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# logging function for tuning and feature selection
# has 2 stages:
# 1: After point to eval is selected
# 2: after evaluation
logFunDefault = function(learner, task, resampling, measures, par.set, control, opt.path, dob,
x.string, y, remove.nas, stage, prev.stage, prefixes) {
if (stage == 1L) {
start.time = Sys.time()
messagef("[%s] %i: %s", prefixes[stage], dob, x.string)
return(list(start.time = start.time))
} else if (stage == 2L) {
end.time = Sys.time()
diff.time = difftime(time1 = end.time, time2 = prev.stage$start.time, units = "mins")
messagef("[%s] %i: %s; time: %.1f min",
prefixes[stage], dob, perfsToString(y), diff.time[[1]])
return(NULL)
}
}
logFunMemory = function(learner, task, resampling, measures, par.set, control, opt.path, dob,
x.string, y, remove.nas, stage, prev.stage, prefixes) {
if (stage == 1L) {
gc()
gc()
gc()
start.time = Sys.time()
messagef("[%s] %i: %s", prefixes[stage], dob, x.string)
return(list(start.time = start.time))
} else if (stage == 2L) {
# get cur mem and max mem and sum ncells and vcells.
# we had a diff here with gc(reset=TRUE). but this is bad the user cant avoid this, it might even be
# not allowed on CRAN
mem = colSums(gc()[, c(2L, 6L)])
end.time = Sys.time()
diff.time = difftime(time1 = end.time, time2 = prev.stage$start.time, units = "mins")
messagef("[%s] %i: %s; time: %.1f min; memory: %.0fMb use, %.0fMb max",
prefixes[stage], dob, perfsToString(y), diff.time[[1]], mem[1L], mem[2L])
return(NULL)
}
}
logFunTune = function(learner, task, resampling, measures, par.set, control, opt.path, dob,
x, y, remove.nas, stage, prev.stage) {
x.string = paramValueToString(par.set, x, show.missing.values = !remove.nas)
# shorten tuning logging a bit. we remove the sel.learner prefix from params
if (inherits(learner, "ModelMultiplexer")) {
x.string = stri_replace_all(x.string, "", regex = stri_paste(x$selected.learner, "\\."))
}
logFunDefault(learner, task, resampling, measures, par.set, control, opt.path, dob,
x.string, y, remove.nas, stage, prev.stage, prefixes = c("Tune-x", "Tune-y")
)
}
logFunTuneMemory = function(learner, task, resampling, measures, par.set, control, opt.path, dob,
x, y, remove.nas, stage, prev.stage) {
x.string = paramValueToString(par.set, x, show.missing.values = !remove.nas)
# shorten tuning logging a bit. we remove the sel.learner prefix from params
if (inherits(learner, "ModelMultiplexer")) {
x.string = stri_replace_all(x.string, "", regex = stri_paste(x$selected.learner, "\\."))
}
logFunMemory(learner, task, resampling, measures, par.set, control, opt.path, dob,
x.string, y, remove.nas, stage, prev.stage, prefixes = c("Tune-x", "Tune-y")
)
}
logFunFeatSel = function(learner, task, resampling, measures, par.set, control, opt.path, dob,
x, y, remove.nas, stage, prev.stage) {
x.string = sprintf("%s (%i bits)", clipString(collapse(x, ""), 80L), sum(x))
logFunDefault(learner, task, resampling, measures, par.set, control, opt.path, dob,
x.string, y, remove.nas, stage, prev.stage, prefixes = c("FeatSel-x", "FeatSel-y")
)
}
logFunFeatSelMemory = function(learner, task, resampling, measures, par.set, control, opt.path, dob,
x, y, remove.nas, stage, prev.stage) {
x.string = sprintf("%s (%i bits)", clipString(collapse(x, ""), 80L), sum(x))
logFunMemory(learner, task, resampling, measures, par.set, control, opt.path, dob,
x.string, y, remove.nas, stage, prev.stage, prefixes = c("FeatSel-x", "FeatSel-y")
)
}
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