Class-based Oversampling technique

Target

Implement a class-based Oversampling technique. Classes are defined by fixed ppb intervalls and the Oversampling then (fully) balances the frequency of the classes. The method is added in pre-processing.

Tasks

• add method apply_oversampling in PreProcessing
• store results of apply_oversampling in data store
• make all hardcoded parameters (e.g. bins or rates_cap) more flexible
  • add parameter to experiment setup (init, run)
  • load information from data store within apply_oversampling by using data_store.get_default(...)
  • defaults could be either set in the experiment setup (by using the defaults file) or just in the get_default call

The following steps are not specified currently: DataHandler should be able to use the oversampling information