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
Archive -> ArchiveAsync
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. Points queued for a mirai compute profile have aprofilecolumn.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 in the shared queue and the queues of all mirai compute profiles.n_queued_per_profile(named
integer())
Number of queued points in the shared queue and the queues of all mirai compute profiles. The number of points in the shared queue is named"default".n_queued_available(
integer(1))
Number of queued points a worker can evaluate, i.e. the points in the shared queue and in the queue of the compute profile the worker runs on. Points queued for other compute profiles are not counted.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
Inherited methods
ArchiveAsync$new()
Creates a new instance of this R6 class.
Usage
ArchiveAsync$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.
ArchiveAsync$push_points()
Push queued points to the archive.
Points pushed without a profile are added to the shared queue and are evaluated by any worker.
Points pushed with a profile are added to the queue of the mirai compute profile and are only
evaluated by the workers running on that profile.
ArchiveAsync$push_point()
Push a single queued point to the archive.
Points pushed without a profile are added to the shared queue and are evaluated by any worker.
Points pushed with a profile are added to the queue of the mirai compute profile and are only
evaluated by the workers running on that profile.
ArchiveAsync$push_failed_point()
Push a single failed point to the archive.
Arguments
xs(named
list())
Named list of point values.xs_extra(
named list()|NULL)
Named list of additional information.condition(
any|NULL)
Condition of the failed point. IfNULL, a generic error message is used.
ArchiveAsync$finish_points()
Save the results of multiple running points and move them to the finished points.
Arguments
keys(
character())
Keys of the points.yss(list of named
list())
List of named lists of results.x_domains(
list())
List of named lists of transformed point values.yss_extra(list of named
list()|NULL)
List of named lists of additional information.
ArchiveAsync$fail_points()
Move multiple running points to the failed points.
Arguments
keys(
character())
Keys of the points.conditions(
list()|NULL)
Conditions of the failed points.
ArchiveAsync$push_result()
Deprecated.
Use $finish_point() instead.
Arguments
key(
character())
Key of the point.ys(
list())
Named list of results.x_domain(
list())
Named list of transformed point values.extra(
list())
Named list of additional information.
ArchiveAsync$data_with_state()
Fetch points with a specific state.
Arguments
fields(
character())
Fields to fetch. Defaults toc("worker_id", "xs", "ys", "xs_extra", "ys_extra", "condition").states(
character())
States of the tasks to be fetched. Defaults toc("queued", "running", "finished", "failed").
ArchiveAsync$best()
Returns the best scoring evaluation(s). For single-crit optimization, the solution that minimizes / maximizes the objective function. For multi-crit optimization, the Pareto set / front.
Arguments
n_select(
integer(1L))
Amount of points to select. Ignored for multi-crit optimization.ties_method(
character(1L))
Method to break ties when multiple points have the same score. Either"first"(default) or"random". Ignored for multi-crit optimization. Ifn_select > 1L, the tie method is ignored and the first point is returned.
ArchiveAsync$nds_selection()
Calculate best points w.r.t. non dominated sorting with hypervolume contribution.
Arguments
n_select(
integer(1L))
Amount of points to select.ref_point(
numeric())
Reference point for hypervolume.
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$new(
fun = fun,
domain = domain,
codomain = codomain,
properties = "deterministic"
)
# start workers
rush::rush_plan(worker_type = "mirai")
mirai::daemons(1)
# initialize instance
instance = oi_async(
objective = objective,
terminator = trm("evals", n_evals = 20)
)
# load optimizer
optimizer = opt("async_random_search")
# trigger optimization
optimizer$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()
}
#> ERROR [09:28:08.685] [rush] Lost worker 'overzealous_cowrie_e5a1eef5': Error in !self$is_terminated && self$archive$n_queued_available: invalid 'y' type in 'x && y'
#> Error: Optimization terminated without any finished evaluations.