1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LearnerParam.R, R/makeLearnerParamFuns.R
\name{LearnerParam}
\alias{LearnerParam}
\alias{makeNumericLearnerParam}
\alias{makeNumericVectorLearnerParam}
\alias{makeIntegerLearnerParam}
\alias{makeIntegerVectorLearnerParam}
\alias{makeDiscreteLearnerParam}
\alias{makeDiscreteVectorLearnerParam}
\alias{makeLogicalLearnerParam}
\alias{makeLogicalVectorLearnerParam}
\alias{makeUntypedLearnerParam}
\alias{makeFunctionLearnerParam}
\title{Create a description object for a parameter of a machine learning
algorithm.}
\usage{
makeNumericLearnerParam(
id,
lower = -Inf,
upper = Inf,
allow.inf = FALSE,
default,
when = "train",
requires = NULL,
tunable = TRUE,
special.vals = list()
)
makeNumericVectorLearnerParam(
id,
len = as.integer(NA),
lower = -Inf,
upper = Inf,
allow.inf = FALSE,
default,
when = "train",
requires = NULL,
tunable = TRUE,
special.vals = list()
)
makeIntegerLearnerParam(
id,
lower = -Inf,
upper = Inf,
default,
when = "train",
requires = NULL,
tunable = TRUE,
special.vals = list()
)
makeIntegerVectorLearnerParam(
id,
len = as.integer(NA),
lower = -Inf,
upper = Inf,
default,
when = "train",
requires = NULL,
tunable = TRUE,
special.vals = list()
)
makeDiscreteLearnerParam(
id,
values,
default,
when = "train",
requires = NULL,
tunable = TRUE,
special.vals = list()
)
makeDiscreteVectorLearnerParam(
id,
len = as.integer(NA),
values,
default,
when = "train",
requires = NULL,
tunable = TRUE,
special.vals = list()
)
makeLogicalLearnerParam(
id,
default,
when = "train",
requires = NULL,
tunable = TRUE,
special.vals = list()
)
makeLogicalVectorLearnerParam(
id,
len = as.integer(NA),
default,
when = "train",
requires = NULL,
tunable = TRUE,
special.vals = list()
)
makeUntypedLearnerParam(
id,
default,
when = "train",
requires = NULL,
tunable = TRUE,
special.vals = list()
)
makeFunctionLearnerParam(
id,
default,
when = "train",
requires = NULL,
tunable = TRUE,
special.vals = list()
)
}
\arguments{
\item{id}{(\code{character(1)})\cr
Name of parameter.}
\item{lower}{(\code{numeric} | \code{expression})\cr
Lower bounds. A singe value of
length 1 is automatically replicated to \code{len} for vector parameters. If
\code{len = NA} you can only pass length-1 scalars. Default is \code{-Inf}.}
\item{upper}{(\code{numeric} | \code{expression})\cr
Upper bounds. A singe value of
length 1 is automatically replicated to \code{len} for vector parameters. If
\code{len = NA} you can only pass length-1 scalars. Default is \code{Inf}.}
\item{allow.inf}{(\code{logical(1)})\cr
Allow infinite values for numeric and
numericvector params to be feasible settings. Default is \code{FALSE}.}
\item{default}{(any concrete value | \code{expression})\cr
Default value used in
learner. Note: When this is a discrete parameter make sure to use a VALUE
here, not the NAME of the value. If this argument is missing, it means no
default value is available.}
\item{when}{(\code{character(1)})\cr
Specifies when parameter is used in the learner: \dQuote{train},
\dQuote{predict} or \dQuote{both}. Default is \dQuote{train}.}
\item{requires}{(\code{NULL} | \code{call} | \code{expression})\cr
States requirements on
other parameters' values, so that setting this parameter only makes sense
if its requirements are satisfied (dependent parameter). Can be an object
created either with \code{expression} or \code{quote}, the former type is
auto-converted into the later. Only really useful if the parameter is
included in a (ParamSet()). Default is \code{NULL} which means no requirements.}
\item{tunable}{(\code{logical(1)})\cr
Is this parameter tunable? Defining a
parameter to be not-tunable allows to mark arguments like, e.g.,
\dQuote{verbose} or other purely technical stuff. Note that this flag is
most likely not respected by optimizing procedures unless stated otherwise.
Default is \code{TRUE} (except for \code{untyped}, \code{function}, \code{character} and
\code{characterVector}) which means it is tunable.}
\item{special.vals}{(\code{list()})\cr
A list of special values the parameter can
except which are outside of the defined range. Default is an empty list.}
\item{len}{(\code{integer(1)})\cr
Length of vector parameter.
Can be set to \code{NA} to define a vector with unspecified length.}
\item{values}{(\code{vector} | \code{list} | \code{expression})\cr
Possible discrete values.
Instead of using a vector of atomic values, you are also allowed to pass a
list of quite \dQuote{complex} R objects, which are used as discrete
choices. If you do the latter, the elements must be uniquely named, so that
the names can be used as internal representations for the choice.}
}
\value{
\code{\link[=LearnerParam]{LearnerParam()}}.
}
\description{
This specializes \code{\link[=Param]{Param()}} by adding a few more attributes, like
a default value, whether it refers to a training or a predict function, etc.
Note that you can set \code{length} to \code{NA}
The S3 class is a \code{\link[=Param]{Param()}} which additionally stores these elements:
\describe{
\item{when \code{character(1)}}{See argument of same name.}
}
See the note in \code{\link[=Param]{Param()}} about being able to pass expressions to certain arguments.
}
|
