Method clone()
The objects of this class are cloneable with this method.
-
Usage
+
Usage
Archive$clone(deep = FALSE)
-
Arguments
+
Arguments
deepWhether to make a deep clone.
diff --git a/dev/reference/ArchiveAsync.html b/dev/reference/ArchiveAsync.html
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+
+
Rush Data Storage — ArchiveAsync • bbotk
+
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+
+
+
+
+
+
+
+
The ArchiveAsync stores all evaluated points and performance scores in a rush::Rush data base.
+
+
Public fields
+
rush(Rush)
+Rush controller for parallel optimization.
+
+
Active bindings
+
data(data.table::data.table)
+Data table with all finished points.
queued_data(data.table::data.table)
+Data table with all queued points.
running_data(data.table::data.table)
+Data table with all running points.
finished_data(data.table::data.table)
+Data table with all finished points.
failed_data(data.table::data.table)
+Data table with all failed points.
n_queued(integer(1))
+Number of queued points.
n_running(integer(1))
+Number of running points.
n_finished(integer(1))
+Number of finished points.
n_failed(integer(1))
+Number of failed points.
n_evals(integer(1))
+Number of evaluations stored in the archive.
+
+
Methods
+
+
Inherited methods
+
Method new()
+
Creates a new instance of this R6 class.
+
Usage
+
ArchiveAsync$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$push_points(xss)
+
+
+
+
Arguments
+
xss(list of named list())
+List of named lists of point values.
+
+
+
+
Method pop_point()
+
Pop a point from the queue.
+
+
+
+
Method push_running_point()
+
Push running point to the archive.
+
Usage
+
ArchiveAsync$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)
+
+
+
+
+
+
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.
+
+
+
+
+
+
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.
+
+
+
+
+
Method clear()
+
Clear all evaluation results from archive.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/dev/reference/ArchiveBatch.html b/dev/reference/ArchiveBatch.html
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+
+
Data Table Storage — ArchiveBatch • bbotk
+
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+
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+
+
Public fields
+
data(data.table::data.table)
+Contains all performed Objective function calls.
data_extra(named list)
+Data created by specific Optimizers that does not relate to any individual function evaluation and can therefore not be held in $data.
+Every optimizer should create and refer to its own entry in this list, named by its class().
+
+
Active bindings
+
n_evals(integer(1))
+Number of evaluations stored in the archive.
n_batch(integer(1))
+Number of batches stored in the archive.
+
+
Methods
+
+
Inherited methods
+
Method new()
+
Creates a new instance of this R6 class.
+
Usage
+
ArchiveBatch$new(search_space, codomain, check_values = FALSE)
+
+
+
+
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 x-values that are added to the archive be checked for validity?
+Search space that is logged into archive.
+
+
+
+
Method add_evals()
+
Adds function evaluations to the archive table.
+
Usage
+
ArchiveBatch$add_evals(xdt, xss_trafoed = NULL, ydt)
+
+
+
+
Arguments
+
xdt(data.table::data.table())
+Set of untransformed points / points from the search space.
+One point per row, e.g. data.table(x1 = c(1, 3), x2 = c(2, 4)).
+Column names have to match ids of the search_space.
+However, xdt can contain additional columns.
xss_trafoed(list())
+Transformed point(s) in the domain space.
ydt(data.table::data.table())
+Optimal outcome.
+
+
+
+
Method best()
+
Returns the best scoring evaluation(s).
+For single-crit optimization, the solution that minimizes / maximizes the objective function.
+For multi-crit optimization, the Pareto set / front.
+
Usage
+
ArchiveBatch$best(batch = NULL, n_select = 1L, ties_method = "first")
+
+
+
+
Arguments
+
batch(integer())
+The batch number(s) to limit the best results to.
+Default is all batches.
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.
+
+
+
+
+
+
Calculate best points w.r.t. non dominated sorting with hypervolume contribution.
+
Usage
+
ArchiveBatch$nds_selection(batch = NULL, n_select = 1, ref_point = NULL)
+
+
+
+
Arguments
+
batch(integer())
+The batch number(s) to limit the best points to. Default is
+all batches.
n_select(integer(1L))
+Amount of points to select.
ref_point(numeric())
+Reference point for hypervolume.
+
+
+
+
+
Method clear()
+
Clear all evaluation results from archive.
+
+
+
+
Method clone()
+
The objects of this class are cloneable with this method.
+
Usage
+
ArchiveBatch$clone(deep = FALSE)
+
+
+
+
Arguments
+
deepWhether to make a deep clone.
+
+
+
+
+
+
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diff --git a/dev/reference/CallbackAsync.html b/dev/reference/CallbackAsync.html
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+
+
Create Asynchronous Optimization Callback — CallbackAsync • bbotk
+
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+
+
+
+
+
+
+
+
+
+
+
+
Public fields
+
on_optimization_begin(function())
+Stage called at the beginning of the optimization in the main process.
+Called in Optimizer$optimize().
on_worker_begin(function())
+Stage called at the beginning of the optimization on the worker.
+Called in the worker loop.
on_worker_end(function())
+Stage called at the end of the optimization on the worker.
+Called in the worker loop.
on_result(function())
+Stage called after result are written.
+Called in OptimInstance$assign_result().
on_optimization_end(function())
+Stage called at the end of the optimization in the main process.
+Called in Optimizer$optimize().
+
+
Methods
+
+
Inherited methods
+
Method clone()
+
The objects of this class are cloneable with this method.
+
Usage
+
CallbackAsync$clone(deep = FALSE)
+
+
+
+
Arguments
+
deepWhether to make a deep clone.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/dev/reference/CallbackBatch.html b/dev/reference/CallbackBatch.html
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+
+
Create Batch Optimization Callback — CallbackBatch • bbotk
+
Skip to contents
+
+
+
+
+
+
+
+
+
+
+
+
Public fields
+
on_optimization_begin(function())
+Stage called at the beginning of the optimization.
+Called in Optimizer$optimize().
on_optimizer_before_eval(function())
+Stage called after the optimizer proposes points.
+Called in OptimInstance$eval_batch().
on_optimizer_after_eval(function())
+Stage called after points are evaluated.
+Called in OptimInstance$eval_batch().
on_result(function())
+Stage called after result are written.
+Called in OptimInstance$assign_result().
on_optimization_end(function())
+Stage called at the end of the optimization.
+Called in Optimizer$optimize().
+
+
Methods
+
+
Inherited methods
+
Method clone()
+
The objects of this class are cloneable with this method.
+
Usage
+
CallbackBatch$clone(deep = FALSE)
+
+
+
+
Arguments
+
deepWhether to make a deep clone.
+
+
+
+
+
+
Examples
+
# write archive to disk
+callback_batch("bbotk.backup",
+ on_optimization_end = function(callback, context) {
+ saveRDS(context$instance$archive, "archive.rds")
+ }
+)
+#> <CallbackBatch:bbotk.backup>
+#> * Active Stages: on_optimization_end
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/dev/reference/Codomain.html b/dev/reference/Codomain.html
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--- a/dev/reference/Codomain.html
+++ b/dev/reference/Codomain.html
@@ -4,7 +4,7 @@
"maximize". The codomain may contain extra parameters which are ignored
when calling the Archive methods $best(), $nds_selection() and
$cols_y. This class is usually constructed internally from a
-ParamSet when Objective is initialized.'>
Codomain of Function — Codomain • bbotkCodomain of Function — Codomain • bbotkbbotk: Black-Box Optimization Toolkit — bbotk-package • bbotkbbotk: Black-Box Optimization Toolkit — bbotk-package • bbotk
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