Smac

* CI only check code quality for python3.
* Perform local search on configurations from previous runs as proposed in the
original paper from 2011 instead of random configurations as implemented
before.
* CI run travis-ci unit tests with python3.6.
* FIX 167, remove an endless loop which occured when using pSMAC.

* 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).

* 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 63 using memory limit for function target algorithms (broken since 0.1.1).