Open rmrmg opened 9 months ago
Hi @rmrmg, for your questions:
advisor_type:
for BO you need to define surrogate model
surrogate_type='auto'
to automatically choose a proper model for the optimization.for BO you need to defined acq_type, EI is the most common choice for single objective optimization.
acq_optimizer_type. Set to 'random_scipy' if the space are full of continuous parameters (int and float). It cost more time but performs better. Otherwise set to 'local_random'. You can also set 'auto'.
init_strategy; the strategy applies only for initial_runs (default=3) number of steps. It controls the generation of initial configs. After the initial steps, the optimization uses BO to generate new config. It does not control the long (300+ steps) optimization process. However, the overall optimization result is to some extent based on the selection of initial points.
There is very little information in documentation so based on source code it seems to me there are such option to control optimization process (all from https://github.com/PKU-DAIR/open-box/blob/96841e65a7e9900e5549d5f68411db35500f2e92/openbox/optimizer/generic_smbo.py):
advisor_type - basically define how now points for testing are proposed. There are such option:
for BO you need to define surrogate model
for BO you need to defined acq_type, it is algorithm for new point proposal (which is tradeoff between exploration and exploitation). Among available option 'ei' (default) and 'pi' are well know and very popular with try to balance between exploration and exploitation, 'lbc' will promotes exploration, id est you will find shape of your space but not necessary best solution in the space.
acq_optimizer_type I dont know what it is and when it is applicable
init_strategy; is the strategy apply only for initial_runs (default=3) number of steps? Is this affect long (300+ steps) optimization process? @open-box-team could you please coment and answer to questions?