark schema inspection so it uses only metadata
• Refactor schemas in autoai_libs

1. Test-suite improvements such as adding sphinx build
2. Documentation improvements: fixed all sphinx warnings
3. New operators: TargetEncoder and fairSMOTE
4. Bug fixes and support of new schema features such as "transient":"alwaysPrint" to always print a hyperparameter value in pretty_print even if it has default value.