xgb.cv.lowmem: Low memory cross-validation wrapper for XGBoost

In MIC: Analysis of Antimicrobial Minimum Inhibitory Concentration Data

Low memory cross-validation wrapper for XGBoost

Description

This function performs similar operations to xgboost::xgb.cv, but with the operations performed in a memory efficient manner. Unlike xgboost::xgb.cv, this version does not load all folds into memory from the start. Rather it loads each fold into memory sequentially, and trains trains each fold using xgboost::xgb.train. This allows larger datasets to be cross-validated.

The main disadvantage of this function is that it is not possible to perform early stopping based the results of all folds. The function does accept an early stopping argument, but this is applied to each fold separately. This means that different folds can (and should be expected to) train for a different number of rounds.

This function also allows for a train-test split (as opposed to multiple) folds. This is done by providing a value of less than 1 to nfold, or a list of 1 fold to folds. This is not possible with xgboost::xgb.cv, but can be desirable if there is downstream processing that depends on an xgb.cv.synchromous object (which is the return object of both this function and xgboost::xgb.cv).

Otherwise, where possible this function tries to return the same data structure as xgboost::xgb.cv, with the exception of callbacks (not supported as a field within the return object). To save models, use the save_models argument, rather than the cb.cv.predict(save_models = TRUE) callback.

Usage

xgb.cv.lowmem(
  params = list(),
  data,
  nrounds,
  nfold,
  label = NULL,
  missing = NA,
  prediction = FALSE,
  metrics = list(),
  obj = NULL,
  feval = NULL,
  stratified = TRUE,
  folds = NULL,
  train_folds = NULL,
  verbose = 1,
  print_every_n = 1L,
  early_stopping_rounds = NULL,
  maximize = NULL,
  save_models = FALSE,
  ...
)

Arguments

params	parameters for xgboost
data	DMatrix or matrix
nrounds	number of training rounds
nfold	number of folds, or if < 1 then the proportion will be used as the training split in a train-test split
label	data labels (alternatively provide with DMatrix)
missing	handling of missing data (see xgb.cv)
prediction	return predictions
metrics	evaluation metrics
obj	custom objective function
feval	custom evaluation function
stratified	whether to use stratified folds
folds	custom folds
train_folds	custom train folds
verbose	verbosity level
print_every_n	print every n iterations
early_stopping_rounds	early stopping rounds (applied to each fold)
maximize	whether to maximize the evaluation metric
save_models	whether to save the models
...	additional arguments passed to xgb.train

Value

xgb.cv.synchronous object

Examples

train <- list(data = matrix(rnorm(20), ncol = 2),
             label = rbinom(10, 1, 0.5))
dtrain <- xgboost::xgb.DMatrix(train$data, label = train$label, nthread = 1)
cv <- xgb.cv.lowmem(data = dtrain,
                   params = list(objective = "binary:logistic"),
                   nrounds = 2,
                   nfold = 3,
                   prediction = TRUE,
                   nthread = 1)
cv

MIC documentation built on April 12, 2025, 2:26 a.m.