(runs)
return([lc,])
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.
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.
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 .
def realtime_learning_curves(runs):
	"""
	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.
	
	"""
	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,])
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 , ] )