ParamSet

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

assert_values: (logical(1))
Should values be checked for validity during assigment to active binding $values? Default is TRUE, only switch this off if you know what you are doing.

Active bindings

data: (data.table) data.table representation of the ParamSet.
values: (named list())
Currently set / fixed parameter values. Settable, and feasibility of values will be checked when you set them. You do not have to set values for all parameters, but only for a subset. When you set values, all previously set values will be unset / removed.
tags: (named list() of character())
Can be used to group and subset parameters. Named with parameter IDs.
params: (named list())
data.table representing the combined Domain objects used to construct the ParamSet. Used for internal purpuses. Its use by external code is deprecated.
domains: (named list of Domain) List of Domain objects that could be used to initialize this ParamSet.
extra_trafo: (function(x, param_set))
Transformation function. Settable. User has to pass a function(x), of the form
(named list(), ParamSet) -> named list().
The function is responsible to transform a feasible configuration into another encoding, before potentially evaluating the configuration with the target algorithm. For the output, not many things have to hold. It needs to have unique names, and the target algorithm has to accept the configuration. For convenience, the self-paramset is also passed in, if you need some info from it (e.g. tags). Is NULL by default, and you can set it to NULL to switch the transformation off.
constraint: (function(x))
Constraint function. Settable. This function must evaluate a named list() of values and determine whether it satisfies constraints, returning a scalar logical(1) value.
deps: (data.table::data.table())
Table has cols id (character(1)) and on (character(1)) and cond (Condition). Lists all (direct) dependency parents of a param, through parameter IDs. Internally created by a call to add_dep. Settable, if you want to remove dependencies or perform other changes.
length: (integer(1))
Number of contained parameters.
is_empty: (logical(1))
Is the ParamSet empty? Named with parameter IDs.
has_trafo: (logical(1))
Whether a trafo function is present, in parameters or in extra_trafo.
has_extra_trafo: (logical(1))
Whether extra_trafo is set.
has_deps: (logical(1))
Whether the parameter dependencies are present
has_constraint: (logical(1))
Whether parameter constraint is set.
all_numeric: (logical(1))
Is TRUE if all parameters are p_dbl() or p_int().
all_categorical: (logical(1))
Is TRUE if all parameters are p_fct() and p_lgl().
all_bounded: (logical(1))
Is TRUE if all parameters are bounded.
class: (named character())
Classes of contained parameters. Named with parameter IDs.
lower: (named double())
Lower bounds of numeric parameters (NA for non-numerics). Named with parameter IDs.
upper: (named double())
Upper bounds of numeric parameters (NA for non-numerics). Named with parameter IDs.
levels: (named list() of character)
Allowed levels of categorical parameters (NULL for non-categoricals). Named with parameter IDs.
storage_type: (character())
Data types of parameters when stored in tables. Named with parameter IDs.
special_vals: (named list() of list())
Special values for all parameters. Named with parameter IDs.
default: (named list())
Default values of all parameters. If no default exists, element is not present. Named with parameter IDs.
has_trafo_param: (logical())
Whether trafo is set for any parameter.
is_logscale: (logical())
Whether trafo was set to logscale during construction.
Note that this only refers to the logscale flag set during construction, e.g. p_dbl(logscale = TRUE). If the parameter was set to logscale manually, e.g. through p_dbl(trafo = exp), this is_logscale will be FALSE.
nlevels: (named integer())
Number of distinct levels of parameters. Inf for double parameters or unbounded integer parameters. Named with param IDs.
is_number: (named logical())
Whether parameter is p_dbl() or p_int(). Named with parameter IDs.
is_categ: (named logical())
Whether parameter is p_fct() or p_lgl(). Named with parameter IDs.
is_bounded: (named logical())
Whether parameters have finite bounds. Named with parameter IDs.

Methods

Method `new()`

Creates a new instance of this R6 class.

Usage

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

Arguments

params: (named list())
List of Domain, named with their respective ID.
allow_dangling_dependencies: (character(1))
Whether dependencies depending on parameters that are not present should be allowed. A parameter x having depends = y == 0 if y is not present would usually throw an error, but if dangling dependencies are allowed, the dependency is added regardless. This is mainly for internal use.

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

class: (character())
Typically a subset of "ParamDbl", "ParamInt", "ParamFct", "ParamLgl", "ParamUty". Other classes are possible if implemented by 3rd party packages. Return only IDs of dimensions with the given class.
tags: (character()). Return only IDs of dimensions that have all tags given in this argument.
any_tags: (character()). Return only IDs of dimensions that have at least one of the tags given in this argument.

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

class: (character()). See $ids().
tags: (character()). See $ids().
any_tags: (character()). See $ids().
type: (character(1))
Return values "with_token" (i.e. all values),
check_required: (logical(1))
Check if all required parameters are set?
remove_dependencies: (logical(1))
If TRUE, set values with dependencies that are not fulfilled to NULL.

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

...: (any)
Named parameter values.
.values: (named list())
Named list with parameter values. Names must not already appear in ....
.insert: (logical(1))
Whether to insert the values (old values are being kept, if not overwritten), or to replace all values. Default is TRUE.

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

x: (named list() | data.frame)
The value(s) to be transformed.
param_set: (ParamSet)
Passed to extra_trafo(). Note that the extra_trafo of self is used, not the extra_trafo of the ParamSet given in the param_set argument. In almost all cases, the default param_set = self should be used.

Method `aggr_internal_tuned_values()`

Aggregate parameter values according to their aggregation rules.

Usage

ParamSet$aggr_internal_tuned_values(x)

Arguments

x: (named list() of list()s)
The value(s) to be aggregated. Names are parameter values. The aggregation function is selected based on the parameter.

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

ids: (character())
The ids of the parameters for which to disable internal tuning.

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

search_space: (ParamSet)
The internal search space.

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

x: (named list())
The value to test.
assert_value: (logical(1))
Whether to verify that x satisfies the bounds and types given by this ParamSet. Should be TRUE unless this was already checked before.

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

x: (data.table)
The values to test.
assert_value: (logical(1))
Whether to verify that x satisfies the bounds and types given by this ParamSet. Should be TRUE unless this was already checked before.

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

xs: (named list()).
check_strict: (logical(1))
Whether to check that constraints and dependencies are satisfied.
sanitize: (logical(1))
Whether to move values that are slightly outside bounds to valid values. These values are accepted independent of sanitize (depending on the tolerance arguments of p_dbl() and p_int()) . If sanitize is TRUE, the additional effect is that, should checks pass, the sanitized values of xs are added to the result as attribute "sanitized".

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

xs: (named list()).

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

xs: (named list()).
check_strict: (logical(1))
Whether to check that constraints and dependencies are satisfied.

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

xs: (named list()).
check_strict: (logical(1))
Whether to check that constraints and dependencies are satisfied.
.var.name: (character(1))
Name of the checked object to print in error messages.
Defaults to the heuristic implemented in vname.
sanitize: (logical(1))
Whether to move values that are slightly outside bounds to valid values. These values are accepted independent of sanitize (depending on the tolerance arguments of p_dbl() and p_int()) . If sanitize is TRUE, the additional effect is that xs is converted to within bounds.

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

xdt: (data.table::data.table | data.frame()).
check_strict: (logical(1))
Whether to check that constraints and dependencies are satisfied.

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

xdt: (data.table::data.table).
check_strict: (logical(1))
Whether to check that constraints and dependencies are satisfied.

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

xdt: (data.table::data.table).
check_strict: (logical(1))
Whether to check that constraints and dependencies are satisfied.
.var.name: (character(1))
Name of the checked object to print in error messages.
Defaults to the heuristic implemented in vname.

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

x: (matrix | data.frame)
Values to map. Column names must be a subset of the names of parameters.

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

id: (character(1)).

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

ids: (character()).
allow_dangling_dependencies: (logical(1))
Whether to allow subsets that cut across parameter dependencies. Dependencies that point to dropped parameters are kept (but will be "dangling", i.e. their "on" will not be present).
keep_constraint: (logical(1))
Whether to keep the $constraint function.

Returns

ParamSet.

Method `subspaces()`

Create new one-dimensional ParamSets for each dimension.

Usage

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

Arguments

ids: (character())
IDs for which to create ParamSets. Defaults to all IDs.

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

values: (named list): optional named list of TuneToken objects to convert, in place of $values.

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

id: (character(1)).
on: (character(1)).
cond: (Condition).
allow_dangling_dependencies: (logical(1)): Whether to allow dependencies on parameters that are not present.

Method `format()`

Helper for print outputs.

Usage

ParamSet$format()

Arguments

...: (ignored).

Method `print()`

Printer.

Usage

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

Arguments

...: (ignored).
hide_cols: (character())
Which fields should not be printed? Default is "levels", "is_bounded", "special_vals", "tags", and "storage_type".

Method `clone()`

The objects of this class are cloneable with this method.

Usage

ParamSet$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

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"))
#> [1] TRUE

pset$check(list(d = 2.1, f = "d"))
#> [1] "f: Must be element of set {'a','b','c'}, but is 'd'"

S3 methods and type converters

Public fields

Active bindings

Methods

Public methods

Method new()

Usage

Arguments

Method ids()

Usage

Arguments

Returns

Method get_values()

Usage

Arguments

Returns

Method set_values()

Usage

Arguments

Method trafo()

Usage

Arguments

Method aggr_internal_tuned_values()

Usage

Arguments

Returns

Method disable_internal_tuning()

Usage

Arguments

Returns

Method convert_internal_search_space()

Usage

Arguments

Returns

Method test_constraint()

Usage

Arguments

Returns

Method test_constraint_dt()

Usage

Arguments

Returns

Method check()

Usage

Arguments

Returns

Method check_dependencies()

Usage

Arguments

Returns

Method test()

Usage

Arguments

Returns

Method assert()

Usage

Arguments

Returns

Method check_dt()

Usage

Arguments

Returns

Method test_dt()

Usage

Arguments

Returns

Method assert_dt()

Usage

Arguments

Returns

Method qunif()

Usage

Arguments

Returns

Method get_domain()

Usage

Arguments

Returns

Method subset()

Method `new()`

Method `ids()`

Method `get_values()`

Method `set_values()`

Method `trafo()`

Method `aggr_internal_tuned_values()`

Method `disable_internal_tuning()`

Method `convert_internal_search_space()`

Method `test_constraint()`

Method `test_constraint_dt()`

Method `check()`

Method `check_dependencies()`

Method `test()`

Method `assert()`

Method `check_dt()`

Method `test_dt()`

Method `assert_dt()`

Method `qunif()`

Method `get_domain()`

Method `subset()`

Method `subspaces()`

Method `flatten()`

Method `search_space()`

Method `add_dep()`

Method `format()`

Method `print()`

Method `clone()`