lightgbm: saveRDS.lgb.Booster – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

saveRDS.lgb.Booster

saveRDS for lgb.Booster models

Description

Attempts to save a model using RDS. Has an additional parameter (raw) which decides whether to save the raw model or not.

Usage

saveRDS.lgb.Booster(
  object,
  file,
  ascii = FALSE,
  version = NULL,
  compress = TRUE,
  refhook = NULL,
  raw = TRUE
)

Arguments

`object`	R object to serialize.
`file`	a connection or the name of the file where the R object is saved to or read from.
`ascii`	a logical. If TRUE or NA, an ASCII representation is written; otherwise (default), a binary one is used. See the comments in the help for save.
`version`	the workspace format version to use. `NULL` specifies the current default version (2). Versions prior to 2 are not supported, so this will only be relevant when there are later versions.
`compress`	a logical specifying whether saving to a named file is to use "gzip" compression, or one of `"gzip"`, `"bzip2"` or `"xz"` to indicate the type of compression to be used. Ignored if file is a connection.
`refhook`	a hook function for handling reference objects.
`raw`	whether to save the model in a raw variable or not, recommended to leave it to `TRUE`.

Value

NULL invisibly.

Examples

library(lightgbm)
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)
data(agaricus.test, package = "lightgbm")
test <- agaricus.test
dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
params <- list(objective = "regression", metric = "l2")
valids <- list(test = dtest)
model <- lgb.train(
    params = params
    , data = dtrain
    , nrounds = 10L
    , valids = valids
    , min_data = 1L
    , learning_rate = 1.0
    , early_stopping_rounds = 5L
)
model_file <- tempfile(fileext = ".rds")
saveRDS.lgb.Booster(model, model_file)

lightgbm

Light Gradient Boosting Machine

v3.2.1

MIT + file LICENSE

Authors

Guolin Ke [aut, cre], Damien Soukhavong [aut], James Lamb [aut], Qi Meng [aut], Thomas Finley [aut], Taifeng Wang [aut], Wei Chen [aut], Weidong Ma [aut], Qiwei Ye [aut], Tie-Yan Liu [aut], Yachen Yan [ctb], Microsoft Corporation [cph], Dropbox, Inc. [cph], Jay Loden [cph], Dave Daeschler [cph], Giampaolo Rodola [cph], Alberto Ferreira [ctb], Daniel Lemire [ctb], Victor Zverovich [cph], IBM Corporation [ctb]

Initial release

2021-04-12