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
|
makeHomogeneousEnsemble = function(id, type, next.learner, package, par.set = makeParamSet(),
learner.subclass, model.subclass, ...) {
makeBaseWrapper(id, type, next.learner, package, par.set,
learner.subclass = c(learner.subclass, "HomogeneousEnsemble"),
model.subclass = c(model.subclass, "HomogeneousEnsembleModel"),
...)
}
############################## HomogeneousEnsembleModel ##############################
#' @export
# if ANY model in the list is broken --> failure
isFailureModel.HomogeneousEnsembleModel = function(model) {
mods = getLearnerModel(model, more.unwrap = FALSE)
any(vlapply(mods, isFailureModel))
}
#' @export
getFailureModelMsg.HomogeneousEnsembleModel = function(model) {
mods = getLearnerModel(model, more.unwrap = FALSE)
msgs = vcapply(mods, getFailureModelMsg)
j = which.first(!is.na(msgs))
ifelse(j == 0L, NA_character_, msgs[j])
}
#' @export
getFailureModelDump.HomogeneousEnsembleModel = function(model) {
mods = getLearnerModel(model, more.unwrap = FALSE)
msgs = lapply(mods, getFailureModelDump)
j = which.first(!is.null(msgs))
ifelse(j == 0L, NULL, msgs[[j]])
}
#' Deprecated, use `getLearnerModel` instead.
#' @param model Deprecated.
#' @param learner.models Deprecated.
#' @export
getHomogeneousEnsembleModels = function(model, learner.models = FALSE) {
.Deprecated("getLearnerModel")
getLearnerModel(model, more.unwrap = learner.models)
}
#' @export
getLearnerModel.HomogeneousEnsembleModel = function(model, more.unwrap = FALSE) {
ms = model$learner.model$next.model
if (more.unwrap) {
extractSubList(ms, "learner.model", simplify = FALSE)
} else {
ms
}
}
############################## helpers ##############################
# internal mini helper: return a matrix of predictions, either numeric for regr or character for classif
# rows = newdata points, cols = ensembles members
# does only work for responses, not probs, se, etc
predictHomogeneousEnsemble = function(.learner, .model, .newdata, .subset = NULL, ...) {
models = getLearnerModel(.model, more.unwrap = FALSE)
# for classif we convert factor to char, nicer to handle later on
preds = lapply(models, function(mod) {
p = predict(mod, newdata = .newdata, subset = .subset, ...)$data$response
if (is.factor(p)) {
p = as.character(p)
}
return(p)
})
do.call(cbind, preds)
}
# call this at end of trainLearner.CostSensRegrWrapper
# FIXME: potentially remove this when ChainModel is removed
makeHomChainModel = function(learner, models) {
makeChainModel(next.model = models, cl = c(learner$model.subclass, "HomogeneousEnsembleModel"))
}
|
