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
|
#' @export
makeRLearner.classif.penalized = function() {
makeRLearnerClassif(
cl = "classif.penalized",
package = "!penalized",
par.set = makeParamSet(
makeNumericLearnerParam(id = "lambda1", default = 0, lower = 0),
makeNumericLearnerParam(id = "lambda2", default = 0, lower = 0),
makeLogicalLearnerParam(id = "fusedl", default = FALSE),
makeUntypedLearnerParam(id = "unpenalized", tunable = FALSE),
makeLogicalVectorLearnerParam(id = "positive", default = FALSE),
makeNumericVectorLearnerParam(id = "startbeta"),
makeNumericVectorLearnerParam(id = "startgamma"),
# untyped here because one can also pass "Park" to steps
makeUntypedLearnerParam(id = "steps", default = 1L, tunable = FALSE),
makeNumericLearnerParam(id = "epsilon", lower = 0, default = 1e-10),
makeIntegerLearnerParam(id = "maxiter", lower = 1L),
makeLogicalLearnerParam(id = "standardize", default = FALSE),
makeLogicalLearnerParam(id = "trace", default = TRUE, tunable = FALSE)
),
par.vals = list(trace = FALSE),
properties = c("twoclass", "numerics", "factors", "ordered", "prob"),
name = "Penalized Logistic Regression",
short.name = "penalized",
note = "trace=FALSE was set by default to disable logging output.",
callees = "penalized"
)
}
#' @export
trainLearner.classif.penalized = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskFormula(.task)
penalized::penalized(f, data = getTaskData(.task, .subset), model = "logistic", ...)
}
#' @export
predictLearner.classif.penalized = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
levs = .model$task.desc$class.levels
# FIXME: should be removed, reported in issue 840
m@formula$unpenalized[[2L]] = as.symbol(.model$task.desc$target)
.newdata[, .model$task.desc$target] = 0
pred = penalized::predict(m, data = .newdata, ...)
if (.learner$predict.type == "prob") {
propVectorToMatrix(pred, levs)
} else {
as.factor(ifelse(pred > 0.5, levs[2L], levs[1L]))
}
}
|
