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Improved logic in stratified CV to guess class/regr

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Somewhat more robust and clear logic in stratified CV to guess classification/regression settings. Allows to accomodate custom objectives (classification is assumed when number of unique values in labels <= 5).
This commit is contained in:
Vadim Khotilovich 2015-05-01 15:08:08 -05:00
parent bab7b58d94
commit f325930bd9
1 changed files with 18 additions and 5 deletions
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23
R-package/R/utils.R
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@ -211,6 +211,7 @@ xgb.iter.eval <- function(booster, watchlist, iter, feval = NULL, prediction = F
}
return(msg)
}
#------------------------------------------
# helper functions for cross validation
#
@ -223,11 +224,23 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
randidx <- sample(1 : xgb.numrow(dall))
if (stratified & length(y) == length(randidx)) {
y <- y[randidx]
# By default assume that y is a classification label,
# and only leave it numeric for the reg:linear objective.
# WARNING: if there would be any other objectives with truly
# numerical labels, they currently would not be treated correctly!
if (param[['objective']] != 'reg:linear') y <- factor(y)
#
# WARNING: some heuristic logic is employed to identify classification setting!
#
# For classification, need to convert y labels to factor before making the folds,
# and then do stratification by factor levels.
# For regression, leave y numeric and do stratification by quantiles.
n_uniq <- length(unique(y))
if (exists('objective', where=param)) {
# If 'objective' provided in params, assume that y is a classification label
# unless objective is reg:linear
if (param[['objective']] != 'reg:linear') y <- factor(y)
} else {
# If no 'objective' given in params, it means that user either wants to use
# the default 'reg:linear' objective or has provided a custom obj function.
# Here, assume classification setting when y has 5 or less unique values:
if (length(unique(y)) <= 5) y <- factor(y)
}
folds <- xgb.createFolds(y, nfold)
} else {
# make simple non-stratified folds