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R/OptimizerMbo.R
In mlr3mbo: Flexible Bayesian Optimization
#' @title Model Based Optimization
#'
#' @name mlr_optimizers_mbo
#'
#' @description
#' `OptimizerMbo` class that implements Model Based Optimization (MBO).
#' The implementation follows a modular layout relying on a [loop_function] determining the MBO flavor to be used, e.g.,
#' [bayesopt_ego] for sequential single-objective Bayesian Optimization, a [Surrogate], an [AcqFunction], e.g., [mlr_acqfunctions_ei] for
#' Expected Improvement and an [AcqOptimizer].
#'
#' MBO algorithms are iterative optimization algorithms that make use of a continuously updated surrogate model built for the objective function.
#' By optimizing a comparably cheap to evaluate acquisition function defined on the surrogate prediction, the next candidate is chosen for evaluation.
#'
#' Detailed descriptions of different MBO flavors are provided in the documentation of the respective [loop_function].
#'
#' Termination is handled via a [bbotk::Terminator] part of the [bbotk::OptimInstance] to be optimized.
#'
#' Note that in general the [Surrogate] is updated one final time on all available data after the optimization process has terminated.
#' However, in certain scenarios this is not always possible or meaningful, e.g., when using [bayesopt_parego()] for multi-objective optimization
#' which uses a surrogate that relies on a scalarization of the objectives.
#' It is therefore recommended to manually inspect the [Surrogate] after optimization if it is to be used, e.g., for visualization purposes to make
#' sure that it has been properly updated on all available data.
#' If this final update of the [Surrogate] could not be performed successfully, a warning will be logged.
#'
#' @section Archive:
#' The [bbotk::Archive] holds the following additional columns that are specific to MBO algorithms:
#' * `[acq_function$id]` (`numeric(1)`)\cr
#' The value of the acquisition function.
#' * `.already_evaluated` (`logical(1))`\cr
#' Whether this point was already evaluated. Depends on the `skip_already_evaluated` parameter of the [AcqOptimizer].
#' @export
#' @examples
#' \donttest{
#' if (requireNamespace("mlr3learners") &
#' requireNamespace("DiceKriging") &
#' requireNamespace("rgenoud")) {
#'
#' library(bbotk)
#' library(paradox)
#' library(mlr3learners)
#'
#' # single-objective EGO
#' fun = function(xs) {
#' list(y = xs$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))
#'
#' surrogate = default_surrogate(instance)
#'
#' acq_function = acqf("ei")
#'
#' acq_optimizer = acqo(
#' optimizer = opt("random_search", batch_size = 100),
#' terminator = trm("evals", n_evals = 100))
#'
#' optimizer = opt("mbo",
#' loop_function = bayesopt_ego,
#' surrogate = surrogate,
#' acq_function = acq_function,
#' acq_optimizer = acq_optimizer)
#'
#' optimizer$optimize(instance)
#'
#' # multi-objective ParEGO
#' fun = function(xs) {
#' list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2)
#' }
#' domain = ps(x = p_dbl(lower = -10, upper = 10))
#' codomain = ps(y1 = p_dbl(tags = "minimize"), y2 = p_dbl(tags = "minimize"))
#' objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
#'
#' instance = OptimInstanceBatchMultiCrit$new(
#' objective = objective,
#' terminator = trm("evals", n_evals = 5))
#'
#' optimizer = opt("mbo",
#' loop_function = bayesopt_parego,
#' surrogate = surrogate,
#' acq_function = acq_function,
#' acq_optimizer = acq_optimizer)
#'
#' optimizer$optimize(instance)
#' }
#' }
OptimizerMbo = R6Class("OptimizerMbo",
inherit = bbotk::OptimizerBatch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' If `surrogate` is `NULL` and the `acq_function$surrogate` field is populated, this [Surrogate] is used.
#' Otherwise, `default_surrogate(instance)` is used.
#' If `acq_function` is NULL and the `acq_optimizer$acq_function` field is populated, this [AcqFunction] is used (and therefore its `$surrogate` if populated; see above).
#' Otherwise `default_acqfunction(instance)` is used.
#' If `acq_optimizer` is NULL, `default_acqoptimizer(instance)` is used.
#'
#' Even if already initialized, the `surrogate$archive` field will always be overwritten by the [bbotk::Archive] of the current [bbotk::OptimInstance] to be optimized.
#'
#' For more information on default values for `loop_function`, `surrogate`, `acq_function` and `acq_optimizer`, see `?mbo_defaults`.
#'
#' @template param_loop_function
#' @template param_surrogate
#' @template param_acq_function
#' @template param_acq_optimizer
#' @template param_args
#' @template param_result_assigner
initialize = function(loop_function = NULL, surrogate = NULL, acq_function = NULL, acq_optimizer = NULL, args = NULL, result_assigner = NULL) {
param_set = ParamSet$new()
super$initialize("mbo",
param_set = param_set,
param_classes = c("ParamLgl", "ParamInt", "ParamDbl", "ParamFct"), # is replaced with dynamic AB after construction
properties = c("dependencies", "multi-crit", "single-crit"), # is replaced with dynamic AB after construction
packages = "mlr3mbo", # is replaced with dynamic AB after construction
label = "Model Based Optimization",
man = "mlr3mbo::OptimizerMbo")
self$loop_function = assert_loop_function(loop_function)
self$surrogate = assert_r6(surrogate, classes = "Surrogate", null.ok = TRUE)
self$acq_function = assert_r6(acq_function, classes = "AcqFunction", null.ok = TRUE)
self$acq_optimizer = assert_r6(acq_optimizer, classes = "AcqOptimizer", null.ok = TRUE)
assert_list(args, names = "named", null.ok = TRUE)
if (!is.null(self$loop_function)) {
assert_subset(names(args), choices = setdiff(names(formals(self$loop_function)), c("instance", "surrogate", "acq_function", "acq_optimizer")), empty.ok = TRUE)
}
self$args = args
self$result_assigner = assert_r6(result_assigner, classes = "ResultAssigner", null.ok = TRUE)
},
#' @description
#' Print method.
#'
#' @return (`character()`).
print = function() {
catn(format(self), if (is.na(self$label)) "" else paste0(": ", self$label))
#catn(str_indent("* Parameters:", as_short_string(self$param_set$values)))
catn(str_indent("* Parameter classes:", self$param_classes))
catn(str_indent("* Properties:", self$properties))
catn(str_indent("* Packages:", self$packages))
catn(str_indent("* Loop function:", if (is.null(self$loop_function)) "-" else attr(self$loop_function, "id")))
catn(str_indent("* Surrogate:", if (is.null(self$surrogate)) "-" else self$surrogate$print_id))
catn(str_indent("* Acquisition Function:", if (is.null(self$acq_function)) "-" else class(self$acq_function)[1L]))
catn(str_indent("* Acquisition Function Optimizer:", if (is.null(self$acq_optimizer)) "-" else self$acq_optimizer$print_id))
},
#' @description
#' Reset the optimizer.
#' Sets the following fields to `NULL`:
#' `loop_function`, `surrogate`, `acq_function`, `acq_optimizer`, `args`, `result_assigner`
reset = function() {
private$.loop_function = NULL
private$.surrogate = NULL
private$.acq_function = NULL
private$.acq_optimizer = NULL
private$.args = NULL
private$.result_assigner = NULL
}
),
active = list(
#' @template field_loop_function
loop_function = function(rhs) {
if (missing(rhs)) {
private$.loop_function
} else {
private$.loop_function = assert_loop_function(rhs)
}
},
#' @template field_surrogate
surrogate = function(rhs) {
if (missing(rhs)) {
private$.surrogate
} else {
private$.surrogate = assert_r6(rhs, classes = "Surrogate", null.ok = TRUE)
}
},
#' @template field_acq_function
acq_function = function(rhs) {
if (missing(rhs)) {
private$.acq_function
} else {
private$.acq_function = assert_r6(rhs, classes = "AcqFunction", null.ok = TRUE)
}
},
#' @template field_acq_optimizer
acq_optimizer = function(rhs) {
if (missing(rhs)) {
private$.acq_optimizer
} else {
private$.acq_optimizer = assert_r6(rhs, classes = "AcqOptimizer", null.ok = TRUE)
}
},
#' @template field_args
args = function(rhs) {
if (missing(rhs)) {
if (!is.null(self$loop_function)) {
assert_subset(names(private$.args), choices = setdiff(names(formals(self$loop_function)), c("instance", "surrogate", "acq_function", "acq_optimizer")), empty.ok = TRUE) # args could have been set prior to a loop_function
}
private$.args
} else {
assert_list(rhs, names = "named", null.ok = TRUE)
if (!is.null(self$loop_function)) {
assert_subset(names(rhs), choices = setdiff(names(formals(self$loop_function)), c("instance", "surrogate", "acq_function", "acq_optimizer")), empty.ok = TRUE)
}
private$.args = rhs
}
},
#' @template field_result_assigner
result_assigner = function(rhs) {
if (missing(rhs)) {
private$.result_assigner
} else {
private$.result_assigner = assert_r6(rhs, classes = "ResultAssigner", null.ok = TRUE)
}
},
#' @template field_param_classes
param_classes = function(rhs) {
if (missing(rhs)) {
param_classes_surrogate = c("logical" = "ParamLgl", "integer" = "ParamInt", "numeric" = "ParamDbl", "factor" = "ParamFct")
if (!is.null(self$surrogate)) {
param_classes_surrogate = param_classes_surrogate[c("logical", "integer", "numeric", "factor") %in% self$surrogate$feature_types] # surrogate has precedence over acq_function$surrogate
}
param_classes_acq_opt = if (!is.null(self$acq_optimizer)) {
self$acq_optimizer$optimizer$param_classes
} else {
c("ParamLgl", "ParamInt", "ParamDbl", "ParamFct")
}
unname(intersect(param_classes_surrogate, param_classes_acq_opt))
} else {
stop("$param_classes is read-only.")
}
},
#' @template field_properties
properties = function(rhs) {
if (missing(rhs)) {
properties_loop_function = c("single-crit", "multi-crit")
if (!is.null(self$loop_function)) {
properties_loop_function = intersect(properties_loop_function, attr(self$loop_function, "instance"))
}
properties_surrogate = "dependencies"
if (!is.null(self$surrogate)) {
if ("missings" %nin% self$surrogate$properties) {
properties_surrogate = character()
}
}
unname(c(properties_surrogate, properties_loop_function))
} else {
stop("$properties is read-only.")
}
},
#' @template field_packages
packages = function(rhs) {
if (missing(rhs)) {
union("mlr3mbo", c(self$acq_function$packages, self$surrogate$packages, self$acq_optimizer$optimizer$packages, self$result_assigner$packages))
} else {
stop("$packages is read-only.")
}
}
),
private = list(
.loop_function = NULL,
.surrogate = NULL,
.acq_function = NULL,
.acq_optimizer = NULL,
.args = NULL,
.result_assigner = NULL,
.optimize = function(inst) {
# FIXME: this needs more checks for edge cases like eips or loop_function bayesopt_parego then default_surrogate should use one learner
if (is.null(self$loop_function)) {
self$loop_function = default_loop_function(inst)
}
if (is.null(self$acq_function)) { # acq_optimizer$acq_function has precedence
self$acq_function = self$acq_optimizer$acq_function %??% default_acqfunction(inst)
}
if (is.null(self$surrogate)) { # acq_function$surrogate has precedence
self$surrogate = self$acq_function$surrogate %??% default_surrogate(inst)
}
if (is.null(self$acq_optimizer)) {
self$acq_optimizer = default_acqoptimizer(self$acq_function)
}
if (is.null(self$result_assigner)) {
self$result_assigner = default_result_assigner(inst)
}
self$surrogate$archive = inst$archive
self$acq_function$surrogate = self$surrogate
self$acq_optimizer$acq_function = self$acq_function
# FIXME: if result_assigner is for example ResultAssignerSurrogate the surrogate won't be set automatically
check_packages_installed(self$packages, msg = sprintf("Package '%%s' required but not installed for Optimizer '%s'", format(self)))
invoke(self$loop_function, instance = inst, surrogate = self$surrogate, acq_function = self$acq_function, acq_optimizer = self$acq_optimizer, .args = self$args)
on.exit({
tryCatch(
{
self$surrogate$update()
}, surrogate_update_error = function(error_condition) {
logger = lgr::get_logger("bbotk")
logger$warn("Could not update the surrogate a final time after the optimization process has terminated.")
}
)
})
},
.assign_result = function(inst) {
self$result_assigner$assign_result(inst)
}
)
)
#' @include aaa.R
optimizers[["mbo"]] = OptimizerMbo
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#' @title Model Based Optimization
#'
#' @name mlr_optimizers_mbo
#'
#' @description
#' `OptimizerMbo` class that implements Model Based Optimization (MBO).
#' The implementation follows a modular layout relying on a [loop_function] determining the MBO flavor to be used, e.g.,
#' [bayesopt_ego] for sequential single-objective Bayesian Optimization, a [Surrogate], an [AcqFunction], e.g., [mlr_acqfunctions_ei] for
#' Expected Improvement and an [AcqOptimizer].
#'
#' MBO algorithms are iterative optimization algorithms that make use of a continuously updated surrogate model built for the objective function.
#' By optimizing a comparably cheap to evaluate acquisition function defined on the surrogate prediction, the next candidate is chosen for evaluation.
#'
#' Detailed descriptions of different MBO flavors are provided in the documentation of the respective [loop_function].
#'
#' Termination is handled via a [bbotk::Terminator] part of the [bbotk::OptimInstance] to be optimized.
#'
#' Note that in general the [Surrogate] is updated one final time on all available data after the optimization process has terminated.
#' However, in certain scenarios this is not always possible or meaningful, e.g., when using [bayesopt_parego()] for multi-objective optimization
#' which uses a surrogate that relies on a scalarization of the objectives.
#' It is therefore recommended to manually inspect the [Surrogate] after optimization if it is to be used, e.g., for visualization purposes to make
#' sure that it has been properly updated on all available data.
#' If this final update of the [Surrogate] could not be performed successfully, a warning will be logged.
#'
#' @section Archive:
#' The [bbotk::Archive] holds the following additional columns that are specific to MBO algorithms:
#' * `[acq_function$id]` (`numeric(1)`)\cr
#' The value of the acquisition function.
#' * `.already_evaluated` (`logical(1))`\cr
#' Whether this point was already evaluated. Depends on the `skip_already_evaluated` parameter of the [AcqOptimizer].
#' @export
#' @examples
#' \donttest{
#' if (requireNamespace("mlr3learners") &
#' requireNamespace("DiceKriging") &
#' requireNamespace("rgenoud")) {
#'
#' library(bbotk)
#' library(paradox)
#' library(mlr3learners)
#'
#' # single-objective EGO
#' fun = function(xs) {
#' list(y = xs$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))
#'
#' surrogate = default_surrogate(instance)
#'
#' acq_function = acqf("ei")
#'
#' acq_optimizer = acqo(
#' optimizer = opt("random_search", batch_size = 100),
#' terminator = trm("evals", n_evals = 100))
#'
#' optimizer = opt("mbo",
#' loop_function = bayesopt_ego,
#' surrogate = surrogate,
#' acq_function = acq_function,
#' acq_optimizer = acq_optimizer)
#'
#' optimizer$optimize(instance)
#'
#' # multi-objective ParEGO
#' fun = function(xs) {
#' list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2)
#' }
#' domain = ps(x = p_dbl(lower = -10, upper = 10))
#' codomain = ps(y1 = p_dbl(tags = "minimize"), y2 = p_dbl(tags = "minimize"))
#' objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
#'
#' instance = OptimInstanceBatchMultiCrit$new(
#' objective = objective,
#' terminator = trm("evals", n_evals = 5))
#'
#' optimizer = opt("mbo",
#' loop_function = bayesopt_parego,
#' surrogate = surrogate,
#' acq_function = acq_function,
#' acq_optimizer = acq_optimizer)
#'
#' optimizer$optimize(instance)
#' }
#' }
OptimizerMbo = R6Class("OptimizerMbo",
inherit = bbotk::OptimizerBatch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' If `surrogate` is `NULL` and the `acq_function$surrogate` field is populated, this [Surrogate] is used.
#' Otherwise, `default_surrogate(instance)` is used.
#' If `acq_function` is NULL and the `acq_optimizer$acq_function` field is populated, this [AcqFunction] is used (and therefore its `$surrogate` if populated; see above).
#' Otherwise `default_acqfunction(instance)` is used.
#' If `acq_optimizer` is NULL, `default_acqoptimizer(instance)` is used.
#'
#' Even if already initialized, the `surrogate$archive` field will always be overwritten by the [bbotk::Archive] of the current [bbotk::OptimInstance] to be optimized.
#'
#' For more information on default values for `loop_function`, `surrogate`, `acq_function` and `acq_optimizer`, see `?mbo_defaults`.
#'
#' @template param_loop_function
#' @template param_surrogate
#' @template param_acq_function
#' @template param_acq_optimizer
#' @template param_args
#' @template param_result_assigner
initialize = function(loop_function = NULL, surrogate = NULL, acq_function = NULL, acq_optimizer = NULL, args = NULL, result_assigner = NULL) {
param_set = ParamSet$new()
super$initialize("mbo",
param_set = param_set,
param_classes = c("ParamLgl", "ParamInt", "ParamDbl", "ParamFct"), # is replaced with dynamic AB after construction
properties = c("dependencies", "multi-crit", "single-crit"), # is replaced with dynamic AB after construction
packages = "mlr3mbo", # is replaced with dynamic AB after construction
label = "Model Based Optimization",
man = "mlr3mbo::OptimizerMbo")
self$loop_function = assert_loop_function(loop_function)
self$surrogate = assert_r6(surrogate, classes = "Surrogate", null.ok = TRUE)
self$acq_function = assert_r6(acq_function, classes = "AcqFunction", null.ok = TRUE)
self$acq_optimizer = assert_r6(acq_optimizer, classes = "AcqOptimizer", null.ok = TRUE)
assert_list(args, names = "named", null.ok = TRUE)
if (!is.null(self$loop_function)) {
assert_subset(names(args), choices = setdiff(names(formals(self$loop_function)), c("instance", "surrogate", "acq_function", "acq_optimizer")), empty.ok = TRUE)
}
self$args = args
self$result_assigner = assert_r6(result_assigner, classes = "ResultAssigner", null.ok = TRUE)
},
#' @description
#' Print method.
#'
#' @return (`character()`).
print = function() {
catn(format(self), if (is.na(self$label)) "" else paste0(": ", self$label))
#catn(str_indent("* Parameters:", as_short_string(self$param_set$values)))
catn(str_indent("* Parameter classes:", self$param_classes))
catn(str_indent("* Properties:", self$properties))
catn(str_indent("* Packages:", self$packages))
catn(str_indent("* Loop function:", if (is.null(self$loop_function)) "-" else attr(self$loop_function, "id")))
catn(str_indent("* Surrogate:", if (is.null(self$surrogate)) "-" else self$surrogate$print_id))
catn(str_indent("* Acquisition Function:", if (is.null(self$acq_function)) "-" else class(self$acq_function)[1L]))
catn(str_indent("* Acquisition Function Optimizer:", if (is.null(self$acq_optimizer)) "-" else self$acq_optimizer$print_id))
},
#' @description
#' Reset the optimizer.
#' Sets the following fields to `NULL`:
#' `loop_function`, `surrogate`, `acq_function`, `acq_optimizer`, `args`, `result_assigner`
reset = function() {
private$.loop_function = NULL
private$.surrogate = NULL
private$.acq_function = NULL
private$.acq_optimizer = NULL
private$.args = NULL
private$.result_assigner = NULL
}
),
active = list(
#' @template field_loop_function
loop_function = function(rhs) {
if (missing(rhs)) {
private$.loop_function
} else {
private$.loop_function = assert_loop_function(rhs)
}
},
#' @template field_surrogate
surrogate = function(rhs) {
if (missing(rhs)) {
private$.surrogate
} else {
private$.surrogate = assert_r6(rhs, classes = "Surrogate", null.ok = TRUE)
}
},
#' @template field_acq_function
acq_function = function(rhs) {
if (missing(rhs)) {
private$.acq_function
} else {
private$.acq_function = assert_r6(rhs, classes = "AcqFunction", null.ok = TRUE)
}
},
#' @template field_acq_optimizer
acq_optimizer = function(rhs) {
if (missing(rhs)) {
private$.acq_optimizer
} else {
private$.acq_optimizer = assert_r6(rhs, classes = "AcqOptimizer", null.ok = TRUE)
}
},
#' @template field_args
args = function(rhs) {
if (missing(rhs)) {
if (!is.null(self$loop_function)) {
assert_subset(names(private$.args), choices = setdiff(names(formals(self$loop_function)), c("instance", "surrogate", "acq_function", "acq_optimizer")), empty.ok = TRUE) # args could have been set prior to a loop_function
}
private$.args
} else {
assert_list(rhs, names = "named", null.ok = TRUE)
if (!is.null(self$loop_function)) {
assert_subset(names(rhs), choices = setdiff(names(formals(self$loop_function)), c("instance", "surrogate", "acq_function", "acq_optimizer")), empty.ok = TRUE)
}
private$.args = rhs
}
},
#' @template field_result_assigner
result_assigner = function(rhs) {
if (missing(rhs)) {
private$.result_assigner
} else {
private$.result_assigner = assert_r6(rhs, classes = "ResultAssigner", null.ok = TRUE)
}
},
#' @template field_param_classes
param_classes = function(rhs) {
if (missing(rhs)) {
param_classes_surrogate = c("logical" = "ParamLgl", "integer" = "ParamInt", "numeric" = "ParamDbl", "factor" = "ParamFct")
if (!is.null(self$surrogate)) {
param_classes_surrogate = param_classes_surrogate[c("logical", "integer", "numeric", "factor") %in% self$surrogate$feature_types] # surrogate has precedence over acq_function$surrogate
}
param_classes_acq_opt = if (!is.null(self$acq_optimizer)) {
self$acq_optimizer$optimizer$param_classes
} else {
c("ParamLgl", "ParamInt", "ParamDbl", "ParamFct")
}
unname(intersect(param_classes_surrogate, param_classes_acq_opt))
} else {
stop("$param_classes is read-only.")
}
},
#' @template field_properties
properties = function(rhs) {
if (missing(rhs)) {
properties_loop_function = c("single-crit", "multi-crit")
if (!is.null(self$loop_function)) {
properties_loop_function = intersect(properties_loop_function, attr(self$loop_function, "instance"))
}
properties_surrogate = "dependencies"
if (!is.null(self$surrogate)) {
if ("missings" %nin% self$surrogate$properties) {
properties_surrogate = character()
}
}
unname(c(properties_surrogate, properties_loop_function))
} else {
stop("$properties is read-only.")
}
},
#' @template field_packages
packages = function(rhs) {
if (missing(rhs)) {
union("mlr3mbo", c(self$acq_function$packages, self$surrogate$packages, self$acq_optimizer$optimizer$packages, self$result_assigner$packages))
} else {
stop("$packages is read-only.")
}
}
),
private = list(
.loop_function = NULL,
.surrogate = NULL,
.acq_function = NULL,
.acq_optimizer = NULL,
.args = NULL,
.result_assigner = NULL,
.optimize = function(inst) {
# FIXME: this needs more checks for edge cases like eips or loop_function bayesopt_parego then default_surrogate should use one learner
if (is.null(self$loop_function)) {
self$loop_function = default_loop_function(inst)
}
if (is.null(self$acq_function)) { # acq_optimizer$acq_function has precedence
self$acq_function = self$acq_optimizer$acq_function %??% default_acqfunction(inst)
}
if (is.null(self$surrogate)) { # acq_function$surrogate has precedence
self$surrogate = self$acq_function$surrogate %??% default_surrogate(inst)
}
if (is.null(self$acq_optimizer)) {
self$acq_optimizer = default_acqoptimizer(self$acq_function)
}
if (is.null(self$result_assigner)) {
self$result_assigner = default_result_assigner(inst)
}
self$surrogate$archive = inst$archive
self$acq_function$surrogate = self$surrogate
self$acq_optimizer$acq_function = self$acq_function
# FIXME: if result_assigner is for example ResultAssignerSurrogate the surrogate won't be set automatically
check_packages_installed(self$packages, msg = sprintf("Package '%%s' required but not installed for Optimizer '%s'", format(self)))
invoke(self$loop_function, instance = inst, surrogate = self$surrogate, acq_function = self$acq_function, acq_optimizer = self$acq_optimizer, .args = self$args)
on.exit({
tryCatch(
{
self$surrogate$update()
}, surrogate_update_error = function(error_condition) {
logger = lgr::get_logger("bbotk")
logger$warn("Could not update the surrogate a final time after the optimization process has terminated.")
}
)
})
},
.assign_result = function(inst) {
self$result_assigner$assign_result(inst)
}
)
)
#' @include aaa.R
optimizers[["mbo"]] = OptimizerMbo
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