0.2.3

MAINT refactor Intensifcation and adding unit tests
CHANGE StatusType to Enum
RM parameter importance package
FIX ROAR facade bug for cli
ADD easy access of runhistory within Python
FIX imputation of censored data
FIX conversion of runhistory to EPM training data (in particular running time data)
FIX initial run only added once in runhistory
MV version number to a separate file
MAINT more efficient computations in run_history (assumes average as aggregation function across instances)

0.2.2

FIX 124: SMAC could crash if the number of instances was less than seven
FIX 126: Memory limit was not correctly passed to the target algorithm evaluator
Local search is now started from the configurations with highest EI, drawn by random sampling
Reduce the number of trees to 10 to allow faster predictions (as in SMAC2)
Do an adaptive number of stochastic local search iterations instead of a fixd number (a5914a1d97eed2267ae82f22bd53246c92fe1e2c)
FIX a bug which didn't make SMAC run at least two configurations per call to intensify
ADD more efficient data structure to update the cost of a configuration
FIX do only count a challenger as a run if it actually was run (and not only considered)(a993c29abdec98c114fc7d456ded1425a6902ce3)

CI: travis-ci continuous integration on OSX
ADD: initial design for mulitple configurations, initial design for a random configuration
MAINT: use sklearn PCA if more than 7 instance features are available (as in SMAC 1 and 2)
MAINT: use same minimum step size for the stochastic local search as in SMAC2.
MAINT: use same number of imputation iterations as in SMAC2.
FIX 98: automatically seed the configuration space object based on the SMAC seed.

ADD 55: Separate modules for the initial design and a more flexible constructor for the SMAC class
ADD 41: Add ROAR (random online adaptive racing) class
ADD 82: Add fmin_smac, a scipy.optimize.fmin_l_bfgs_b-like interface to the SMAC algorithm
NEW documentation at https://automl.github.io/SMAC3/stable and https://automl.github.io/SMAC3/dev
FIX 62: intensification previously used a random seed from np.random instead of from SMAC's own random number generator
FIX 42: class RunHistory can now be pickled
FIX 48: stats and runhistory objects are now injected into the target algorithm execution classes
FIX 72: it is now mandatory to either specify a configuration space or to pass the path to a PCS file
FIX 49: allow passing a callable directly to SMAC. SMAC will wrap the callable with the appropriate target algorithm runner

FIX 58 output of the final statistics
FIX 56 using the command line target algorithms (broken since 0.1.1)
FIX 50 as variance prediction, we use the average predicted variance across the instances