AgitatedDove14 , I am referring to some generic HPO scenario where you define some HP space lets say:
param1 = np.linspace(lower_bound, upper_bound, n) param2 = np.linspace(lower_bound, upper_bound, n)then you run an optimization that samples this HP space,
For each trial a sample is pulled from the space, some experiment is performed and you get a score. Then to analyze the behavior of your objective you want to understand the relation between the params and objective score.
Then if you pull the trials metrics, you most likely want to know to which HP they belong.
So the bottom line is that when pulling results you are interested in the metrics values + HP point (param1=values, param2=values, ...) of the trial