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What's the difference between normalize_Y=True and exact_feval=False? #350

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lewisscola opened this issue Nov 15, 2020 · 1 comment
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@lewisscola
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Hi,
I don't know the difference between normalize_Y and exact_feval in GpyOpt.
I see the documention like:
image
image
Is it means that we only have to set one of the parameters? (for example, if we choose normalize_Y=True, we don't have to set exact_feval=False) Could you help me explain it?
Thank you!

@astroHaoPeng
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Base on my understanding, they are two different things:

normalize_Y means it will normalize the Y data before doing optimization. But user will always get dimensional output Y. Unless you debug into the detailed optimization processes, you will not interactive with the normalized Y.

Exact_feval means if your evaluation of the objective function f is exact. For example,

  • if f_math has a know mathematical formula, usually extact_feval=True unless you contaminate the evaluation of f.
  • if f_measure is some a measurement of some physical quantify like temperature, you should gain inexact f_measure and thus extact_feval=False, unless you want to treat the measurement as truth and assume no noise in it.

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