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).
2.1.0
Improvements
LocalAndSortedPriorRandomSearchfunctionality intoLocalAndSortedRandomSearch(#1106).LocalAndSortedRandomSearchworks 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
get_random_design(#1066).np.integerused, i.e. as generated from anp.random.RandomState(#1084).Misc
Minor
n_workersparameter is ignored only if it deviates from its default value,1(#1071).