https://github.com/mayer79/splittools
Light weight R package to do fast data splitting for cross-validation or train/valid/test splits
https://github.com/mayer79/splittools
cross-validation machine-learning rstats time-series validation
Last synced: 10 months ago
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Light weight R package to do fast data splitting for cross-validation or train/valid/test splits
- Host: GitHub
- URL: https://github.com/mayer79/splittools
- Owner: mayer79
- License: gpl-2.0
- Created: 2020-01-03T11:44:20.000Z (over 6 years ago)
- Default Branch: main
- Last Pushed: 2025-04-06T09:21:24.000Z (about 1 year ago)
- Last Synced: 2025-04-06T10:20:24.377Z (about 1 year ago)
- Topics: cross-validation, machine-learning, rstats, time-series, validation
- Language: R
- Homepage: https://mayer79.github.io/splitTools/
- Size: 1020 KB
- Stars: 13
- Watchers: 2
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE.md
Awesome Lists containing this project
README
# {splitTools} 
[](https://github.com/mayer79/splitTools/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/mayer79/splitTools)
[](https://cran.r-project.org/package=splitTools)
[](https://cran.r-project.org/package=splitTools)
[](https://cran.r-project.org/package=splitTools)
## Overview
{splitTools} is a toolkit for fast data splitting. It does not have any dependencies.
Its two main functions `partition()` and `create_folds()` support
- data partitioning (e.g. into training, validation and test),
- creating (in- or out-of-sample) folds for cross-validation (CV),
- creating *repeated* folds for CV,
- stratified splitting,
- grouped splitting as well as
- blocked splitting (if the sequential order of the data should be retained).
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`.
## Installation
```r
# From CRAN
install.packages("splitTools")
# Development version
devtools::install_github("mayer79/splitTools")
```
## Usage
``` r
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.