ArchiveAsync: Rush Data Storage
In bbotk: Black-Box Optimization Toolkit
ArchiveAsync R Documentation
Rush Data Storage
Description
The ArchiveAsync stores all evaluated points and performance scores in a rush::Rush data base.
S3 Methods
-
as.data.table(archive)
ArchiveAsync -> data.table::data.table()
Returns a tabular view of all performed function calls of the Objective.
The x_domain column is unnested to separate columns.
Super class
bbotk::Archive -> ArchiveAsync
Public fields
rush(Rush)
Rush controller for parallel optimization.
Active bindings
data(data.table::data.table)
Data table with all finished points.
queued_data(data.table::data.table)
Data table with all queued points.
running_data(data.table::data.table)
Data table with all running points.
finished_data(data.table::data.table)
Data table with all finished points.
failed_data(data.table::data.table)
Data table with all failed points.
n_queued(integer(1))
Number of queued points.
n_running(integer(1))
Number of running points.
n_finished(integer(1))
Number of finished points.
n_failed(integer(1))
Number of failed points.
n_evals(integer(1))
Number of evaluations stored in the archive.
Methods
Public methods
-
-
-
-
-
-
-
-
-
-
-
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
ArchiveAsync$new(search_space, codomain, check_values = FALSE, rush)
Arguments
search_space(paradox::ParamSet)
Specifies the search space for the Optimizer. The paradox::ParamSet
describes either a subset of the domain of the Objective or it describes
a set of parameters together with a trafo function that transforms values
from the search space to values of the domain. Depending on the context, this
value defaults to the domain of the objective.
codomain(paradox::ParamSet)
Specifies codomain of function.
Most importantly the tags of each output "Parameter" define whether it should
be minimized or maximized. The default is to minimize each component.
check_values(logical(1))
Should points before the evaluation and the results be checked for validity?
rush(Rush)
If a rush instance is supplied, the tuning runs without batches.
Method push_points()
Push queued points to the archive.
Usage
ArchiveAsync$push_points(xss)
Arguments
xss(list of named list())
List of named lists of point values.
Method pop_point()
Pop a point from the queue.
Usage
ArchiveAsync$pop_point()
Method push_running_point()
Push running point to the archive.
Usage
ArchiveAsync$push_running_point(xs, extra = NULL)
Arguments
xs(named list)
Named list of point values.
extra(list())
Named list of additional information.
Method push_result()
Push result to the archive.
Usage
ArchiveAsync$push_result(key, ys, x_domain, extra = NULL)
Arguments
key(character())
Key of the point.
ys(list())
Named list of results.
x_domain(list())
Named list of transformed point values.
extra(list())
Named list of additional information.
Method push_failed_point()
Push failed point to the archive.
Usage
ArchiveAsync$push_failed_point(key, message)
Arguments
key(character())
Key of the point.
message(character())
Error message.
Method data_with_state()
Fetch points with a specific state.
Usage
ArchiveAsync$data_with_state(
fields = c("xs", "ys", "xs_extra", "worker_extra", "ys_extra", "condition"),
states = c("queued", "running", "finished", "failed"),
reset_cache = FALSE
)
Arguments
fields(character())
Fields to fetch.
Defaults to c("xs", "ys", "xs_extra", "worker_extra", "ys_extra").
states(character())
States of the tasks to be fetched.
Defaults to c("queued", "running", "finished", "failed").
reset_cache(logical(1))
Whether to reset the cache of the finished points.
Method best()
Returns the best scoring evaluation(s).
For single-crit optimization, the solution that minimizes / maximizes the objective function.
For multi-crit optimization, the Pareto set / front.
Usage
ArchiveAsync$best(n_select = 1, ties_method = "first")
Arguments
n_select(integer(1L))
Amount of points to select.
Ignored for multi-crit optimization.
ties_method(character(1L))
Method to break ties when multiple points have the same score.
Either "first" (default) or "random".
Ignored for multi-crit optimization.
If n_select > 1L, the tie method is ignored and the first point is returned.
Returns
data.table::data.table()
Method nds_selection()
Calculate best points w.r.t. non dominated sorting with hypervolume contribution.
Usage
ArchiveAsync$nds_selection(n_select = 1, ref_point = NULL)
Arguments
n_select(integer(1L))
Amount of points to select.
ref_point(numeric())
Reference point for hypervolume.
Returns
data.table::data.table()
Method clear()
Clear all evaluation results from archive.
Usage
ArchiveAsync$clear()
Method clone()
The objects of this class are cloneable with this method.
Usage
ArchiveAsync$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
if (mlr3misc::require_namespaces(c("rush", "redux", "mirai"), quietly = TRUE) &&
redux::redis_available()) {
fun = function(xs) {
list(y = - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
}
# set domain
domain = ps(
x1 = p_dbl(-10, 10),
x2 = p_dbl(-5, 5)
)
# set codomain
codomain = ps(
y = p_dbl(tags = "maximize")
)
# create objective
objective = ObjectiveRFun$new(
fun = fun,
domain = domain,
codomain = codomain,
properties = "deterministic"
)
# start workers
rush::rush_plan(worker_type = "remote")
mirai::daemons(1)
# initialize instance
instance = oi_async(
objective = objective,
terminator = trm("evals", n_evals = 20)
)
# load optimizer
optimizer = opt("async_random_search")
# trigger optimization
optimizer$optimize(instance)
# all evaluated configuration
instance$archive
# best performing configuration
instance$archive$best()
# covert to data.table
as.data.table(instance$archive)
# reset the rush data base
instance$rush$reset()
}
bbotk documentation built on Nov. 5, 2025, 6:23 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| ArchiveAsync | R Documentation |
Rush Data Storage
Description
The ArchiveAsync stores all evaluated points and performance scores in a rush::Rush data base.
S3 Methods
-
as.data.table(archive)
ArchiveAsync ->data.table::data.table()
Returns a tabular view of all performed function calls of the Objective. Thex_domaincolumn is unnested to separate columns.
Super class
bbotk::Archive -> ArchiveAsync
Public fields
rush(
Rush)
Rush controller for parallel optimization.
Active bindings
data(data.table::data.table)
Data table with all finished points.queued_data(data.table::data.table)
Data table with all queued points.running_data(data.table::data.table)
Data table with all running points.finished_data(data.table::data.table)
Data table with all finished points.failed_data(data.table::data.table)
Data table with all failed points.n_queued(
integer(1))
Number of queued points.n_running(
integer(1))
Number of running points.n_finished(
integer(1))
Number of finished points.n_failed(
integer(1))
Number of failed points.n_evals(
integer(1))
Number of evaluations stored in the archive.
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
ArchiveAsync$new(search_space, codomain, check_values = FALSE, rush)
Arguments
search_space(paradox::ParamSet)
Specifies the search space for the Optimizer. The paradox::ParamSet describes either a subset of thedomainof the Objective or it describes a set of parameters together with atrafofunction that transforms values from the search space to values of the domain. Depending on the context, this value defaults to the domain of the objective.codomain(paradox::ParamSet)
Specifies codomain of function. Most importantly the tags of each output "Parameter" define whether it should be minimized or maximized. The default is to minimize each component.check_values(
logical(1))
Should points before the evaluation and the results be checked for validity?rush(
Rush)
If a rush instance is supplied, the tuning runs without batches.
Method push_points()
Push queued points to the archive.
Usage
ArchiveAsync$push_points(xss)
Arguments
xss(list of named
list())
List of named lists of point values.
Method pop_point()
Pop a point from the queue.
Usage
ArchiveAsync$pop_point()
Method push_running_point()
Push running point to the archive.
Usage
ArchiveAsync$push_running_point(xs, extra = NULL)
Arguments
xs(named
list)
Named list of point values.extra(
list())
Named list of additional information.
Method push_result()
Push result to the archive.
Usage
ArchiveAsync$push_result(key, ys, x_domain, extra = NULL)
Arguments
key(
character())
Key of the point.ys(
list())
Named list of results.x_domain(
list())
Named list of transformed point values.extra(
list())
Named list of additional information.
Method push_failed_point()
Push failed point to the archive.
Usage
ArchiveAsync$push_failed_point(key, message)
Arguments
key(
character())
Key of the point.message(
character())
Error message.
Method data_with_state()
Fetch points with a specific state.
Usage
ArchiveAsync$data_with_state(
fields = c("xs", "ys", "xs_extra", "worker_extra", "ys_extra", "condition"),
states = c("queued", "running", "finished", "failed"),
reset_cache = FALSE
)Arguments
fields(
character())
Fields to fetch. Defaults toc("xs", "ys", "xs_extra", "worker_extra", "ys_extra").states(
character())
States of the tasks to be fetched. Defaults toc("queued", "running", "finished", "failed").reset_cache(
logical(1))
Whether to reset the cache of the finished points.
Method best()
Returns the best scoring evaluation(s). For single-crit optimization, the solution that minimizes / maximizes the objective function. For multi-crit optimization, the Pareto set / front.
Usage
ArchiveAsync$best(n_select = 1, ties_method = "first")
Arguments
n_select(
integer(1L))
Amount of points to select. Ignored for multi-crit optimization.ties_method(
character(1L))
Method to break ties when multiple points have the same score. Either"first"(default) or"random". Ignored for multi-crit optimization. Ifn_select > 1L, the tie method is ignored and the first point is returned.
Returns
data.table::data.table()
Method nds_selection()
Calculate best points w.r.t. non dominated sorting with hypervolume contribution.
Usage
ArchiveAsync$nds_selection(n_select = 1, ref_point = NULL)
Arguments
n_select(
integer(1L))
Amount of points to select.ref_point(
numeric())
Reference point for hypervolume.
Returns
data.table::data.table()
Method clear()
Clear all evaluation results from archive.
Usage
ArchiveAsync$clear()
Method clone()
The objects of this class are cloneable with this method.
Usage
ArchiveAsync$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
if (mlr3misc::require_namespaces(c("rush", "redux", "mirai"), quietly = TRUE) &&
redux::redis_available()) {
fun = function(xs) {
list(y = - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
}
# set domain
domain = ps(
x1 = p_dbl(-10, 10),
x2 = p_dbl(-5, 5)
)
# set codomain
codomain = ps(
y = p_dbl(tags = "maximize")
)
# create objective
objective = ObjectiveRFun$new(
fun = fun,
domain = domain,
codomain = codomain,
properties = "deterministic"
)
# start workers
rush::rush_plan(worker_type = "remote")
mirai::daemons(1)
# initialize instance
instance = oi_async(
objective = objective,
terminator = trm("evals", n_evals = 20)
)
# load optimizer
optimizer = opt("async_random_search")
# trigger optimization
optimizer$optimize(instance)
# all evaluated configuration
instance$archive
# best performing configuration
instance$archive$best()
# covert to data.table
as.data.table(instance$archive)
# reset the rush data base
instance$rush$reset()
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.