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https://github.com/ccao-data/lightsnip
Hard fork of curso-r/treesnip specifically for CCAO LightGBM regressions
https://github.com/ccao-data/lightsnip
lightgbm machine-learning r r-package
Last synced: about 11 hours ago
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Hard fork of curso-r/treesnip specifically for CCAO LightGBM regressions
- Host: GitHub
- URL: https://github.com/ccao-data/lightsnip
- Owner: ccao-data
- License: agpl-3.0
- Created: 2023-07-04T17:30:38.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-06-03T15:45:57.000Z (5 months ago)
- Last Synced: 2024-06-03T18:28:06.457Z (5 months ago)
- Topics: lightgbm, machine-learning, r, r-package
- Language: R
- Homepage: https://ccao-data.github.io/lightsnip/
- Size: 21.9 MB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# Lightsnip 
[](https://github.com/ccao-data/lightsnip/actions/workflows/R-CMD-check.yaml)
[](https://github.com/ccao-data/lightsnip/actions/workflows/test-coverage.yaml)
[](https://github.com/ccao-data/lightsnip/actions/workflows/lint.yaml)
[](https://github.com/ccao-data/lightsnip/actions/workflows/pre-commit.yaml)
[](https://codecov.io/gh/ccao-data/lightsnip)
Lightsnip is a hard fork of [curso-r/treesnip](https://github.com/curso-r/treesnip). It adds LightGBM bindings for parsnip and enables more advanced LightGBM features, such as early stopping. It is not intended for general use, only as a dependency for CCAO regression models.
For detailed documentation on included functions, [**visit the full reference list**](https://ccao-data.github.io/lightsnip/reference/index.html).
## Installation
You can install the released version of `lightsnip` directly from GitHub with one of the following commands:
```{r, eval=FALSE}
# Using remotes
remotes::install_github("ccao-data/lightsnip")
# Using renv
renv::install("ccao-data/lightsnip")
# Using pak
pak::pak("ccao-data/lightsnip")
# Append the @ symbol for a specific version
remotes::install_github("ccao-data/[email protected]")
```
Once it is installed, you can use it just like any other package. Simply call `library(assessr)` at the beginning of your script.
## Differences compared to [treesnip](https://github.com/curso-r/treesnip)
- Removed support for `tree` and `catboost` (LightGBM only)
- Removed classification support for LightGBM (regression only)
- Removed treesnip caps and warnings on `max_depth`, other parameters
- Removed vignettes and samples
- Remap parameters to engine args instead of parsnip model args
- Added LightGBM-specific hyperparameter functions
- Added LightGBM-specific save/load helpers
- Added recipe/fit cleaning helpers
- Force user to specify categorical columns by name, does _not_ implicitly convert factors to categoricals
- Added early stopping from xgboost
- Added more unit tests
- Fixed a number of bugs
## Basic usage with Tidymodels
Here is a quick example using `lightsnip` with a Tidymodels cross-validation workflow:
```{r message=FALSE, results='asis'}
library(dplyr)
library(lightgbm)
library(lightsnip)
library(parsnip)
library(recipes)
library(workflows)
# Create a dataset for training
mtcars_train <- mtcars %>%
dplyr::slice(1:28) %>%
sample_n(size = 500, replace = TRUE) %>%
mutate(cyl = as.factor(cyl), vs = as.factor(vs))
# Create a test set
mtcars_test <- mtcars %>%
dplyr::slice(29:32) %>%
mutate(cyl = as.factor(cyl), vs = as.factor(vs))
# Recipe to convert factors to categorical integers
rec <- recipe(mpg ~ ., mtcars_train) %>%
step_integer(all_nominal(), zero_based = TRUE)
# Split data into V-folds
resamples <- rsample::vfold_cv(mtcars_train, v = 2)
# Create a model specification. LightGBM-specific parameters are passed to
# set_engine, NOT to boost_tree
model <- parsnip::boost_tree(
trees = tune::tune()
) %>%
parsnip::set_engine(
engine = "lightgbm",
verbose = -1,
learning_rate = tune::tune(),
min_gain_to_split = tune::tune(),
feature_fraction = tune::tune(),
min_data_in_leaf = tune::tune(),
max_depth = tune::tune()
)
# Run grid search
search <- tune::tune_grid(
parsnip::set_mode(model, "regression"),
preprocessor = rec,
resamples = resamples,
param_info = model %>%
hardhat::extract_parameter_set_dials() %>%
stats::update(
learning_rate = learning_rate(),
min_gain_to_split = min_gain_to_split(),
feature_fraction = feature_fraction(),
min_data_in_leaf = min_data_in_leaf(c(1L, 2L)),
max_depth = max_depth(c(3L, 6L))
),
grid = 2,
metrics = yardstick::metric_set(yardstick::rmse)
)
# Finalize model
final <- model %>%
tune::finalize_model(tune::select_best(search)) %>%
parsnip::set_mode("regression") %>%
parsnip::fit(mpg ~ ., bake(prep(rec), mtcars_train))
# Predict on test set
mtcars_test %>%
mutate(pred_mpg = predict(final, bake(prep(rec), .))$.pred) %>%
select(actual_mpg = mpg, pred_mpg) %>%
knitr::kable(digits = 2)
```