data.table Depends -> Imports

.gitignore

@@ -6,3 +6,6 @@
 /vignettes/*.pdf
 /.Rproj.user
 /batchtools.Rproj
+*.tar.gz
+*.Rcheck
+

DESCRIPTION

@@ -28,13 +28,13 @@ NeedsCompilation: yes
 ByteCompile: yes
 Encoding: UTF-8
 Depends:
-    R (>= 3.0.0),
-    data.table (>= 1.11.2)
+    R (>= 3.0.0)
 Imports:
     backports (>= 1.1.2),
     base64url (>= 1.1),
     brew,
     checkmate (>= 1.8.5),
+    data.table (>= 1.11.2),
     digest (>= 0.6.9),
     fs (>= 1.2.0),
     parallel,

NAMESPACE

@@ -122,13 +122,29 @@ export(ujoin)
 export(unwrap)
 export(waitForJobs)
 import(checkmate)
-import(data.table)
 import(stringi)
 import(utils)
 importFrom(R6,R6Class)
 importFrom(base64url,base32_decode)
 importFrom(base64url,base32_encode)
 importFrom(brew,brew)
+importFrom(data.table,"%chin%")
+importFrom(data.table,CJ)
+importFrom(data.table,alloc.col)
+importFrom(data.table,as.data.table)
+importFrom(data.table,chmatch)
+importFrom(data.table,copy)
+importFrom(data.table,data.table)
+importFrom(data.table,is.data.table)
+importFrom(data.table,key)
+importFrom(data.table,rbindlist)
+importFrom(data.table,set)
+importFrom(data.table,setattr)
+importFrom(data.table,setkey)
+importFrom(data.table,setkeyv)
+importFrom(data.table,setnames)
+importFrom(data.table,setorderv)
+importFrom(data.table,uniqueN)
 importFrom(digest,digest)
 importFrom(progress,progress_bar)
 importFrom(rappdirs,site_config_dir)

R/ExperimentRegistry.R

@@ -22,7 +22,7 @@
 #'   fun = function(job, data, n, mean, sd, ...) rnorm(n, mean = mean, sd = sd))
 #' addAlgorithm(reg = tmp, "a1", fun = function(job, data, instance, ...) mean(instance))
 #' addAlgorithm(reg = tmp, "a2", fun = function(job, data, instance, ...) median(instance))
-#' ids = addExperiments(reg = tmp, list(p1 = CJ(n = c(50, 100), mean = -2:2, sd = 1:4)))
+#' ids = addExperiments(reg = tmp, list(p1 = data.table::CJ(n = c(50, 100), mean = -2:2, sd = 1:4)))
 #'
 #' # Overview over defined experiments:
 #' tmp$problems

R/addExperiments.R

@@ -56,6 +56,7 @@
 #' addAlgorithm("deviation", fun = fun, reg = tmp)
 #'
 #' # define problem and algorithm designs
+#' library(data.table)
 #' prob.designs = algo.designs = list()
 #' prob.designs$rnorm = CJ(n = 100, mean = -1:1, sd = 1:5)
 #' prob.designs$rexp = data.table(n = 100, lambda = 1:5)

R/batchMap.R

@@ -46,7 +46,7 @@
 #'
 #' # example for an expand.grid()-like operation on parameters
 #' tmp = makeRegistry(file.dir = NA, make.default = FALSE)
-#' ids = batchMap(paste, args = CJ(x = letters[1:3], y = 1:3), reg = tmp)
+#' ids = batchMap(paste, args = data.table::CJ(x = letters[1:3], y = 1:3), reg = tmp)
 #' getJobPars(reg = tmp)
 #' testJob(6, reg = tmp)
 batchMap = function(fun, ..., args = list(), more.args = list(), reg = getDefaultRegistry()) {

R/chunkIds.R

@@ -61,6 +61,7 @@
 #' ids = batchMap(identity, 1:25, reg = tmp)
 #'
 #' ### Group into chunks with 10 jobs each
+#' library(data.table)
 #' ids[, chunk := chunk(job.id, chunk.size = 10)]
 #' print(ids[, .N, by = chunk])
 #'

R/estimateRuntimes.R

@@ -32,14 +32,15 @@
 #' addProblem(name = "iris", data = iris, fun = function(data, ...) nrow(data), reg = tmp)
 #' addAlgorithm(name = "nrow", function(instance, ...) nrow(instance), reg = tmp)
 #' addAlgorithm(name = "ncol", function(instance, ...) ncol(instance), reg = tmp)
-#' addExperiments(algo.designs = list(nrow = CJ(x = 1:50, y = letters[1:5])), reg = tmp)
-#' addExperiments(algo.designs = list(ncol = CJ(x = 1:50, y = letters[1:5])), reg = tmp)
+#' addExperiments(algo.designs = list(nrow = data.table::CJ(x = 1:50, y = letters[1:5])), reg = tmp)
+#' addExperiments(algo.designs = list(ncol = data.table::CJ(x = 1:50, y = letters[1:5])), reg = tmp)
 #'
 #' # We use the job parameters to predict runtimes
 #' tab = unwrap(getJobPars(reg = tmp))
 #'
 #' # First we need to submit some jobs so that the forest can train on some data.
 #' # Thus, we just sample some jobs from the registry while grouping by factor variables.
+#' library(data.table)
 #' ids = tab[, .SD[sample(nrow(.SD), 5)], by = c("problem", "algorithm", "y")]
 #' setkeyv(ids, "job.id")
 #' submitJobs(ids, reg = tmp)

R/reduceResults.R

@@ -161,6 +161,7 @@ reduceResults = function(fun, ids = NULL, init, ..., reg = getDefaultRegistry())
 #' addAlgorithm("deviation", fun = fun, reg = tmp)
 #'
 #' # define problem and algorithm designs
+#' library(data.table)
 #' prob.designs = algo.designs = list()
 #' prob.designs$rnorm = CJ(n = 100, mean = -1:1, sd = 1:5)
 #' prob.designs$rexp = data.table(n = 100, lambda = 1:5)

R/submitJobs.R

@@ -145,7 +145,7 @@
 #' fun = function(n, p) colMeans(matrix(runif(n*p), n, p))
 #'
 #' # Arguments to fun:
-#' args = CJ(n = c(1e4, 1e5), p = c(10, 50)) # like expand.grid()
+#' args = data.table::CJ(n = c(1e4, 1e5), p = c(10, 50)) # like expand.grid()
 #' print(args)
 #'
 #' # Map function to create jobs

R/unwrap.R

@@ -25,7 +25,7 @@
 #' @return [\code{\link{data.table}}].
 #' @export
 #' @examples
-#' x = data.table(
+#' x = data.table::data.table(
 #'   id = 1:3,
 #'   values = list(list(a = 1, b = 3), list(a = 2, b = 2), list(a = 3))
 #' )

R/zzz.R

@@ -19,8 +19,8 @@
 #' loading \pkg{batchtools}.
 #' @import utils
 #' @import checkmate
-#' @import data.table
 #' @import stringi
+#' @importFrom data.table %chin% alloc.col as.data.table data.table chmatch CJ copy data.table is.data.table key rbindlist set setattr setkey setkeyv setnames setorderv uniqueN
 #' @importFrom R6 R6Class
 #' @importFrom digest digest
 #' @importFrom brew brew
@@ -31,6 +31,9 @@
 #' @importFrom withr with_dir with_seed local_options local_dir
 "_PACKAGE"
 
+.BY = .N = .SD = NULL
+`:=` = function(...) NULL
+
 batchtools = new.env(parent = emptyenv())
 batchtools$debug = FALSE
 batchtools$hooks = list(

vignettes/batchtools.Rmd

@@ -13,6 +13,7 @@ vignette: >
 
 ```{r,include = FALSE, cache = FALSE}
 library(batchtools)
+library(data.table) # for %chin%, data.table
 options(batchtools.progress = FALSE, datatable.print.class = TRUE, batchtools.timestamps = FALSE)
 if (identical(Sys.getenv("IN_PKGDOWN"), "true")) {
   tmp_dir = fs::path(dirname(tempdir()), "batchtools-vignette")
@@ -527,4 +528,3 @@ This approach is totally viable as long as some general rules are followed:
 6. Avoid accessing the `file.dir` with multiple sessions simultaneously.
    This includes accessing the registry via a mount!
    Simultaneous access may lead to inconsistencies and missing results.
-