mlr_acqfunctions_stochastic_cb {mlr3mbo} | R Documentation |
Acquisition Function Stochastic Confidence Bound
Description
Lower / Upper Confidence Bound with lambda sampling and decay.
The initial \lambda
is drawn from an uniform distribution between min_lambda
and max_lambda
or from an exponential distribution with rate 1 / lambda
.
\lambda
is updated after each update by the formula lambda * exp(-rate * (t %% period))
, where t
is the number of times the acquisition function has been updated.
While this acquisition function usually would be used within an asynchronous optimizer, e.g., OptimizerAsyncMbo, it can in principle also be used in synchronous optimizers, e.g., OptimizerMbo.
Dictionary
This AcqFunction can be instantiated via the dictionary
mlr_acqfunctions or with the associated sugar function acqf()
:
mlr_acqfunctions$get("stochastic_cb") acqf("stochastic_cb")
Parameters
-
"lambda"
(numeric(1)
)
\lambda
value for sampling from the exponential distribution. Defaults to1.96
. -
"min_lambda"
(numeric(1)
)
Minimum value of\lambda
for sampling from the uniform distribution. Defaults to0.01
. -
"max_lambda"
(numeric(1)
)
Maximum value of\lambda
for sampling from the uniform distribution. Defaults to10
. -
"distribution"
(character(1)
)
Distribution to sample\lambda
from. One ofc("uniform", "exponential")
. Defaults touniform
. -
"rate"
(numeric(1)
)
Rate of the exponential decay. Defaults to0
i.e. no decay. -
"period"
(integer(1)
)
Period of the exponential decay. Defaults toNULL
, i.e., the decay has no period.
Note
This acquisition function always also returns its current (
acq_lambda
) and original (acq_lambda_0
)\lambda
. These values will be logged into the bbotk::ArchiveBatch of the bbotk::OptimInstanceBatch of the AcqOptimizer and therefore also in the bbotk::Archive of the actual bbotk::OptimInstance that is to be optimized.
Super classes
bbotk::Objective
-> mlr3mbo::AcqFunction
-> AcqFunctionStochasticCB
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
AcqFunctionStochasticCB$new( surrogate = NULL, lambda = 1.96, min_lambda = 0.01, max_lambda = 10, distribution = "uniform", rate = 0, period = NULL )
Arguments
surrogate
(
NULL
| SurrogateLearner).lambda
(
numeric(1)
).min_lambda
(
numeric(1)
).max_lambda
(
numeric(1)
).distribution
(
character(1)
).rate
(
numeric(1)
).period
(
NULL
|integer(1)
).
Method update()
Update the acquisition function. Samples and decays lambda.
Usage
AcqFunctionStochasticCB$update()
Method reset()
Reset the acquisition function.
Resets the private update counter .t
used within the epsilon decay.
Usage
AcqFunctionStochasticCB$reset()
Method clone()
The objects of this class are cloneable with this method.
Usage
AcqFunctionStochasticCB$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
References
Snoek, Jasper, Larochelle, Hugo, Adams, P R (2012). “Practical Bayesian Optimization of Machine Learning Algorithms.” In Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds.), Advances in Neural Information Processing Systems, volume 25, 2951–2959.
EgelĂ©, Romain, Guyon, Isabelle, Vishwanath, Venkatram, Balaprakash, Prasanna (2023). “Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization.” In 2023 IEEE 19th International Conference on e-Science (e-Science), 1–10.
See Also
Other Acquisition Function:
AcqFunction
,
mlr_acqfunctions
,
mlr_acqfunctions_aei
,
mlr_acqfunctions_cb
,
mlr_acqfunctions_ehvi
,
mlr_acqfunctions_ehvigh
,
mlr_acqfunctions_ei
,
mlr_acqfunctions_ei_log
,
mlr_acqfunctions_eips
,
mlr_acqfunctions_mean
,
mlr_acqfunctions_multi
,
mlr_acqfunctions_pi
,
mlr_acqfunctions_sd
,
mlr_acqfunctions_smsego
,
mlr_acqfunctions_stochastic_ei
Examples
if (requireNamespace("mlr3learners") &
requireNamespace("DiceKriging") &
requireNamespace("rgenoud")) {
library(bbotk)
library(paradox)
library(mlr3learners)
library(data.table)
fun = function(xs) {
list(y = xs$x ^ 2)
}
domain = ps(x = p_dbl(lower = -10, upper = 10))
codomain = ps(y = p_dbl(tags = "minimize"))
objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
instance = OptimInstanceBatchSingleCrit$new(
objective = objective,
terminator = trm("evals", n_evals = 5))
instance$eval_batch(data.table(x = c(-6, -5, 3, 9)))
learner = default_gp()
surrogate = srlrn(learner, archive = instance$archive)
acq_function = acqf("stochastic_cb", surrogate = surrogate, lambda = 3)
acq_function$surrogate$update()
acq_function$update()
acq_function$eval_dt(data.table(x = c(-1, 0, 1)))
}