Remove event based logging pattern

[adrianmolzon]

Hey all, I'm back with another pull request! This one removes some of the old functionality for saving logs since I've created a better way of saving state. In doing so, I've tried to simplify the file structure, mostly by deleting unnecessary files, but also by moving all the logging methods to a Logger class in logger.py. This class is now invoked on initialization of the BayesianOptimization object, and exposes the steps previously invoked by the Observable pattern, optimization_start, optimization_step, and optimization_end. I think this unification is cleaner. Changes to the format of the Logger can still be made, they are just stored under optimizer.logger.whateveryouwant.

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 98.01%. Comparing base (626aafe) to head (ef31faf).

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #557      +/-   ##
==========================================
+ Coverage   96.43%   98.01%   +1.58%     
==========================================
  Files          12       10       -2     
  Lines        1235     1161      -74     
==========================================
- Hits         1191     1138      -53     
+ Misses         44       23      -21     

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[till-m]

I think it's generally great that we're moving on from the observer pattern, however, I think it would be better to not pass in the whole BayesianOptimization object in these steps, but pass the specific relevant information. E.g., in log_optimization_step, we should pass in the params of the point, the target value, constraint-value, if applicable, and a boolean for is_new_max. Similarly, the log_optimization_start function should accept parameter names, etc.

The only argument that I can see against this is if we want to allow people to write their own loggers, but I don't really see a usecase for this.