Tpot2

What's Changed
- Fixed Tutorial 8
- Updated to pandas 2.2.0+
- Eval error moved to a new column
- Fixed the parent selection function (steady state)

* sync dev by perib in https://github.com/EpistasisLab/tpot2/pull/116
* Merge pull request 116 from EpistasisLab/main by jay-m-dev in https://github.com/EpistasisLab/tpot2/pull/118
* fix turorial 8 by perib in https://github.com/EpistasisLab/tpot2/pull/119
* Pandas update by nickotto in https://github.com/EpistasisLab/tpot2/pull/121
* Dev by perib in https://github.com/EpistasisLab/tpot2/pull/120
* Dev by jay-m-dev in https://github.com/EpistasisLab/tpot2/pull/125


**Full Changelog**: https://github.com/EpistasisLab/tpot2/compare/0.1.5-alpha...0.1.6-alpha

What's Changed
* fixed bug preventing recursive search spaces - by perib in https://github.com/EpistasisLab/tpot2/pull/113
* fixed config dict typo preventing lists of configuration dictionaries from being used - by perib in https://github.com/EpistasisLab/tpot2/pull/114


**Full Changelog**: https://github.com/EpistasisLab/tpot2/compare/0.1.4-alpha...0.1.5-alpha

What's Changed
* Hyperparameter mutation probabilities and gradual changes by perib in https://github.com/EpistasisLab/tpot2/pull/103
* selection fix when zero mutation/cx selected by perib in https://github.com/EpistasisLab/tpot2/pull/105
* Increase reproducibility and control stochasticity by jgh9094 in https://github.com/EpistasisLab/tpot2/pull/106
* fix to allow mutations to actually occur to an individual by jgh9094 in https://github.com/EpistasisLab/tpot2/pull/108
* Random initialization by jgh9094 in https://github.com/EpistasisLab/tpot2/pull/107
* bug fixes by perib in https://github.com/EpistasisLab/tpot2/pull/109
* preprocessing fix by perib in https://github.com/EpistasisLab/tpot2/pull/110


**Full Changelog**: https://github.com/EpistasisLab/tpot2/compare/0.1.3-alpha...0.1.4-alpha

What's Changed
* API changes on the Base Evolver
* SteadyStateEvolver integration and updates
* New Tutorials and updates
* Updated the base evolver for improved functionality.
* Enhanced the Dask code for increased stability.
* Added the sklearnex library and support.

Changes to BaseEvolver:

- Sample probabilities for mutation and crossover individually, rather than using fixed frequencies.
- Reorganized step() and one_generation_step(), moving survival selection and evaluation to step() and adding generate_offspring() for more flexibility in creating offspring.
- Introduced generate_offspring() to address customization of offspring creation, simplifying modifications.
- Integrated survival selection and parent selection in the population class to streamline user interaction at the evolver level.
- Enabled the use of individual mutation and crossover functions and their respective weights.
- Maintained support for both "mutate then crossover" and "crossover then mutate" methods, with consideration for potential code complexity.

Other Changes:

- Updated the parallel eval function for the base evolver to handle futures and timeouts individually.
- Reduced the fallback timeout to 1.25 times max_eval_time_seconds.
- Resolved issues with Jupyter notebooks for GitHub by removing problematic markdown syntax.
- Noted that the steady-state code remains unchanged, with a suggestion to consider altering the population class for improved clarity and functionality by separating evaluated parents from unevaluated offspring. Currently, both are stored in the same list within the population class.

**Full Changelog**: https://github.com/EpistasisLab/tpot2/compare/0.1.2-alpha...0.1.3-alpha

API changes:
Removed max_children and max_depth.
Added linear_pipeline (bool). If True, it will evolve linear pipelines.
Changed inner_config_dict: If set to None, your graph should have no inner nodes. (Previously, if None, inner nodes would be pulled from root_config_dict)

Bug fixes:

In rare circumstances, some pipelines could not be timed-out correctly with max_eval_time_seconds. Additionally, the overall timeout from max_time_seconds was only checked at the end of a generation. Both of these could lead to long runtimes. Now TPOT2 can terminate the evaluation within a generation so that the final runtime is closer to max_time_seconds. There is also a fallback timeout to terminate the generation if it appears that the threads are not being terminated in time.

Optimized calculation of the pareto frontier.

Some other minor internal edits/cleanups/fixes.

Known Issues:

The fallback timeout is currently not compatible with the population evaluation progress bar (though the generation progress bar works correctly). Temporarily set the evaluation progress bar to be verbose>=6. If the progress bar is used, the fallback timeout will not work.