syne_tune.optimizer.schedulers.searchers.cost_aware.cost_aware_gp_multifidelity_searcher module
- class syne_tune.optimizer.schedulers.searchers.cost_aware.cost_aware_gp_multifidelity_searcher.MultiModelGPMultiFidelitySearcher(config_space, metric, points_to_evaluate=None, **kwargs)[source]
Bases:
GPMultiFidelitySearcher
Superclass for multi-model extensions of
GPMultiFidelitySearcher
. We first call_create_internal()
passing factory andskip_optimization
predicate for theINTERNAL_METRIC_NAME
model, then replace the state transformer by a multi-model one.
- class syne_tune.optimizer.schedulers.searchers.cost_aware.cost_aware_gp_multifidelity_searcher.CostAwareGPMultiFidelitySearcher(config_space, metric, points_to_evaluate=None, **kwargs)[source]
Bases:
MultiModelGPMultiFidelitySearcher
Gaussian process-based cost-aware multi-fidelity hyperparameter optimization (to be used with
HyperbandScheduler
). The searcher requires a cost metric, which is given bycost_attr
.The acquisition function used here is the same as in
GPMultiFidelitySearcher
, but expected improvement (EI) is replaced by EIpu (seeEIpuAcquisitionFunction
).Cost values are read from each report and cost is modeled as \(c(x, r)\), the cost model being given by
kwargs["cost_model"]
.Additional arguments on top of parent class
GPMultiFidelitySearcher
:- Parameters:
cost_attr (str) – Mandatory. Name of cost attribute in data obtained from reporter (e.g., elapsed training time). Depending on whether
resource_attr
is given, cost values are read from each report or only at the end.resource_attr (str) – Name of resource attribute in reports. Cost values are read from each report and cost is modeled as \(c(x, r)\), the cost model being given by
cost_model
.cost_model (
CostModel
, optional) – Model for \(c(x, r)\)
- clone_from_state(state)[source]
Together with
get_state()
, this is needed in order to store and re-create the mutable state of the searcher.Given state as returned by
get_state()
, this method combines the non-pickle-able part of the immutable state from self with state and returns the corresponding searcher clone. Afterwards,self
is not used anymore.- Parameters:
state – See above
- Returns:
New searcher object