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Dump an XGBoost model in text format.

Source: R/xgb.dump.R
xgb.dump.Rd

Dump an XGBoost model in text format.

Usage

xgb.dump(
  model,
  fname = NULL,
  fmap = "",
  with_stats = FALSE,
  dump_format = c("text", "json", "dot"),
  ...
)

Arguments

model

The model object.

fname

The name of the text file where to save the model text dump. If not provided or set to NULL, the model is returned as a character vector.

fmap

Feature map file representing feature types. See demo/ for a walkthrough example in R, and https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt to see an example of the value.

with_stats

Whether to dump some additional statistics about the splits. When this option is on, the model dump contains two additional values: gain is the approximate loss function gain we get in each split; cover is the sum of second order gradient in each node.

dump_format

Either 'text', 'json', or 'dot' (graphviz) format could be specified.

Format 'dot' for a single tree can be passed directly to packages that consume this format for graph visualization, such as function DiagrammeR::grViz()

...

Not used.

Some arguments that were part of this function in previous XGBoost versions are currently deprecated or have been renamed. If a deprecated or renamed argument is passed, will throw a warning (by default) and use its current equivalent instead. This warning will become an error if using the 'strict mode' option.

If some additional argument is passed that is neither a current function argument nor a deprecated or renamed argument, a warning or error will be thrown depending on the 'strict mode' option.

Important: ... will be removed in a future version, and all the current deprecation warnings will become errors. Please use only arguments that form part of the function signature.

Value

If fname is not provided or set to NULL the function will return the model as a character vector. Otherwise it will return TRUE.

Examples

DONTSHOW({RhpcBLASctl::omp_set_num_threads(1)})
data(agaricus.train, package = "xgboost")
data(agaricus.test, package = "xgboost")

train <- agaricus.train
test <- agaricus.test

bst <- xgb.train(
  data = xgb.DMatrix(train$data, label = train$label, nthread = 1),
  nrounds = 2,
  params = xgb.params(
    max_depth = 2,
    nthread = 2,
    objective = "binary:logistic"
  )
)

# save the model in file 'xgb.model.dump'
dump_path = file.path(tempdir(), 'model.dump')
xgb.dump(bst, dump_path, with_stats = TRUE)

# print the model without saving it to a file
print(xgb.dump(bst, with_stats = TRUE))

# print in JSON format:
cat(xgb.dump(bst, with_stats = TRUE, dump_format = "json"))

# plot first tree leveraging the 'dot' format
if (requireNamespace('DiagrammeR', quietly = TRUE)) {
  DiagrammeR::grViz(xgb.dump(bst, dump_format = "dot")[[1L]])
}