Type:	Package
Title:	Define and Work with Parameter Spaces for Complex Algorithms
Version:	1.0.1
Description:	Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. Also includes statistical designs and random samplers. Objects are implemented as 'R6' classes.
License:	LGPL-3
URL:	https://paradox.mlr-org.com, https://github.com/mlr-org/paradox
BugReports:	https://github.com/mlr-org/paradox/issues
Imports:	backports, checkmate, data.table, methods, mlr3misc (≥ 0.9.4), R6
Suggests:	rmarkdown, mlr3learners, e1071, knitr, lhs, spacefillr, testthat
Encoding:	UTF-8
Config/testthat/edition:	3
Config/testthat/parallel:	false
NeedsCompilation:	no
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
Collate:	'Condition.R' 'Design.R' 'Domain.R' 'Domain_methods.R' 'NoDefault.R' 'ParamDbl.R' 'ParamFct.R' 'ParamInt.R' 'ParamLgl.R' 'ParamSet.R' 'ParamSetCollection.R' 'ParamUty.R' 'Sampler.R' 'Sampler1D.R' 'SamplerHierarchical.R' 'SamplerJointIndep.R' 'SamplerUnif.R' 'asserts.R' 'default_values.R' 'generate_design_grid.R' 'generate_design_lhs.R' 'generate_design_random.R' 'generate_design_sobol.R' 'helper.R' 'ps.R' 'ps_replicate.R' 'ps_union.R' 'reexports.R' 'to_tune.R' 'zzz.R'
Packaged:	2024-07-09 11:25:23 UTC; user
Author:	Michel Lang [aut], Bernd Bischl [aut], Jakob Richter [aut], Xudong Sun [aut], Martin Binder [aut, cre], Marc Becker [ctb]
Maintainer:	Martin Binder <mlr.developer@mb706.com>
Repository:	CRAN
Date/Publication:	2024-07-09 13:10:02 UTC

paradox: Define and Work with Parameter Spaces for Complex Algorithms

Description

Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. Also includes statistical designs and random samplers. Objects are implemented as 'R6' classes.

Author(s)

Maintainer: Martin Binder mlr.developer@mb706.com

Authors:

• Michel Lang michellang@gmail.com (ORCID)

• Bernd Bischl bernd_bischl@gmx.net (ORCID)

• Jakob Richter jakob1richter@gmail.com (ORCID)

• Xudong Sun smilesun.east@gmail.com (ORCID)

Other contributors:

• Marc Becker marcbecker@posteo.de (ORCID) [contributor]

See Also

Useful links:

• https://paradox.mlr-org.com

• https://github.com/mlr-org/paradox

• Report bugs at https://github.com/mlr-org/paradox/issues

Design of Configurations

Description

A lightweight wrapper around a ParamSet and a data.table::data.table(), where the latter is a design of configurations produced from the former - e.g., by calling a generate_design_grid() or by sampling.

Public fields

Methods

Public methods

• Design$new()

• Design$format()

• Design$print()

• Design$transpose()

• Design$clone()

Method new()

Creates a new instance of this R6 class.

Usage

Design$new(param_set, data, remove_dupl)

Arguments

Method format()

Helper for print outputs.

Usage

Design$format(...)

Arguments

Method print()

Printer.

Usage

Design$print(...)

Arguments

Method transpose()

Converts data into a list of lists of row-configurations, possibly removes NA entries of inactive parameter values due to unsatisfied dependencies, and possibly calls the trafo function of the ParamSet.

Usage

Design$transpose(filter_na = TRUE, trafo = TRUE)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Design$clone(deep = FALSE)

Arguments

Domain: Parameter Range without an Id

Description

A Domain object is a representation of a single dimension of a ParamSet. Domain objects are used to construct ParamSets, either through the ps() short form, through the ParamSet constructor itself, or through the ParamSet⁠$search_space()⁠ mechanism (see to_tune()). For each of the basic parameter classes ("ParamInt", "ParamDbl", "ParamLgl", "ParamFct", and "ParamUty") there is a function constructing a Domain object (p_int(), p_dbl(), p_lgl(), p_fct(), p_uty()). They each have fitting construction arguments that control their bounds and behavior.

Domain objects are representations of parameter ranges and are intermediate objects to be used in short form constructions in to_tune() and ps(). Because of their nature, they should not be modified by the user, once constructed. The Domain object's internals are subject to change and should not be relied upon.

Usage

p_dbl(
  lower = -Inf,
  upper = Inf,
  special_vals = list(),
  default = NO_DEF,
  tags = character(),
  tolerance = sqrt(.Machine$double.eps),
  depends = NULL,
  trafo = NULL,
  logscale = FALSE,
  init,
  aggr = NULL,
  in_tune_fn = NULL,
  disable_in_tune = NULL
)

p_fct(
  levels,
  special_vals = list(),
  default = NO_DEF,
  tags = character(),
  depends = NULL,
  trafo = NULL,
  init,
  aggr = NULL,
  in_tune_fn = NULL,
  disable_in_tune = NULL
)

p_int(
  lower = -Inf,
  upper = Inf,
  special_vals = list(),
  default = NO_DEF,
  tags = character(),
  tolerance = sqrt(.Machine$double.eps),
  depends = NULL,
  trafo = NULL,
  logscale = FALSE,
  init,
  aggr = NULL,
  in_tune_fn = NULL,
  disable_in_tune = NULL
)

p_lgl(
  special_vals = list(),
  default = NO_DEF,
  tags = character(),
  depends = NULL,
  trafo = NULL,
  init,
  aggr = NULL,
  in_tune_fn = NULL,
  disable_in_tune = NULL
)

p_uty(
  custom_check = NULL,
  special_vals = list(),
  default = NO_DEF,
  tags = character(),
  depends = NULL,
  trafo = NULL,
  repr = substitute(default),
  init,
  aggr = NULL,
  in_tune_fn = NULL,
  disable_in_tune = NULL
)

Arguments

Details

Although the levels values of a constructed p_fct() will always be character-valued, the p_fct function admits a levels argument that goes beyond this: Besides a character vector, any atomic vector or list (optionally named) may be given. (If the value is a list that is not named, the names are inferred using as.character() on the values.) The resulting Domain will correspond to a range of values given by the names of the levels argument with a trafo that maps the character names to the arbitrary values of the levels argument.

Value

A Domain object.

See Also

Other ParamSet construction helpers: ps(), to_tune()

Examples

params = ps(
  unbounded_integer = p_int(),
  bounded_double = p_dbl(0, 10),
  half_bounded_integer = p_dbl(1),
  half_bounded_double = p_dbl(upper = 1),
  double_with_trafo = p_dbl(-1, 1, trafo = exp),
  extra_double = p_dbl(0, 1, special_vals = list("xxx"), tags = "tagged"),
  factor_param = p_fct(c("a", "b", "c")),
  factor_param_with_implicit_trafo = p_fct(list(a = 1, b = 2, c = list()))
)
print(params)

params$trafo(list(
  bounded_double = 1,
  double_with_trafo = 1,
  factor_param = "c",
  factor_param_with_implicit_trafo = "c"
))

# logscale:
params = ps(x = p_dbl(1, 100, logscale = TRUE))

# The ParamSet has bounds log(1) .. log(100):
print(params)

# When generating a equidistant grid, it is equidistant within log values
grid = generate_design_grid(params, 3)
print(grid)

# But the values are on a log scale with desired bounds after trafo
print(grid$transpose())

# Integer parameters with logscale are `p_dbl()`s pre-trafo
params = ps(x = p_int(0, 10, logscale = TRUE))
print(params)

grid = generate_design_grid(params, 4)
print(grid)

# ... but get transformed to integers.
print(grid$transpose())


# internal tuning

param_set = ps(
  iters = p_int(0, Inf, tags = "internal_tuning", aggr = function(x) round(mean(unlist(x))),
    in_tune_fn = function(domain, param_vals) {
      stopifnot(domain$lower <= 1)
      stopifnot(param_vals$early_stopping == TRUE)
      domain$upper
    },
    disable_in_tune = list(early_stopping = FALSE)),
  early_stopping = p_lgl()
)
param_set$set_values(
  iters = to_tune(upper = 100, internal = TRUE),
  early_stopping = TRUE
)
param_set$convert_internal_search_space(param_set$search_space())
param_set$aggr_internal_tuned_values(
  list(iters = list(1, 2, 3))
)

param_set$disable_internal_tuning("iters")
param_set$values$early_stopping

Extra data type for "no default value"

Description

Special new data type for no-default. Not often needed by the end-user, mainly internal.

• NO_DEF: Singleton object for type, used in Domain when no default is given.

• is_nodefault(): Is an object the 'no default' object?

ParamSet

Description

An object representing the space of possible parametrizations of a function or another object. ParamSets are used on the side of objects being parameterized, where they function as a configuration space determining the set of possible configurations accepted by these objects. They can also be used to specify search spaces for optimization, indicating the set of legal configurations to try out. It is often convenient to generate search spaces from configuration spaces, which can be done using the ⁠$search_space()⁠ method in combination with to_tune() / TuneToken objects.

Individual dimensions of a ParamSet are specified by Domain objects, created as p_dbl(), p_lgl() etc. The field ⁠$values⁠ can be used to store an active configuration or to partially fix some parameters to constant values – the precise effect can be determined by the object being parameterized.

Constructing a ParamSet can be done using ParamSet$new() in combination with a named list of Domain objects. This route is recommended when the set of dimensions (i.e. the members of this named list) is dynamically created, such as when the number of parameters is variable. ParamSets can also be created using the ps() shorthand, which is the recommended way when the set of parameters is fixed. In practice, the majority of cases where a ParamSet is created, the ps() should be used.

S3 methods and type converters

• as.data.table()
ParamSet -> data.table::data.table()
  Compact representation as datatable. Col types are:
  

  • id: character

  • class: character

  • lower, upper: numeric

  • levels: list col, with NULL elements

  • nlevels: integer valued numeric

  • is_bounded: logical

  • special_vals: list col of list

  • default: list col

  • storage_type: character

  • tags: list col of character vectors

Public fields

Active bindings

Methods

Public methods

• ParamSet$new()

• ParamSet$ids()

• ParamSet$get_values()

• ParamSet$set_values()

• ParamSet$trafo()

• ParamSet$aggr_internal_tuned_values()

• ParamSet$disable_internal_tuning()

• ParamSet$convert_internal_search_space()

• ParamSet$test_constraint()

• ParamSet$test_constraint_dt()

• ParamSet$check()

• ParamSet$check_dependencies()

• ParamSet$test()

• ParamSet$assert()

• ParamSet$check_dt()

• ParamSet$test_dt()

• ParamSet$assert_dt()

• ParamSet$qunif()

• ParamSet$get_domain()

• ParamSet$subset()

• ParamSet$subspaces()

• ParamSet$flatten()

• ParamSet$search_space()

• ParamSet$add_dep()

• ParamSet$format()

• ParamSet$print()

• ParamSet$clone()

Method new()

Creates a new instance of this R6 class.

Usage

ParamSet$new(params = named_list(), allow_dangling_dependencies = FALSE)

Arguments

Method ids()

Retrieves IDs of contained parameters based on some filter criteria selections, NULL means no restriction. Only returns IDs of parameters that satisfy all conditions.

Usage

ParamSet$ids(class = NULL, tags = NULL, any_tags = NULL)

Arguments

Returns

character().

Method get_values()

Retrieves parameter values based on some selections, NULL means no restriction and is equivalent to ⁠$values⁠. Only returns values of parameters that satisfy all conditions.

Usage

ParamSet$get_values(
  class = NULL,
  tags = NULL,
  any_tags = NULL,
  type = "with_token",
  check_required = TRUE,
  remove_dependencies = TRUE
)

Arguments

Returns

Named list().

Method set_values()

Allows to to modify (and overwrite) or replace the parameter values. Per default already set values are being kept unless new values are being provided.

Usage

ParamSet$set_values(..., .values = list(), .insert = TRUE)

Arguments

Method trafo()

Perform transformation specified by the trafo of Domain objects, as well as the ⁠$extra_trafo⁠ field.

Usage

ParamSet$trafo(x, param_set = self)

Arguments

Method aggr_internal_tuned_values()

Aggregate parameter values according to their aggregation rules.

Usage

ParamSet$aggr_internal_tuned_values(x)

Arguments

Returns

(named list())

Method disable_internal_tuning()

Set the parameter values so that internal tuning for the selected parameters is disabled.

Usage

ParamSet$disable_internal_tuning(ids)

Arguments

Returns

Self

Method convert_internal_search_space()

Convert all parameters from the search space to parameter values using the transformation given by in_tune_fn.

Usage

ParamSet$convert_internal_search_space(search_space)

Arguments

Returns

(named list())

Method test_constraint()

checkmate-like test-function. Takes a named list. Return FALSE if the given ⁠$constraint⁠ is not satisfied, TRUE otherwise. Note this is different from satisfying the bounds or types given by the ParamSet itself: If x does not satisfy these, an error will be thrown, given that assert_value is TRUE.

Usage

ParamSet$test_constraint(x, assert_value = TRUE)

Arguments

Returns

logical(1): Whether x satisfies the ⁠$constraint⁠.

Method test_constraint_dt()

checkmate-like test-function. Takes a data.table. For each row, return FALSE if the given ⁠$constraint⁠ is not satisfied, TRUE otherwise. Note this is different from satisfying the bounds or types given by the ParamSet itself: If x does not satisfy these, an error will be thrown, given that assert_value is TRUE.

Usage

ParamSet$test_constraint_dt(x, assert_value = TRUE)

Arguments

Returns

logical: For each row in x, whether it satisfies the ⁠$constraint⁠.

Method check()

checkmate-like check-function. Takes a named list. A point x is feasible, if it configures a subset of params, all individual param constraints are satisfied and all dependencies are satisfied. Params for which dependencies are not satisfied should not be part of x. Constraints and dependencies are not checked when check_strict is FALSE.

Usage

ParamSet$check(xs, check_strict = TRUE, sanitize = FALSE)

Arguments

Returns

If successful TRUE, if not a string with an error message.

Method check_dependencies()

checkmate-like check-function. Takes a named list. Checks that all individual param dependencies are satisfied.

Usage

ParamSet$check_dependencies(xs)

Arguments

Returns

If successful TRUE, if not a string with an error message.

Method test()

checkmate-like test-function. Takes a named list. A point x is feasible, if it configures a subset of params, all individual param constraints are satisfied and all dependencies are satisfied. Params for which dependencies are not satisfied should not be part of x. Constraints and dependencies are not checked when check_strict is FALSE.

Usage

ParamSet$test(xs, check_strict = TRUE)

Arguments

Returns

If successful TRUE, if not FALSE.

Method assert()

checkmate-like assert-function. Takes a named list. A point x is feasible, if it configures a subset of params, all individual param constraints are satisfied and all dependencies are satisfied. Params for which dependencies are not satisfied should not be part of x. Constraints and dependencies are not checked when check_strict is FALSE.

Usage

ParamSet$assert(
  xs,
  check_strict = TRUE,
  .var.name = vname(xs),
  sanitize = FALSE
)

Arguments

Returns

If successful xs invisibly, if not an error message.

Method check_dt()

checkmate-like check-function. Takes a data.table::data.table where rows are points and columns are parameters. A point x is feasible, if it configures a subset of params, all individual param constraints are satisfied and all dependencies are satisfied. Params for which dependencies are not satisfied should not be part of x. Constraints and dependencies are not checked when check_strict is FALSE.

Usage

ParamSet$check_dt(xdt, check_strict = TRUE)

Arguments

Returns

If successful TRUE, if not a string with the error message.

Method test_dt()

checkmate-like test-function (s. ⁠$check_dt()⁠).

Usage

ParamSet$test_dt(xdt, check_strict = TRUE)

Arguments

Returns

If successful TRUE, if not FALSE.

Method assert_dt()

checkmate-like assert-function (s. ⁠$check_dt()⁠).

Usage

ParamSet$assert_dt(xdt, check_strict = TRUE, .var.name = vname(xdt))

Arguments

Returns

If successful xs invisibly, if not, an error is generated.

Method qunif()

Map a matrix or data.frame of values between 0 and 1 to proportional values inside the feasible intervals of individual parameters.

Usage

ParamSet$qunif(x)

Arguments

Returns

data.table.

Method get_domain()

get the Domain object that could be used to create a given parameter.

Usage

ParamSet$get_domain(id)

Arguments

Returns

Domain.

Method subset()

Create a new ParamSet restricted to the passed IDs.

Usage

ParamSet$subset(
  ids,
  allow_dangling_dependencies = FALSE,
  keep_constraint = TRUE
)

Arguments

Returns

ParamSet.

Method subspaces()

Create new one-dimensional ParamSets for each dimension.

Usage

ParamSet$subspaces(ids = private$.params$id)

Arguments

Returns

named list() of ParamSet.

Method flatten()

Create a ParamSet from this object, even if this object itself is not a ParamSet but e.g. a ParamSetCollection.

Usage

ParamSet$flatten()

Method search_space()

Construct a ParamSet to tune over. Constructed from TuneToken in ⁠$values⁠, see to_tune().

Usage

ParamSet$search_space(values = self$values)

Arguments

Method add_dep()

Adds a dependency to this set, so that param id now depends on param on.

Usage

ParamSet$add_dep(id, on, cond, allow_dangling_dependencies = FALSE)

Arguments

Method format()

Helper for print outputs.

Usage

ParamSet$format()

Arguments

Method print()

Printer.

Usage

ParamSet$print(
  ...,
  hide_cols = c("levels", "is_bounded", "special_vals", "tags", "storage_type")
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ParamSet$clone(deep = FALSE)

Arguments

Examples

pset = ParamSet$new(
  params = list(
    d = p_dbl(lower = -5, upper = 5, default = 0, trafo = function(x) 2^x),
    f = p_fct(levels = letters[1:3])
  )
)

# alternative, recommended way of construction in this case since the
# parameter list is not dynamic:
pset = ps(
  d = p_dbl(lower = -5, upper = 5, default = 0, trafo = function(x) 2^x),
  f = p_fct(levels = letters[1:3])
)

pset$check(list(d = 2.1, f = "a"))

pset$check(list(d = 2.1, f = "d"))

ParamSetCollection

Description

A collection of multiple ParamSet objects.

• The collection is basically a light-weight wrapper / container around references to multiple sets.

• In order to ensure unique param names, every param in the collection is referred to with "<set_id>.<param_id>", where ⁠<set_id>⁠ is the name of the entry a given ParamSet in the named list given during construction. Parameters from ParamSet with empty (i.e. "") set_id are referenced directly. Multiple ParamSets with set_id "" can be combined, but their parameter names may not overlap to avoid name clashes.

• When you either ask for 'values' or set them, the operation is delegated to the individual, contained ParamSet references. The collection itself does not maintain a values state. This also implies that if you directly change values in one of the referenced sets, this change is reflected in the collection.

• Dependencies: It is possible to currently handle dependencies

  • regarding parameters inside of the same set - in this case simply add the dependency to the set, best before adding the set to the collection

  • across sets, where a param from one set depends on the state of a param from another set - in this case add call add_dep on the collection.

  If you call deps on the collection, you are returned a complete table of dependencies, from sets and across sets.

Super class

paradox::ParamSet -> ParamSetCollection

Active bindings

Methods

Public methods

• ParamSetCollection$new()

• ParamSetCollection$add()

• ParamSetCollection$disable_internal_tuning()

• ParamSetCollection$convert_internal_search_space()

• ParamSetCollection$flatten()

• ParamSetCollection$clone()

Method new()

Creates a new instance of this R6 class.

Usage

ParamSetCollection$new(sets, tag_sets = FALSE, tag_params = FALSE)

Arguments

Method add()

Adds a ParamSet to this collection.

Usage

ParamSetCollection$add(p, n = "", tag_sets = FALSE, tag_params = FALSE)

Arguments

Method disable_internal_tuning()

Set the parameter values so that internal tuning for the selected parameters is disabled.

Usage

ParamSetCollection$disable_internal_tuning(ids)

Arguments

Returns

Self

Method convert_internal_search_space()

Convert all parameters from the search space to parameter values using the transformation given by in_tune_fn.

Usage

ParamSetCollection$convert_internal_search_space(search_space)

Arguments

Returns

(named list())

Method flatten()

Create a ParamSet from this ParamSetCollection.

Usage

ParamSetCollection$flatten()

Method clone()

The objects of this class are cloneable with this method.

Usage

ParamSetCollection$clone(deep = FALSE)

Arguments

Sampler Class

Description

This is the abstract base class for sampling objects like Sampler1D, SamplerHierarchical or SamplerJointIndep.

Public fields

Methods

Public methods

• Sampler$new()

• Sampler$sample()

• Sampler$format()

• Sampler$print()

• Sampler$clone()

Method new()

Creates a new instance of this R6 class.

Note that this object is typically constructed via derived classes, e.g., Sampler1D.

Usage

Sampler$new(param_set)

Arguments

Method sample()

Sample n values from the distribution.

Usage

Sampler$sample(n)

Arguments

Returns

Design.

Method format()

Helper for print outputs.

Usage

Sampler$format(...)

Arguments

Method print()

Printer.

Usage

Sampler$print(...)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Sampler$clone(deep = FALSE)

Arguments

See Also

Other Sampler: Sampler1D, Sampler1DCateg, Sampler1DNormal, Sampler1DRfun, Sampler1DUnif, SamplerHierarchical, SamplerJointIndep, SamplerUnif

Sampler1D Class

Description

1D sampler, abstract base class for Sampler like Sampler1DUnif, Sampler1DRfun, Sampler1DCateg and Sampler1DNormal.

Super class

paradox::Sampler -> Sampler1D

Active bindings

Methods

Public methods

• Sampler1D$new()

• Sampler1D$clone()

Method new()

Creates a new instance of this R6 class.

Note that this object is typically constructed via derived classes, e.g., Sampler1DUnif.

Usage

Sampler1D$new(param)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Sampler1D$clone(deep = FALSE)

Arguments

See Also

Other Sampler: Sampler, Sampler1DCateg, Sampler1DNormal, Sampler1DRfun, Sampler1DUnif, SamplerHierarchical, SamplerJointIndep, SamplerUnif

Sampler1DCateg Class

Description

Sampling from a discrete distribution, for a ParamSet containing a single p_fct() or p_lgl().

Super classes

paradox::Sampler -> paradox::Sampler1D -> Sampler1DCateg

Public fields

Methods

Public methods

• Sampler1DCateg$new()

• Sampler1DCateg$clone()

Method new()

Creates a new instance of this R6 class.

Usage

Sampler1DCateg$new(param, prob = NULL)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Sampler1DCateg$clone(deep = FALSE)

Arguments

See Also

Other Sampler: Sampler, Sampler1D, Sampler1DNormal, Sampler1DRfun, Sampler1DUnif, SamplerHierarchical, SamplerJointIndep, SamplerUnif

Sampler1DNormal Class

Description

Normal sampling (potentially truncated) for p_dbl().

Super classes

paradox::Sampler -> paradox::Sampler1D -> paradox::Sampler1DRfun -> Sampler1DNormal

Active bindings

Methods

Public methods

• Sampler1DNormal$new()

• Sampler1DNormal$clone()

Method new()

Creates a new instance of this R6 class.

Usage

Sampler1DNormal$new(param, mean = NULL, sd = NULL)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Sampler1DNormal$clone(deep = FALSE)

Arguments

See Also

Other Sampler: Sampler, Sampler1D, Sampler1DCateg, Sampler1DRfun, Sampler1DUnif, SamplerHierarchical, SamplerJointIndep, SamplerUnif

Sampler1DRfun Class

Description

Arbitrary sampling from 1D RNG functions from R.

Super classes

paradox::Sampler -> paradox::Sampler1D -> Sampler1DRfun

Public fields

Methods

Public methods

• Sampler1DRfun$new()

• Sampler1DRfun$clone()

Method new()

Creates a new instance of this R6 class.

Usage

Sampler1DRfun$new(param, rfun, trunc = TRUE)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Sampler1DRfun$clone(deep = FALSE)

Arguments

See Also

Other Sampler: Sampler, Sampler1D, Sampler1DCateg, Sampler1DNormal, Sampler1DUnif, SamplerHierarchical, SamplerJointIndep, SamplerUnif

Sampler1DUnif Class

Description

Uniform random sampler for arbitrary (bounded) parameters.

Super classes

paradox::Sampler -> paradox::Sampler1D -> Sampler1DUnif

Methods

Public methods

• Sampler1DUnif$new()

• Sampler1DUnif$clone()

Method new()

Creates a new instance of this R6 class.

Usage

Sampler1DUnif$new(param)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Sampler1DUnif$clone(deep = FALSE)

Arguments

See Also

Other Sampler: Sampler, Sampler1D, Sampler1DCateg, Sampler1DNormal, Sampler1DRfun, SamplerHierarchical, SamplerJointIndep, SamplerUnif

SamplerHierarchical Class

Description

Hierarchical sampling for arbitrary param sets with dependencies, where the user specifies 1D samplers per param. Dependencies are topologically sorted, parameters are then sampled in topological order, and if dependencies do not hold, values are set to NA in the resulting data.table.

Super class

paradox::Sampler -> SamplerHierarchical

Public fields

Methods

Public methods

• SamplerHierarchical$new()

• SamplerHierarchical$clone()

Method new()

Creates a new instance of this R6 class.

Usage

SamplerHierarchical$new(param_set, samplers)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

SamplerHierarchical$clone(deep = FALSE)

Arguments

See Also

Other Sampler: Sampler, Sampler1D, Sampler1DCateg, Sampler1DNormal, Sampler1DRfun, Sampler1DUnif, SamplerJointIndep, SamplerUnif

SamplerJointIndep Class

Description

Create joint, independent sampler out of multiple other samplers.

Super class

paradox::Sampler -> SamplerJointIndep

Public fields

Methods

Public methods

• SamplerJointIndep$new()

• SamplerJointIndep$clone()

Method new()

Creates a new instance of this R6 class.

Usage

SamplerJointIndep$new(samplers)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

SamplerJointIndep$clone(deep = FALSE)

Arguments

See Also

Other Sampler: Sampler, Sampler1D, Sampler1DCateg, Sampler1DNormal, Sampler1DRfun, Sampler1DUnif, SamplerHierarchical, SamplerUnif

SamplerUnif Class

Description

Uniform random sampling for an arbitrary (bounded) ParamSet. Constructs 1 uniform sampler per parameter, then passes them to SamplerHierarchical. Hence, also works for ParamSets sets with dependencies.

Super classes

paradox::Sampler -> paradox::SamplerHierarchical -> SamplerUnif

Methods

Public methods

• SamplerUnif$new()

• SamplerUnif$clone()

Method new()

Creates a new instance of this R6 class.

Usage

SamplerUnif$new(param_set)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

SamplerUnif$clone(deep = FALSE)

Arguments

See Also

Other Sampler: Sampler, Sampler1D, Sampler1DCateg, Sampler1DNormal, Sampler1DRfun, Sampler1DUnif, SamplerHierarchical, SamplerJointIndep

Assertions for Params and ParamSets

Description

Assertions for Params and ParamSets

Usage

assert_param_set(
  param_set,
  cl = NULL,
  no_untyped = FALSE,
  must_bounded = FALSE,
  no_deps = FALSE
)

Arguments

Value

The checked object, invisibly.

Dependency Condition

Description

Condition object, to specify the condition in a dependency.

Usage

condition_test(cond, x)

condition_as_string(cond, lhs_chr = "x")

Condition(rhs, condition_format_string)

Arguments

Functions

• condition_test(): Used internally. Tests whether a value satisfies a given condition. Vectorizes when x is atomic.

• condition_as_string(): Used internally. Returns a string that represents the condition for pretty printing, in the form "<lhs> <relation> <rhs>", e.g. "x == 3" or "param %in% {1, 2, 10}".

Currently implemented simple conditions

• CondEqual(rhs)
  Value must be equal to rhs.

• CondAnyOf(rhs)
  Value must be any value of rhs.

Extract Parameter Default Values

Description

Extract parameter default values.

Usage

default_values(x, ...)

## S3 method for class 'ParamSet'
default_values(x, ...)

Arguments

Value

list().

Check Value Validity

Description

checkmate-like check-function. Check whether a list of values is feasible in the domain. A value is feasible if it is of the same storage_type, inside of the bounds or element of special_vals. TuneTokens are generally not accepted, so they should be filtered out before the call, if present.

domain_check will return TRUE for accepted values, a character(1) error message otherwise.

domain_test will return TRUE for accepted values, FALSE otherwise.

domain_assert will return the param argument silently for accepted values, and throw an error message otherwise.

Usage

domain_check(param, values, internal = FALSE)

domain_assert(
  param,
  values,
  internal = FALSE,
  .var.name = checkmate::vname(param),
  add = NULL
)

domain_test(param, values)

Arguments

Value

If successful TRUE, if not a string with the error message.

Whether a Given Domain is Bounded

Description

This should generally be TRUE when lower and upper are given and finite, or when the nlevels is finite, and FALSE otherwise.

Usage

domain_is_bounded(param)

Arguments

Value

logical.

Whether a Given Domain is Categorical

Description

This should generally be TRUE for categorical Domains, such as p_fct() or p_lgl(), and FALSE otherwise.

Usage

domain_is_categ(param)

Arguments

Value

logical.

Whether a Given Domain is Numeric

Description

This should generally be TRUE for discrete or continuous numeric Domains, and FALSE otherwise.

Usage

domain_is_number(param)

Arguments

Value

logical.

The Number of Levels of a Given Domain

Description

This should be the number of discrete possible levels for discrete type Domains such as p_int() or p_fct(), and Inf for continuous or untyped parameters.

Usage

domain_nlevels(param)

Arguments

Value

numeric.

Transform a Numeric Value to a Sample

Description

Return a valid sample from the given Domain, given a value from the interval ⁠[0, 1]⁠.

Usage

domain_qunif(param, x)

Arguments

Value

any – format depending on the Domain.

Map to Acceptable Value

Description

Map values that are close enough to the given Domain to values that are truly acceptable.

This is used to map numeric() values that are close to but outside the acceptable interval to the interval bounds. It is also used to convert integer-valued numeric values to integer values for p_int().

Usage

domain_sanitize(param, values)

Arguments

Value

any – format depending on the Domain.

Generate a Grid Design

Description

Generate a grid with a specified resolution in the parameter space. The resolution for categorical parameters is ignored, these parameters always produce a grid over all their valid levels. For number params the endpoints of the params are always included in the grid.

Usage

generate_design_grid(param_set, resolution = NULL, param_resolutions = NULL)

Arguments

Value

Design.

See Also

Other generate_design: generate_design_lhs(), generate_design_random(), generate_design_sobol()

Examples

pset = ps(
  ratio = p_dbl(lower = 0, upper = 1),
  letters = p_fct(levels = letters[1:3])
)
generate_design_grid(pset, 10)

Generate a Space-Filling LHS Design

Description

Generate a space-filling design using Latin hypercube sampling. Dependent parameters whose constraints are unsatisfied generate NA entries in their respective columns.

Usage

generate_design_lhs(param_set, n, lhs_fun = NULL)

Arguments

Value

Design.

See Also

Other generate_design: generate_design_grid(), generate_design_random(), generate_design_sobol()

Examples

pset = ps(
  ratio = p_dbl(lower = 0, upper = 1),
  letters = p_fct(levels = letters[1:3])
)

if (requireNamespace("lhs", quietly = TRUE)) {
  generate_design_lhs(pset, 10)
}

Generate a Random Design

Description

Generates a design with randomly drawn points. Internally uses SamplerUnif, hence, also works for ParamSets with dependencies. If dependencies do not hold, values are set to NA in the resulting data.table.

Usage

generate_design_random(param_set, n)

Arguments

Value

Design.

See Also

Other generate_design: generate_design_grid(), generate_design_lhs(), generate_design_sobol()

Examples

pset = ps(
  ratio = p_dbl(lower = 0, upper = 1),
  letters = p_fct(levels = letters[1:3])
)
generate_design_random(pset, 10)

Generate a Space-Filling Sobol Sequence Design

Description

Generate a space-filling design using a Sobol sequence. Dependent parameters whose constraints are unsatisfied generate NA entries in their respective columns.

Uses spacefillr::generate_sobol_set.

Note that non determinism is achieved by sampling the seed argument via sample(.Machine$integer.max, size = 1L).

Usage

generate_design_sobol(param_set, n)

Arguments

Value

Design.

See Also

Other generate_design: generate_design_grid(), generate_design_lhs(), generate_design_random()

Examples

pset = ps(
  ratio = p_dbl(lower = 0, upper = 1),
  letters = p_fct(levels = letters[1:3])
)

if (requireNamespace("spacefillr", quietly = TRUE)) {
  generate_design_sobol(pset, 10)
}

Construct a ParamSet using Short Forms

Description

The ps() short form constructor uses Domain objects (p_dbl, p_fct, ...) to construct ParamSets in a succinct and readable way.

For more specifics also see the documentation of Domain.

Usage

ps(
  ...,
  .extra_trafo = NULL,
  .constraint = NULL,
  .allow_dangling_dependencies = FALSE
)

Arguments

Value

A ParamSet object.

See Also

Other ParamSet construction helpers: Domain(), to_tune()

Examples

pars = ps(
  a = p_int(0, 10),
  b = p_int(upper = 20),
  c = p_dbl(),
  e = p_fct(letters[1:3]),
  f = p_uty(custom_check = checkmate::check_function)
)
print(pars)

pars = ps(
  a = p_dbl(0, 1, trafo = exp),
  b = p_dbl(0, 1, trafo = exp),
  .extra_trafo = function(x, ps) {
    x$c <- x$a + x$b
    x
  }
)

# See how the addition happens after exp()ing:
pars$trafo(list(a = 0, b = 0))

pars$values = list(
  a = to_tune(ps(x = p_int(0, 1),
    .extra_trafo = function(x, param_set) list(a = x$x)
  )),
  # make 'y' depend on 'x', but they are defined in different ParamSets
  # Therefore we need to allow dangling dependencies here.
  b = to_tune(ps(y = p_int(0, 1, depends = x == 1),
    .extra_trafo = function(x, param_set) list(b = x$y),
    .allow_dangling_dependencies = TRUE
  ))
)

pars$search_space()

Create a ParamSet by Repeating a Given ParamSet

Description

Repeat a ParamSet a given number of times and thus create a larger ParamSet. By default, the resulting parameters are prefixed with the string ⁠"repX.", where ⁠X⁠counts up from 1. It is also possible to tag parameters by their original name and by their prefix, making grouped retrieval e.g. using⁠$get_values()' easier.

Usage

ps_replicate(
  set,
  times = length(prefixes),
  prefixes = sprintf("rep%s", seq_len(times)),
  tag_sets = FALSE,
  tag_params = FALSE
)

Arguments

Examples

pset = ps(
  i = p_int(),
  z = p_lgl()
)

ps_replicate(pset, 3)

ps_replicate(pset, prefixes = c("first", "last"))

pset$values = list(i = 1, z = FALSE)

psr = ps_replicate(pset, 2, tag_sets = TRUE, tag_params = TRUE)

# observe the effect of tag_sets, tag_params:
psr$tags

# note that values are repeated as well
psr$values

psr$set_values(rep1.i = 10, rep2.z = TRUE)
psr$values

# use `any_tags` to get subset of values.
# `any_tags = ` is preferable to `tags = `, since parameters
# could also have other tags. `tags = ` would require the
# selected params to have the given tags exclusively.

# get all values associated with the original parameter `i`
psr$get_values(any_tags = "param_i")

# get all values associated with the first repetition "rep1"
psr$get_values(any_tags = "set_rep1")

Create a ParamSet from a list of ParamSets

Description

This emulates ParamSetCollection$new(sets), except that the result is a flat ParamSet, not a ParamSetCollection. The resulting object is decoupled from the input ParamSet objects: Unlike ParamSetCollection, changing ⁠$values⁠ of the resulting object will not change the input ParamSet ⁠$values⁠ by reference.

This emulates ParamSetCollection$new(sets), which in particular means that the resulting ParamSet has all the Domains from the input sets, but some ⁠$id⁠s are changed: If the ParamSet is given in sets with a name, then the Domains will have their ⁠<id>⁠ changed to ⁠<name in "sets">.<id>⁠. This is also reflected in deps.

The c() operator, applied to ParamSets, is a synony for ps_union().

Usage

ps_union(sets, tag_sets = FALSE, tag_params = FALSE)

Arguments

Examples

ps1 = ps(x = p_dbl())
ps1$values = list(x = 1)

ps2 = ps(y = p_lgl())

pu = ps_union(list(ps1, ps2))
# same as:
pu = c(ps1, ps2)

pu

pu$values

pu$values$x = 2
pu$values

# p1 is unchanged:
ps1$values

# Prefixes automatically created for named elements.
# This allows repeating components.
pu2 = c(one = ps1, two = ps1, ps2)
pu2

pu2$values

Create a ParamSet Collection

Description

Creates a ParamSetCollection.

Usage

psc(...)

Arguments

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

data.table

as.data.table

Indicate that a Parameter Value should be Tuned

Description

to_tune() creates a TuneToken object which can be assigned to the ⁠$values⁠ slot of a ParamSet as an alternative to a concrete value. This indicates that the value is not given directly but should be tuned using bbotk or mlr3tuning. If the thus parameterized object is invoked directly, without being wrapped by or given to a tuner, it will give an error.

The tuning range ParamSet that is constructed from the TuneToken values in a ParamSet's ⁠$values⁠ slot can be accessed through the ParamSet$search_space() method. This is done automatically by tuners if no tuning range is given, but it is also possible to access the ⁠$search_space()⁠ method, modify it further, and give the modified ParamSet to a tuning function (or do anything else with it, nobody is judging you).

A TuneToken represents the range over which the parameter whose ⁠$values⁠ slot it occupies should be tuned over. It can be constructed via the to_tune() function in one of several ways:

• to_tune(): Indicates a parameter should be tuned over its entire range. Only applies to finite parameters (i.e. discrete or bounded numeric parameters)

• to_tune(lower, upper, logscale): Indicates a numeric parameter should be tuned in the inclusive interval spanning lower to upper, possibly on a log scale if logscale is se to TRUE. All parameters are optional, and the parameter's own lower / upper bounds are used without log scale, by default. Depending on the parameter, integer (if it is a p_int()) or real values (if it is a p_dbl()) are used.
  lower, upper, and logscale can be given by position, except when only one of them is given, in which case it must be named to disambiguate from the following cases.
  When logscale is TRUE, then a trafo is generated automatically that transforms to the given bounds. The bounds are log()'d pre-trafo (see examples). See the logscale argument of Domain functions for more info.
  Note that "logscale" is not inherited from the Domain that the TuneToken belongs to! Defining a parameter with ⁠p_dbl(... logscale = TRUE)⁠ will not automatically give the to_tune() assigned to it log-scale.

• to_tune(levels): Indicates a parameter should be tuned through the given discrete values. levels can be any named or unnamed atomic vector or list (although in the unnamed case it must be possible to construct a corresponding character vector with distinct values using as.character).

• ⁠to_tune(<Domain>)⁠: The given Domain object (constructed e.g. with p_int() or p_fct()) indicates the range which should be tuned over. The supplied trafo function is used for parameter transformation.

• ⁠to_tune(<ParamSet>)⁠: The given ParamSet is used to tune over a single dimension. This is useful for cases where a single evaluation-time parameter value (e.g. p_uty()) is constructed from multiple tuner-visible parameters (which may not be p_uty()). If not one-dimensional, the supplied ParamSet should always contain a ⁠$extra_trafo⁠ function, which must then always return a list with a single entry.

The TuneToken object's internals are subject to change and should not be relied upon. TuneToken objects should only be constructed via to_tune(), and should only be used by giving them to ⁠$values⁠ of a ParamSet.

Usage

to_tune(..., internal = !is.null(aggr), aggr = NULL)

Arguments

Value

A TuneToken object.

See Also

Other ParamSet construction helpers: Domain(), ps()

Examples

params = ps(
  int = p_int(0, 10),
  int_unbounded = p_int(),
  dbl = p_dbl(0, 10),
  dbl_unbounded = p_dbl(),
  dbl_bounded_below = p_dbl(lower = 1),
  fct = p_fct(c("a", "b", "c")),
  uty1 = p_uty(),
  uty2 = p_uty(),
  uty3 = p_uty(),
  uty4 = p_uty(),
  uty5 = p_uty()
)

params$values = list(

  # tune over entire range of `int`, 0..10:
  int = to_tune(),

  # tune over 2..7:
  int_unbounded = to_tune(2, 7),

  # tune on a log scale in range 1..10;
  # recognize upper bound of 10 automatically, but restrict lower bound to 1:
  dbl = to_tune(lower = 1, logscale = TRUE),
  ## This is equivalent to the following:
  # dbl = to_tune(p_dbl(log(1), log(10), trafo = exp)),

  # nothing keeps us from tuning a dbl over integer values
  dbl_unbounded = to_tune(p_int(1, 10)),

  # tune over values "a" and "b" only
  fct = to_tune(c("a", "b")),

  # tune over integers 2..8.
  # ParamUty needs type information in form of p_xxx() in to_tune.
  uty1 = to_tune(p_int(2, 8)),

  # tune uty2 like a factor, trying 1, 10, and 100:
  uty2 = to_tune(c(1, 10, 100)),

  # tune uty3 like a factor. The factor levels are the names of the list
  # ("exp", "square"), but the trafo will generate the values from the list.
  # This way you can tune an objective that has function-valued inputs.
  uty3 = to_tune(list(exp = exp, square = function(x) x^2)),

  # tune through multiple parameters. When doing this, the ParamSet in tune()
  # must have the trafo that generates a list with one element and the right
  # name:
  uty4 = to_tune(ps(
    base = p_dbl(0, 1),
    exp = p_int(0, 3),
    .extra_trafo = function(x, param_set) {
      list(uty4 = x$base ^ x$exp)
    }
  )),

  # not all values need to be tuned!
  uty5 = 100
)

print(params$values)

print(params$search_space())

# Change `$values` directly and generate new `$search_space()` to play around
params$values$uty3 = 8
params$values$uty2 = to_tune(c(2, 4, 8))

print(params$search_space())

# Notice how `logscale` applies `log()` to lower and upper bound pre-trafo:
params = ps(x = p_dbl())

params$values$x = to_tune(1, 100, logscale = TRUE)

print(params$search_space())

grid = generate_design_grid(params$search_space(), 3)

# The grid is equidistant within log-bounds pre-trafo:
print(grid)

# But the values are on a log scale scale with desired bounds after trafo:
print(grid$transpose())