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{splitTools} is a toolkit for fast data splitting. It does not have any dependencies.
Its two main functions partition() and
create_folds() support
The function create_timefolds() does time-series
splitting where the out-of-sample data follows the (extending or moving)
in-sample data.
The result of create_folds() can be directly passed to
the folds argument in CV functions of XGBoost or LightGBM.
Since these functions expect out-of-sample indices, set the
option invert = TRUE.
# From CRAN
install.packages("splitTools")
# Development version
devtools::install_github("mayer79/splitTools")library(splitTools)
p <- c(train = 0.5, valid = 0.25, test = 0.25)
# Train/valid/test indices for iris data stratified by Species
str(inds <- partition(iris$Species, p, seed = 1))
# List of 3
# $ train: int [1:73] 1 3 5 7 8 10 12 13 14 15 ...
# $ valid: int [1:38] 4 9 19 21 27 28 29 30 32 35 ...
# $ test : int [1:39] 2 6 11 16 18 22 26 37 38 40 ...
# Same, but different output interface
head(inds <- partition(iris$Species, p, split_into_list = FALSE, seed = 1))
# [1] train test train valid train test
# Levels: train valid test
# In-sample indices for 5-fold CV (stratified by Species)
str(inds <- create_folds(iris$Species, k = 5, seed = 1))
# List of 5
# $ Fold1: int [1:120] 2 4 5 6 7 8 9 10 11 15 ...
# $ Fold2: int [1:120] 1 2 3 4 5 6 9 10 11 12 ...
# $ Fold3: int [1:120] 1 2 3 4 6 7 8 9 11 12 ...
# $ Fold4: int [1:120] 1 3 5 6 7 8 10 11 12 13 ...
# $ Fold5: int [1:120] 1 2 3 4 5 7 8 9 10 12 ...
# In-sample indices for 3 times repeated 5-fold CV (stratified by Species)
str(inds <- create_folds(iris$Species, k = 5, m_rep = 3, seed = 1))
# List of 15
# $ Fold1.Rep1: int [1:120] 2 4 5 6 7 8 9 10 11 15 ...
# $ Fold2.Rep1: int [1:120] 1 2 3 4 5 6 9 10 11 12 ...
# $ Fold3.Rep1: int [1:120] 1 2 3 4 6 7 8 9 11 12 ...
# $ Fold4.Rep1: int [1:120] 1 3 5 6 7 8 10 11 12 13 ...
# $ Fold5.Rep1: int [1:120] 1 2 3 4 5 7 8 9 10 12 ...
# $ Fold1.Rep2: int [1:120] 1 2 3 4 5 6 8 9 11 12 ...
# $ Fold2.Rep2: int [1:120] 1 3 6 7 8 9 10 12 13 14 ...
# [...]
# Indices for time-series splitting
str(inds <- create_timefolds(1:100, k = 5))
# List of 5
# $ Fold1:List of 2
# ..$ insample : int [1:17] 1 2 3 4 5 6 7 8 9 10 ...
# ..$ outsample: int [1:17] 18 19 20 21 22 23 24 25 26 27 ...
# $ Fold2:List of 2
# ..$ insample : int [1:34] 1 2 3 4 5 6 7 8 9 10 ...
# ..$ outsample: int [1:17] 35 36 37 38 39 40 41 42 43 44 ...
# $ Fold3:List of 2
# [...]For more details, check out the vignette.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.