resamplerR is like M.Kuhn`s rsample, but all resamples are realised as plain data.table objects. This package aims to be as easy to extend as possible.
You can install dev version of resampleR from GitHub with:
devtools::install_github("statist-bhfz/resampleR")This is a basic example which shows you how to solve a common problem:
library(resampleR)
#> Loading required package: data.table
cv_base(as.data.table(iris), "Species")
#> split_1 split_2 split_3 split_4 split_5
#> 1: 0 0 0 0 1
#> 2: 0 0 1 0 0
#> 3: 0 0 1 0 0
#> 4: 0 0 0 1 0
#> 5: 1 0 0 0 0
#> ---
#> 146: 1 0 0 0 0
#> 147: 0 0 1 0 0
#> 148: 1 0 0 0 0
#> 149: 1 0 0 0 0
#> 150: 0 0 0 1 0
dt <- data.table(
user = rep(1:100, each = 5),
date = as.POSIXct(rep(seq(1.8*10e8, 1.8*10e8 + 388800, by = 86400), 100),
origin = "1960-01-01"),
target = rnorm(5e2)
)
cv_split_group_kfold(dt, "target", "user")
#> split_1 split_2 split_3 split_4 split_5
#> 1: 0 0 1 0 0
#> 2: 0 0 1 0 0
#> 3: 0 0 1 0 0
#> 4: 0 0 1 0 0
#> 5: 0 0 1 0 0
#> ---
#> 496: 0 1 0 0 0
#> 497: 0 1 0 0 0
#> 498: 0 1 0 0 0
#> 499: 0 1 0 0 0
#> 500: 0 1 0 0 0
cv_split_temporal(dt, "target", "user", "date")
#> split_1 split_2 split_3 split_4 split_5
#> 1: 0 0 0 0 0
#> 2: 0 0 0 0 0
#> 3: 0 0 0 0 0
#> 4: 0 0 0 0 0
#> 5: 0 1 1 1 0
#> ---
#> 496: 0 0 0 0 0
#> 497: 0 0 0 0 0
#> 498: 0 0 0 0 0
#> 499: 0 0 0 0 0
#> 500: 1 0 1 0 0
repeated_kfold(expr = quote(cv_base(as.data.table(iris), "Species")))
#> rep_1_fold_1 rep_1_fold_2 rep_1_fold_3 rep_1_fold_4 rep_1_fold_5
#> 1: 1 0 0 0 0
#> 2: 0 0 1 0 0
#> 3: 0 0 0 0 1
#> 4: 0 1 0 0 0
#> 5: 1 0 0 0 0
#> ---
#> 146: 0 0 0 0 1
#> 147: 0 0 0 0 1
#> 148: 1 0 0 0 0
#> 149: 0 0 0 0 1
#> 150: 1 0 0 0 0
#> rep_2_fold_1 rep_2_fold_2 rep_2_fold_3 rep_2_fold_4 rep_2_fold_5
#> 1: 1 0 0 0 0
#> 2: 0 0 0 1 0
#> 3: 0 0 0 1 0
#> 4: 0 0 0 0 1
#> 5: 1 0 0 0 0
#> ---
#> 146: 0 0 0 1 0
#> 147: 0 1 0 0 0
#> 148: 0 0 0 0 1
#> 149: 0 0 1 0 0
#> 150: 0 0 1 0 0