flatten() -> unwrap()

Author: mllg

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: batchtools
 Title: Tools for Computation on Batch Systems
-Version: 0.9.7-9002
+Version: 0.9.8
 Authors@R: c(
     person("Michel", "Lang", NULL, "michellang@gmail.com",
       role = c("cre", "aut"), comment = c(ORCID = "0000-0001-9754-0393")),

NAMESPACE

@@ -118,6 +118,7 @@ export(sweepRegistry)
 export(syncRegistry)
 export(testJob)
 export(ujoin)
+export(unwrap)
 export(waitForJobs)
 import(checkmate)
 import(data.table)

NEWS.md

@@ -2,6 +2,8 @@
 
 * Renamed column "memory" in the status table to "mem.used" to avoid name clashes with the resource specification.
 * Exported function `assertRegistry()`.
+* New function `unwrap()` as alias to `flatten()`.
+  The latter causes a name clash with package `purrr` and will be deprecated in a future version.
 * Registries now contain a unique hash which is updated each time the registry is altered.
   Can be utilized to invalidate caches, e.g. the cache of knitr.
 

R/ExperimentRegistry.R

@@ -29,7 +29,7 @@
 #' summarizeExperiments(reg = tmp)
 #' summarizeExperiments(reg = tmp, by = c("problem", "algorithm", "n"))
 #' ids = findExperiments(prob.pars = (n == 50), reg = tmp)
-#' print(flatten(getJobPars(ids, reg = tmp)))
+#' print(unwrap(getJobPars(ids, reg = tmp)))
 #'
 #' # Submit jobs
 #' submitJobs(reg = tmp)
@@ -42,8 +42,8 @@
 #' # Join info table with all results and calculate mean of results
 #' # grouped by n and algorithm
 #' ids = findDone(reg = tmp)
-#' pars = flatten(getJobPars(ids, reg = tmp))
-#' results = flatten(reduceResultsDataTable(ids, fun = function(res) list(res = res), reg = tmp))
+#' pars = unwrap(getJobPars(ids, reg = tmp))
+#' results = unwrap(reduceResultsDataTable(ids, fun = function(res) list(res = res), reg = tmp))
 #' tab = ljoin(pars, results)
 #' tab[, list(mres = mean(res)), by = c("n", "algorithm")]
 makeExperimentRegistry = function(file.dir = "registry", work.dir = getwd(), conf.file = findConfFile(), packages = character(0L), namespaces = character(0L),

R/addExperiments.R

@@ -66,7 +66,7 @@
 #'
 #' # check what has been created
 #' summarizeExperiments(reg = tmp)
-#' flatten(getJobPars(reg = tmp))
+#' unwrap(getJobPars(reg = tmp))
 addExperiments = function(prob.designs = NULL, algo.designs = NULL, repls = 1L, combine = "crossprod", reg = getDefaultRegistry()) {
   convertDesigns = function(type, designs, keywords) {
     check.factors = default.stringsAsFactors()

R/batchMapResults.R

@@ -33,11 +33,11 @@
 #' waitForJobs(reg = target)
 #'
 #' # Map old to new ids. First, get a table with results and parameters
-#' results = flatten(rjoin(getJobPars(reg = target), reduceResultsDataTable(reg = target)))
+#' results = unwrap(rjoin(getJobPars(reg = target), reduceResultsDataTable(reg = target)))
 #' print(results)
 #'
 #' # Parameter '..id' points to job.id in 'source'. Use a inner join to combine:
-#' ijoin(results, flatten(reduceResultsDataTable(reg = tmp)), by = c("..id" = "job.id"))
+#' ijoin(results, unwrap(reduceResultsDataTable(reg = tmp)), by = c("..id" = "job.id"))
 batchMapResults = function(fun, ids = NULL, ..., more.args = list(), target, source = getDefaultRegistry()) {
   assertRegistry(source, sync = TRUE)
   assertRegistry(target, writeable = TRUE, sync = TRUE)

R/clusterFunctionsOpenLava.R

@@ -1,7 +1,7 @@
 #' @title ClusterFunctions for OpenLava
 #'
 #' @description
-#' Cluster functions for OpenLava (\url{http://www.openlava.org/}).
+#' Cluster functions for OpenLava.
 #'
 #' Job files are created based on the brew template \code{template}. This
 #' file is processed with brew and then submitted to the queue using the

R/estimateRuntimes.R

@@ -35,7 +35,7 @@
 #' addExperiments(algo.designs = list(ncol = CJ(x = 1:50, y = letters[1:5])), reg = tmp)
 #'
 #' # We use the job parameters to predict runtimes
-#' tab = flatten(getJobPars(reg = tmp))
+#' 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.

R/reduceResults.R

@@ -174,7 +174,7 @@ reduceResults = function(fun, ids = NULL, init, ..., reg = getDefaultRegistry())
 #'   getJobPars(reg = tmp),
 #'   reduceResultsDataTable(reg = tmp, fun = function(x) list(res = x))
 #' )
-#' flatten(res, sep = ".")
+#' unwrap(res, sep = ".")
 reduceResultsList = function(ids = NULL, fun = NULL, ..., missing.val, reg = getDefaultRegistry()) {
   assertRegistry(reg, sync = TRUE)
   assertFunction(fun, null.ok = TRUE)

R/submitJobs.R

@@ -110,10 +110,10 @@
 #'
 #' # Retrive information about memory, combine with parameters
 #' info = ijoin(getJobStatus(reg = tmp)[, .(job.id, mem.used)], getJobPars(reg = tmp))
-#' print(flatten(info))
+#' print(unwrap(info))
 #'
 #' # Combine job info with results -> each job is aggregated using mean()
-#' flatten(ijoin(info, reduceResultsDataTable(fun = function(res) list(res = mean(res)), reg = tmp)))
+#' unwrap(ijoin(info, reduceResultsDataTable(fun = function(res) list(res = mean(res)), reg = tmp)))
 #'
 #' ### Example 2: Multicore execution on the slave
 #' tmp = makeRegistry(file.dir = NA, make.default = FALSE)