fast.saveload: Fast Saving and Loading Objects

In randomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)

Fast Saving and Loading Objects

Description

Save and load the forest component of a fitted random forest using a directory of files. The loaded forest can be passed to predict(). Corresponding helpers save and load lists of forests.

Usage

fast.save(o, path = NULL, testing = TRUE, units = "Mb")

fast.load(directory, path = NULL, testing = FALSE, units = "Mb")

fast.save.list(o, path = NULL, testing = FALSE, units = "Mb")

fast.load.list(directory, path = NULL, testing = FALSE, units = "Mb")

Arguments

o	For fast.save(), a grow object with class beginning c("rfsrc", "grow") and a retained forest component. Supply the grow object, not o$forest. For fast.save.list(), a nonempty list of such grow objects.
path	For saving, the destination directory, including its name. The default is file.path(getwd(), "forest"). An existing destination and all its contents are removed before saving. For loading, the parent directory containing directory; NULL uses the current working directory. See Details for matching save and load paths.
directory	For loading, the name of the saved forest directory relative to path, or to the current working directory when path = NULL. For fast.load.list(), the name of the directory containing the saved forest subdirectories.
testing	Print component-size diagnostics using lsos(). Defaults to TRUE for fast.save() and FALSE for the other helpers. This is a size display, not a save/load comparison or a timing benchmark.
units	Character string specifying the units for the diagnostic size display, passed to format(object.size(...), units = units). The default is "Mb". This does not affect the saved format.

Details

Saving and loading a forest

fast.save() saves o$forest, including its retained training data and tree information. fast.load() returns the reconstructed forest object, which can be used directly with predict.rfsrc.

Components outside o$forest, such as the grow object's prediction and performance summaries, are not saved. To preserve the complete R object instead, use saveRDS and readRDS.

Saving requires the fst and data.table packages; loading requires fst. Large tabular components are written with fst::write_fst(), and the remaining forest metadata are stored with saveRDS(..., compress = FALSE). The files use a ‘.rda’ suffix despite these different formats. Keep the entire directory together and read it using fast.load().

Directories

Both save helpers remove the destination directory and all its contents before writing. Use a dedicated directory for the forest or forest list, rather than a directory containing other work. The destination is then created, including parent directories as needed.

Saving takes the full destination in path. Loading takes its name in directory and its parent in path. For a destination stored in save.path, the matching load call is fast.load(basename(save.path), path = dirname(save.path)). With the default save destination, use fast.load("forest").

Lists of forests

fast.save.list() saves the elements in subdirectories ‘forest1’, ‘forest2’, and so on, inside path. Original list names are not stored.

fast.load.list() loads every entry returned by list.files() in the list directory and returns an unnamed list in that order. Keep only saved forest subdirectories there. Directory-name ordering can differ from the original numeric index order: for example, ‘forest10’ can precede ‘forest2’. Use fast.load() on a named subdirectory when selecting a particular saved element.

Sampling-size function

The saved sampling-size information is sampfrac = o$forest$sampsize(1). Loading reconstructs the function as function(x) x * sampfrac. This preserves a proportional sampling-size rule; it does not preserve an arbitrary nonlinear custom function. Use saveRDS() when that original function must be retained.

Value

fast.save

Used for its file-writing side effect. Its return value is the memory-usage matrix from the final gc(FALSE) call, not the saved forest.

fast.load

The reconstructed forest component, retaining its saved class, ordinarily c("rfsrc", "forest", family). It is a forest object rather than the original grow object.

fast.save.list

An unnamed list of the return values from the individual fast.save() calls. The forests are saved in the destination subdirectories.

fast.load.list

An unnamed list of reconstructed forest objects in the directory-listing order described above.

See Also

rfsrc, predict.rfsrc, saveRDS, readRDS

Examples

## ------------------------------------------------------------
## Regression: save, load, and compare predictions
## ------------------------------------------------------------
if (requireNamespace("fst", quietly = TRUE) &&
    requireNamespace("data.table", quietly = TRUE)) {
  o <- rfsrc(mpg ~ ., data = mtcars)
  print(o)
  save.path <- tempfile("rfsrc-forest-")
  fast.save(o, path = save.path, testing = FALSE)
  oo <- fast.load(basename(save.path), path = dirname(save.path))
  p <- predict(o)
  pp <- predict(oo)
  print(summary(p$predicted - pp$predicted))
  print(summary(p$predicted.oob - pp$predicted.oob))
  unlink(save.path, recursive = TRUE)
}


## ------------------------------------------------------------
## Regression: a list of forests with different node sizes
## ------------------------------------------------------------
if (requireNamespace("fst", quietly = TRUE) &&
    requireNamespace("data.table", quietly = TRUE)) {
  o1 <- rfsrc(mpg ~ ., data = mtcars, nodesize = 1)
  o2 <- rfsrc(mpg ~ ., data = mtcars, nodesize = 10)
  print(o1)
  print(o2)
  models <- list(o1, o2)
  save.path <- tempfile("rfsrc-forest-list-")
  invisible(fast.save.list(models, path = save.path))
  oo <- fast.load.list(basename(save.path), path = dirname(save.path))
  print(predict(oo[[1]]))
  print(predict(oo[[2]]))
  unlink(save.path, recursive = TRUE)
}

## ------------------------------------------------------------
## RFQ for imbalanced classification
## ------------------------------------------------------------
## Use matching prediction seeds when comparing class labels.
if (requireNamespace("fst", quietly = TRUE) &&
    requireNamespace("data.table", quietly = TRUE)) {
  data(breast, package = "randomForestSRC")
  dta <- na.omit(breast)
  o <- imbalanced(status ~ ., data = dta, ntree = 100)
  print(o)
  save.path <- tempfile("rfsrc-forest-")
  fast.save(o, path = save.path, testing = FALSE)
  oo <- fast.load(basename(save.path), path = dirname(save.path))
  set.seed(19)
  p <- predict(o, seed = -19)
  set.seed(19)
  pp <- predict(oo, seed = -19)
  print(summary(p$predicted - pp$predicted))
  print(summary(p$predicted.oob - pp$predicted.oob))
  print(all.equal(as.character(p$class), as.character(pp$class)))
  print(all.equal(as.character(p$class.oob),
                  as.character(pp$class.oob)))
  unlink(save.path, recursive = TRUE)
}

## ------------------------------------------------------------
## Binary classification with rfq = TRUE
## ------------------------------------------------------------
if (requireNamespace("fst", quietly = TRUE) &&
    requireNamespace("data.table", quietly = TRUE)) {
  data(breast, package = "randomForestSRC")
  dta <- na.omit(breast)
  o <- rfsrc(status ~ ., data = dta, rfq = TRUE, ntree = 100,
             perf.type = "gmean", splitrule = "auc")
  print(o)
  save.path <- tempfile("rfsrc-forest-")
  fast.save(o, path = save.path, testing = FALSE)
  oo <- fast.load(basename(save.path), path = dirname(save.path))
  set.seed(19)
  p <- predict(o, seed = -19)
  set.seed(19)
  pp <- predict(oo, seed = -19)
  print(summary(p$predicted - pp$predicted))
  print(summary(p$predicted.oob - pp$predicted.oob))
  print(all.equal(as.character(p$class), as.character(pp$class)))
  print(all.equal(as.character(p$class.oob),
                  as.character(pp$class.oob)))
  unlink(save.path, recursive = TRUE)
}

## ------------------------------------------------------------
## Anonymous RFQ: supply the same prediction data to both forests
## ------------------------------------------------------------
if (requireNamespace("fst", quietly = TRUE) &&
    requireNamespace("data.table", quietly = TRUE)) {
  data(breast, package = "randomForestSRC")
  dta <- na.omit(breast)
  o <- rfsrc.anonymous(status ~ ., data = dta, rfq = TRUE,
                       ntree = 100, perf.type = "gmean", splitrule = "auc")
  print(o)
  save.path <- tempfile("rfsrc-forest-")
  fast.save(o, path = save.path, testing = FALSE)
  oo <- fast.load(basename(save.path), path = dirname(save.path))
  set.seed(19)
  p <- predict(o, newdata = dta, seed = -19)
  set.seed(19)
  pp <- predict(oo, newdata = dta, seed = -19)
  print(summary(p$predicted - pp$predicted))
  print(all.equal(as.character(p$class), as.character(pp$class)))
  unlink(save.path, recursive = TRUE)
}

## ------------------------------------------------------------
## Survival
## ------------------------------------------------------------
if (requireNamespace("fst", quietly = TRUE) &&
    requireNamespace("data.table", quietly = TRUE)) {
  data(pbc, package = "randomForestSRC")
  o <- rfsrc(Surv(days, status) ~ ., data = pbc, ntree = 100)
  print(o)
  save.path <- tempfile("rfsrc-forest-")
  fast.save(o, path = save.path, testing = FALSE)
  oo <- fast.load(basename(save.path), path = dirname(save.path))
  set.seed(19)
  p <- predict(o, seed = -19)
  set.seed(19)
  pp <- predict(oo, seed = -19)
  print(summary(p$predicted - pp$predicted))
  print(summary(p$predicted.oob - pp$predicted.oob))
  unlink(save.path, recursive = TRUE)
}

## ------------------------------------------------------------
## Survival with save.memory = TRUE
## ------------------------------------------------------------
if (requireNamespace("fst", quietly = TRUE) &&
    requireNamespace("data.table", quietly = TRUE)) {
  data(pbc, package = "randomForestSRC")
  o <- rfsrc(Surv(days, status) ~ ., data = pbc,
             ntree = 100, save.memory = TRUE)
  print(o)
  save.path <- tempfile("rfsrc-forest-")
  fast.save(o, path = save.path, testing = FALSE)
  oo <- fast.load(basename(save.path), path = dirname(save.path))
  set.seed(19)
  p <- predict(o, seed = -19)
  set.seed(19)
  pp <- predict(oo, seed = -19)
  print(summary(p$predicted - pp$predicted))
  print(summary(p$predicted.oob - pp$predicted.oob))
  unlink(save.path, recursive = TRUE)
}

## ------------------------------------------------------------
## Competing risks
## ------------------------------------------------------------
if (requireNamespace("fst", quietly = TRUE) &&
    requireNamespace("data.table", quietly = TRUE)) {
  data(wihs, package = "randomForestSRC")
  o <- rfsrc(Surv(time, status) ~ ., data = wihs, nsplit = 3, ntree = 100)
  print(o)
  save.path <- tempfile("rfsrc-forest-")
  fast.save(o, path = save.path, testing = FALSE)
  oo <- fast.load(basename(save.path), path = dirname(save.path))
  set.seed(19)
  p <- predict(o, seed = -19)
  set.seed(19)
  pp <- predict(oo, seed = -19)
  print(summary(p$predicted - pp$predicted))
  print(summary(p$predicted.oob - pp$predicted.oob))
  unlink(save.path, recursive = TRUE)
}

## ------------------------------------------------------------
## Multivariate regression and classification
## ------------------------------------------------------------
if (requireNamespace("fst", quietly = TRUE) &&
    requireNamespace("data.table", quietly = TRUE)) {
  data(nutrigenomic, package = "randomForestSRC")
  ydta <- data.frame(diet = nutrigenomic$diet,
                     genotype = nutrigenomic$genotype,
                     nutrigenomic$lipids)
  o <- rfsrc(get.mv.formula(colnames(ydta)),
             data = data.frame(ydta, nutrigenomic$genes),
             ntree = 100, importance = TRUE, nsplit = 10)
  print(o)
  save.path <- tempfile("rfsrc-forest-")
  fast.save(o, path = save.path, testing = FALSE)
  oo <- fast.load(basename(save.path), path = dirname(save.path))
  set.seed(19)
  p <- predict(o, seed = -19)
  set.seed(19)
  pp <- predict(oo, seed = -19)
  print(summary(get.mv.predicted(p, oob = FALSE) -
                get.mv.predicted(pp, oob = FALSE)))
  print(summary(get.mv.predicted(p) - get.mv.predicted(pp)))
  for (yn in names(p$classOutput)) {
    cat("check coherence for outcome:", yn, "\n")
    print(all.equal(as.character(p$classOutput[[yn]]$class),
                    as.character(pp$classOutput[[yn]]$class)))
    print(all.equal(as.character(p$classOutput[[yn]]$class.oob),
                    as.character(pp$classOutput[[yn]]$class.oob)))
  }
  unlink(save.path, recursive = TRUE)
}


## Not run: 

## ------------------------------------------------------------
## Classification: optional alzheimers data from varPro
## ------------------------------------------------------------
if (requireNamespace("fst", quietly = TRUE) &&
    requireNamespace("data.table", quietly = TRUE)) {
  data(alzheimers, package = "varPro")
  o <- rfsrc(Diagnosis ~ ., data = alzheimers)
  print(o)
  save.path <- tempfile("rfsrc-forest-")
  fast.save(o, path = save.path, testing = FALSE)
  oo <- fast.load(basename(save.path), path = dirname(save.path))
  set.seed(19)
  p <- predict(o, seed = -19)
  set.seed(19)
  pp <- predict(oo, seed = -19)
  print(summary(p$predicted - pp$predicted))
  print(summary(p$predicted.oob - pp$predicted.oob))
  print(all.equal(as.character(p$class), as.character(pp$class)))
  print(all.equal(as.character(p$class.oob),
                  as.character(pp$class.oob)))
  unlink(save.path, recursive = TRUE)
}


## ------------------------------------------------------------
## Optional memory-intensive anonymous survival test
## ------------------------------------------------------------
## This test repeats each PBC row 250 times and can require substantial memory.
if (requireNamespace("fst", quietly = TRUE) &&
    requireNamespace("data.table", quietly = TRUE)) {
  data(pbc, package = "randomForestSRC")
  dta <- pbc[rep(seq_len(nrow(pbc)), each = 250), ]
  o <- rfsrc.anonymous(Surv(days, status) ~ ., data = dta)
  print(o)
  save.path <- tempfile("rfsrc-forest-")
  fast.save(o, path = save.path, testing = FALSE)
  oo <- fast.load(basename(save.path), path = dirname(save.path))
  set.seed(19)
  p <- predict(o, newdata = dta, seed = -19)
  set.seed(19)
  pp <- predict(oo, newdata = dta, seed = -19)
  print(summary(p$predicted - pp$predicted))
  unlink(save.path, recursive = TRUE)
}

## End(Not run)

randomForestSRC documentation built on Sept. 16, 2026, 5:06 p.m.