`pSS`

, short for “ParamSet Sugar”, is a shorthand API for `makeParamSet`

which enables entry of `ParamSet`

s in short form. It behaves similarly to
`makeParamSet`

, but instead of having to construct each parameter individually,
the parameters can be given in shorthand form with a convenient syntax, making use of R's
nonstandard evaluation.

This makes definition of `ParamSet`

s shorter and more readable.

The difference between `pSS`

and `pSSLrn`

is only in the default value of `.pss.learner.params`

being `FALSE`

for the former and `TRUE`

for the latter.

pSS(..., .pss.learner.params = FALSE, .pss.env = parent.frame()) pSSLrn(..., .pss.learner.params = TRUE, .pss.env = parent.frame())

... | Parameters, see Details below. |
---|---|

.pss.learner.params | [ |

.pss.env | [ |

The arguments are of the form

`name = default: type range [^ dimension] [settings]`

.

** name** is any valid R identifier name.

** = default** Determines the 'default' setting
in

`makeXXXParam`

. Note that this is different from an R function parameter
default value, in that it serves only as information to the user and does not set the
parameter to this value if it is not given. To define ‘no default’, use `NA`

or
leave the “= default” part out. Leaving it out can cause problems when R's static
type checker verifies a package, so this is `(NA)`

in parentheses)** type** is one of
“integer”, “numeric”, “logical”, “discrete”, “funct”, “character”, “untyped”.
Each of these types leads to a

`Param`

or `LearnerParam`

of the given type to be created.
Note that “character” is not available if ‘Learner’-parameters are created.** range** is optional and only used for

`[valuelist]`

with `valuelist`

evaluating to a list,
or of the form `[value1, value2, ...]`

, creating a discrete parameter of character
or numeric values according to `value1`

,
`value2`

etc. If `type`

is one of “integer” or “numeric”,
`range`

is of the form `[lowBound, upBound]`

, where `lowBound`

and `upBound`

must either be numerical (or integer) values indicating the
lower and upper bound, or may be missing (indicating the absence of a bound). To indicate
an exclusive bound, prefix the values with a tilde (“~”). For a “numeric” variable, to
indicate an unbounded value which may not be infinite, you can use `~Inf`

or `~-Inf`

,
or use tilde-dot (“~.”).** ^ dimension** is optionally determining the dimension of a ‘vector’ parameter.
If it is absent, the result is a normal

`Param`

or `LearnerParam`

, if it is present,
the result is a `Vector(Learner)Param`

. Note that a one-dimensional `Vector(Learner)Param`

is distinct from a normal `(Learner)Param`

.** settings** may be a collection of further settings to supply to

`makeXXXParam`

and is optional. To specify one or more settings, put in double square brackets (`[[`

, `]]`

),
and comma-separate settings if more than one is present.pSSLrn(a = NA: integer [~0, ]^2 [[requires = expression(b != 0)]], b = -10: numeric [~., 0], c: discrete [x, y, 1], d: logical, e: integer)#> Type len Def Constr Req Tunable Trafo #> a integervector 2 - 1 to Inf Y TRUE - #> b numeric - -10 -Inf to 0 - TRUE - #> c discrete - - x,y,1 - TRUE - #> d logical - - - - TRUE - #> e integer - - -Inf to Inf - TRUE -# is equivalent to makeParamSet( makeIntegerVectorLearnerParam("a", len = 2, lower = 1, # note exclusive bound upper = Inf, requires = expression(b != 0)), makeNumericLearnerParam("b", lower = -Inf, upper = 0, allow.inf = FALSE, default = -10), # note infinite value is prohibited. makeDiscreteLearnerParam("c", values = list(x = "x", y = "y", `1` = 1)), makeLogicalLearnerParam("d"), makeIntegerLearnerParam("e"))#> Type len Def Constr Req Tunable Trafo #> a integervector 2 - 1 to Inf Y TRUE - #> b numeric - -10 -Inf to 0 - TRUE - #> c discrete - - x,y,1 - TRUE - #> d logical - - - - TRUE - #> e integer - - -Inf to Inf - TRUE -