Calls saveRDS on an lgb.Booster object, making it serializable before the call if it isn't already.

This function throws a warning and will be removed in future versions.

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

Arguments

object

lgb.Booster 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

# \donttest{
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"
  , min_data = 1L
  , learning_rate = 1.0
)
valids <- list(test = dtest)
model <- lgb.train(
    params = params
    , data = dtrain
    , nrounds = 10L
    , valids = valids
    , early_stopping_rounds = 5L
)
#> [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000957 seconds.
#> You can set `force_row_wise=true` to remove the overhead.
#> And if memory is not enough, you can set `force_col_wise=true`.
#> [LightGBM] [Info] Total Bins 232
#> [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
#> [LightGBM] [Info] Start training from score 0.482113
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [1] "[1]:  test's l2:6.44165e-17"
#> [1] "Will train until there is no improvement in 5 rounds."
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [1] "[2]:  test's l2:1.97215e-31"
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [1] "[3]:  test's l2:0"
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
#> [1] "[4]:  test's l2:0"
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
#> [1] "[5]:  test's l2:0"
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
#> [1] "[6]:  test's l2:0"
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
#> [1] "[7]:  test's l2:0"
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
#> [1] "[8]:  test's l2:0"
#> [1] "Early stopping, best iteration is: [3]:  test's l2:0"
model_file <- tempfile(fileext = ".rds")
saveRDS.lgb.Booster(model, model_file)
#> Warning: 'saveRDS.lgb.Booster' is deprecated and will be removed in a future release. Use saveRDS() instead.
# }