Callback

[javdrher]

Implementation of the final use case defined in #7 . This implements a callback strategy to plug a user defined callable in to BayesianOptimizer which is called each iteration and gives full controls over the models. All GPflow manipulations are possible (assigning priors, modifying transforms, fixing parameters). Goal of those callbacks is to assure optimizations are successful, which can be very application specific.

Combined with the optimize_restarts feature, following use-cases are possible:

• By settings optimize_restarts = 0 on the objects it is even possible to do the model optimization manually, e.g. using a multi-step approaches by fixing some parameters first in a first stage.
• Optimize restarts = 1 means the callback sets the initial starting point, optimization is done by the framework
• Optimize restarts > 1: same, but followed by some randomized restarts.

This PR depends on #68 and #72 and should be merged after.

[codecov-io]

Codecov Report

Merging #73 into master will increase coverage by <.01%. The diff coverage is 100%.

@@            Coverage Diff            @@
##           master     #73      +/-   ##
=========================================
+ Coverage    99.8%   99.8%   +<.01%     
=========================================
  Files          17      17              
  Lines        1013    1040      +27     
=========================================
+ Hits         1011    1038      +27     
  Misses          2       2

Impacted Files	Coverage Δ
gpflowopt/acquisition/acquisition.py	100% <100%> (ø)	:arrow_up:
gpflowopt/bo.py	98.94% <100%> (+0.28%)	:arrow_up:

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