Smac

Bug Fixes

* Fixes the docstring of SMAC's default acquisition function optimizer (653)
* Correctly attributes the configurations' origin if using the `FixedSet` acquisition function optimizer (653)
* Fixes an infinite loop which could occur if using only a single configuration per iteration (654)
* Fixes a bug in the kernel construction of the `BOFacade` (655)

Minor Changes

* Upgrade the minimal scikit-learn dependency to 0.22.X.
* Make GP predictions faster (638)
* Allow passing `tae_runner_kwargs` to `ROAR`.
* Add a new StatusType `DONOTADVANCE` for runs that would not benefit from a higher budgets. Such runs are always used
to build a model for SH/HB (632)
* Add facades/examples for HB/SH (610)
* Compute acquisition function only if necessary (627,629)

Bug Fixes
* Fixes a bug which caused SH/HB to consider TIMEOUTS on all budgets for model building (632)
* Fixed a bug in adaptive capping for SH (619,622)

Major Changes

* Support for Successive Halving and Hyperband as new instensification/racing strategies.
* Improve the SMAC architecture by moving from an architecture where new candidates are passed to the racing algorithm
to an architecture where the racing algorithm requests new candidates, which is necessary to implement the
[BOHB](http://proceedings.mlr.press/v80/falkner18a.html) algorithm (#551).
* Source code is now PEP8 compliant. PEP8 compliance is checked by travis-ci (565).
* Source code is now annotated with type annotation and checked with mypy.

Minor Changes

* New argument to directly control the size of the initial design (553).
* Acquisition function is fed additional arguments at update time (557).
* Adds new acquisition function maximizer which goes through a list of pre-specified configurations (558).
* Document that the dependency pyrfr does not work with SWIG 4.X (599).
* Improved error message for objects which cannot be serialized to json (453).
* Dropped the random forest with HPO surrogate which was added in 0.9.
* Dropped the EPILS facade which was added in 0.6.
* Simplified the interface for constructing a runhistory object.
* removed the default rng from the Gaussian process priors (554).
* Adds the possibility to specify the acquisition function optimizer for the random search (ROAR) facade (563).
* Bump minimal version of `ConfigSpace` requirement to 0.4.9 (578).
* Examples are now rendered on the website using sphinx gallery (567).

Bug fixes

* Fixes a bug which caused SMAC to fail for Python function if `use_pynisher=False` and an exception was raised
(437).
* Fixes a bug in which samples from a Gaussian process were shaped differently based on the number of dimesions of
the `y`-array used for fitting the GP (556).
* Fixes a bug with respect saving data as json (555).
* Better error message for a sobol initial design of size `>40` ( 564).
* Add a missing return statement to `GaussianProcess._train`.

Changes

* Updated the default hyperparameters of the Gaussian process facade to follow recent research (529)
* Enabled `flake8` code style checks for newly merged code (525)

Major changes

* Local search now starts from observed configurations with high acquisition function values, low cost and the from
unobserved configurations with high acquisition function values found by random search (509)
* Reduces the number of mandatory requirements (516)
* Make Gaussian processes more resilient to linalg error by more aggressively adding noise to the diagonal (511)
* Inactive hyperparameters are now imputed with a value outside of the modeled range (-1) (508)
* Replace the GP library George by scikit-learn (505)
* Renames facades to better reflect their use cases (492), and adds a table to help deciding which facade to use (495)
* SMAC facades now accept class arguments instead of object arguments (486)

Minor changes

* Vectorize local search for improved speed (500)
* Extend the Sobol and LHD initial design to work for non-continuous hyperparameters as well applying an idea similar
to inverse transform sampling (494)

Bug fixes

* Fixes a regression in the validation scripts (519)
* Fixes a unit test regression with numpy 1.17 (523)
* Fixes an error message (510)
* Fixes an error making random search behave identical for all seeds

Major changes

* ADD further acquisition functions: PI and LCB
* SMAC can now be installed without installing all its dependencies
* Simplify setup.py by moving most thing to setup.cfg

Bug fixes

* RM typing as requirement
* FIX import of authors in setup.py
* MAINT use json-file as standard pcs format for internal logging