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
|
#' @export
makeRLearner.multilabel.cforest = function() {
makeRLearnerMultilabel(
cl = "multilabel.cforest",
package = "party",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "ntree", lower = 1L, default = 500L),
makeIntegerLearnerParam(id = "mtry", lower = 1L, default = 5L),
makeLogicalLearnerParam(id = "replace", default = FALSE),
makeNumericLearnerParam(id = "fraction", lower = 0, upper = 1, default = 0.632,
requires = quote(replace == FALSE)),
makeLogicalLearnerParam(id = "trace", default = FALSE, tunable = FALSE),
makeDiscreteLearnerParam(id = "teststat", values = c("quad", "max"), default = "quad"),
makeDiscreteLearnerParam(id = "testtype",
values = c("Bonferroni", "MonteCarlo", "Univariate", "Teststatistic"),
default = "Univariate"),
makeNumericLearnerParam(id = "mincriterion", lower = 0, default = 0),
makeIntegerLearnerParam(id = "minsplit", lower = 1L, default = 20L),
makeIntegerLearnerParam(id = "minbucket", lower = 1L, default = 7L),
makeLogicalLearnerParam(id = "stump", default = FALSE),
makeIntegerLearnerParam(id = "nresample", lower = 1L, default = 9999L),
makeIntegerLearnerParam(id = "maxsurrogate", lower = 0L, default = 0L),
makeIntegerLearnerParam(id = "maxdepth", lower = 0L, default = 0L),
makeLogicalLearnerParam(id = "savesplitstats", default = FALSE, tunable = FALSE)
),
properties = c("numerics", "factors", "ordered", "missings", "weights", "prob"),
par.vals = list(),
name = "Random forest based on conditional inference trees",
short.name = "cforest",
callees = c("cforest", "cforest_control", "ctree_control")
)
}
#' @export
trainLearner.multilabel.cforest = function(.learner, .task, .subset, .weights = NULL,
ntree, mtry, replace, fraction, trace, teststat, testtype, mincriterion,
minsplit, minbucket, stump, nresample, maxsurrogate,
maxdepth, savesplitstats, ...) {
d = getTaskData(.task, .subset)
f = getTaskFormula(.task)
defaults = getDefaults(getParamSet(.learner))
if (missing(teststat)) teststat = defaults$teststat
if (missing(testtype)) testtype = defaults$testtype
if (missing(mincriterion)) mincriterion = defaults$mincriterion
if (missing(replace)) replace = defaults$replace
if (missing(fraction)) fraction = defaults$fraction
ctrl = learnerArgsToControl(party::cforest_control, ntree, mtry, replace, fraction,
trace, teststat, testtype, mincriterion, minsplit, minbucket, stump,
nresample, maxsurrogate, maxdepth, savesplitstats)
party::cforest(f, data = d, controls = ctrl, weights = .weights, ...)
}
#' @export
predictLearner.multilabel.cforest = function(.learner, .model, .newdata, ...) {
p = predict(.model$learner.model, newdata = .newdata, type = "prob", ...)
p = do.call(rbind, p)
if (.learner$predict.type == "response") {
p = p >= 0.5
} else {
p
}
}
|
