syne_tune.optimizer.schedulers.searchers.hypertune.hypertune_searcher module
- class syne_tune.optimizer.schedulers.searchers.hypertune.hypertune_searcher.HyperTuneSearcher(config_space, **kwargs)[source]
Bases:
GPMultiFidelitySearcher
Implements Hyper-Tune as extension of
GPMultiFidelitySearcher
, seeHyperTuneIndependentGPModel
for references. Two modifications:New brackets are sampled from a model-based distribution \([w_k]\)
The acquisition function is fed with predictive means and variances from a mixture over rung level distributions, weighted by \([ heta_k]\)
It is not recommended to create
HyperTuneSearcher
searcher objects directly, but rather to createHyperbandScheduler
objects withsearcher="hypertune"
, and passing arguments here insearch_options
. This will use the appropriate functions from :mod:syne_tune.optimizer.schedulers.searchers.gp_searcher_factory
to create components in a consistent way.The following arguments of the parent class are not relevant here, and are ignored:
gp_resource_kernel
,resource_acq
,issm_gamma_one
,expdecay_normalize_inputs
.Additional arguments on top of parent class
GPMultiFidelitySearcher
:- Parameters:
model (str, optional) –
Selects surrogate model (learning curve model) to be used. Choices are:
”gp_multitask”: GP multi-task surrogate model
”gp_independent” (default): Independent GPs for each rung level, sharing an ARD kernel
The default is “gp_independent” (as in the Hyper-Tune paper), which is different to the default in
GPMultiFidelitySearcher
(which is “gp_multitask”). “gp_issm”, “gp_expdecay” not supported here.hypertune_distribution_num_samples (int, optional) – Parameter for estimating the distribution, given by \([ heta_k]\). Defaults to 50