Version 2.1.0

2.1.0

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).

What's Changed

• Change stale-reminder to recent-reminder by @helegraf in #1095
• Version 2.1.0 (#1108) by @benjamc in #1108

Full Changelog: v2.0.2...v2.1.0