Smac

Improvements
- Change the surrogate model to be retrained after every iteration by default in the case of blackbox optimization
(1106).
- Integrate `LocalAndSortedPriorRandomSearch` functionality into `LocalAndSortedRandomSearch` (1106).
- Change the way the `LocalAndSortedRandomSearch` works such that the incumbent always is a starting point and that
random configurations are sampled as the basis of the local search, not in addition (1106).

Bugfixes
- Fix path for dask scheduler file (1055).
- Add OrdinalHyperparameter for random forest imputer (1065).
- Don't use mutable default argument (1067).
- Propagate the Scenario random seed to `get_random_design` (1066).
- Configurations that fail to become incumbents will be added to the rejected lists (1069).
- SMAC RandomForest doesn't crash when `np.integer` used, i.e. as generated from a `np.random.RandomState` (1084).
- Fix the handling of n_points/ challengers in the acquisition maximizers, such that this number now functions as the
number of points that are sampled from the acquisition function to find the next challengers. Now also doesn't
restrict the config selector to n_retrain many points for finding the max, and instead uses the defaults that are
defined via facades/ scenarios (1106).

Misc
- ci: Update action version (1072).

Minor
- When a custom dask client is provided, emit the warning that the `n_workers` parameter is ignored only if it deviates from its default value, `1` ([1071](https://github.com/automl/SMAC3/pull/1071)).

Improvements
- Add an error when we get an empty dict data_to_scatter so that we can avoid an internal error caused in Dask precautiously.
- Add experimental instruction for installing SMAC in Windows via a WSL.
- More detailed documentation regarding continuing runs.
- Add a new example that demonstrates the use of intensification to speed up cross-validation for machine learning.

Bugfixes
- Fix bug in the incumbent selection in the case that multi-fidelity is combined with multi-objective (1019).
- Fix callback order (1040).
- Handle configspace as dictionary in mlp and parego example.
- Adapt sgd loss to newest scikit-learn version.

Improvements
- Callbacks registration is now a public method of the optimizer and allows callbacks to be inserted at a specific position.
- Adapt developer install instructions to include pre-commit installation
- Add option to pass a dask client to the facade, e.g. enables running on a hpc cluster (983).
- Added scenario.use_default_config argument/attribute=False, that adds the user's configspace default configuration
as an additional_config to the inital design if set to True. This adds one additional configuration to the number of configs
originating from the initial design. Since n_trials is still respected, this results in one fewer BO steps
- Adapt developer install instructions to include pre-commit installation.
- Add option to pass a dask client to the facade, e.g. enables running on a hpc cluster (983).
- Add example for using a callback to log run metadata to a file (996).
- Move base callback and metadata callback files to own callback directory.
- Add a workaround to be able to pass a dataset via dask.scatter so that serialization/deserialization in Dask becomes much quicker (993).

Bugfixes
- The ISB-pair differences over the incumbent's configurations are computed correctly now (956).
- Adjust amount of configurations in different stages of hyperband brackets to conform to the original paper.
- Fix validation in smbo to use the seed in the scenario.
- Change order of callbacks, intensifier callback for incumbent selection is now the first callback.
- intensifier.get_state() will now check if the configurations contained in the queue is stored in the runhistory (997)

Improvements
- Clarify origin of configurations (908).
- Random forest with instances predicts the marginalized costs by using a C++ implementation in `pyrfr`, which is much faster (903).
- Add version to makefile to install correct test release version.
- Add option to disable logging by setting `logging_level=False`. (947)

Bugfixes
- Continue run when setting incumbent selection to highest budget when using Successive Halving (907).
- If integer features are used, they are automatically converted to strings.

Workflows
- Added workflow to update pre-commit versions (874).

Misc
- Added benchmarking procedure to compare to previous releases.

- Completely reimplemented the intensifiers (including Successive Halving and Hyperband): All intensifiers support multi-fidelity, multi-objective and multi-threading by nature now.
- Expected behaviour for ask-and-tell interface ensured (also for Successive Halving).
- Continuing a run is now fully supported.
- Added more examples.
- Updated documentation based on new implementation.
- Added benchmark to compare different versions.

Bugfixes
- Correct handling of integer hyperparameters in the initial design (531)

Bugfixes
- Fixed random weight (re-)generalization of multi-objective algorithms: Before the weights were generated for each call to ``build_matrix``, now we only re-generate them for every iteration.
- Optimization may get stuck because of deep copying an iterator for callback: We removed the configuration call from ``on_next_configurations_end``.

Minor
- Removed example badget in README.
- Added SMAC logo to README.