code: def realtime_learning_curves(runs):
  	
  	sr = sorted(runs, key=lambda r: r.budget)
  	lc = list(filter(lambda t: not t[1] is None, [(r.time_stamps['finished'], r.info['test accuracy']) for r in sr]))
  	return([lc,])
docs: example how to extract a different kind of learning curve.
  	
  	The x values are now the time the runs finished, not the budget anymore.
  	We no longer plot the validation loss on the y axis, but now the test accuracy.
  	
  	This is just to show how to get different information into the interactive plot.