batchtools (version 0.9.17)

reduceResultsList: Apply Functions on Results

Description

Applies a function on the results of your finished jobs and thereby collects them in a list or data.table. The later requires the provided function to return a list (or data.frame) of scalar values. See rbindlist for features and limitations of the aggregation.

If not all jobs are terminated, the respective result will be NULL.

Usage

reduceResultsList(
  ids = NULL,
  fun = NULL,
  ...,
  missing.val,
  reg = getDefaultRegistry()
)
reduceResultsDataTable(
  ids = NULL,
  fun = NULL,
  ...,
  missing.val,
  reg = getDefaultRegistry()
)

Value

reduceResultsList returns a list of the results in the same order as the provided ids.

reduceResultsDataTable returns a data.table with columns “job.id” and additional result columns created via rbindlist, sorted by “job.id”.

Arguments

ids

[data.frame or integer]
A data.frame (or data.table) with a column named “job.id”. Alternatively, you may also pass a vector of integerish job ids. If not set, defaults to the return value of findDone. Invalid ids are ignored.

fun

[function]
Function to apply to each result. The result is passed unnamed as first argument. If NULL, the identity is used. If the function has the formal argument “job”, the Job/Experiment is also passed to the function.

...

[ANY]
Additional arguments passed to to function fun.

missing.val

[ANY]
Value to impute as result for a job which is not finished. If not provided and a result is missing, an exception is raised.

reg

[Registry]
Registry. If not explicitly passed, uses the default registry (see setDefaultRegistry).

See Also

reduceResults

Other Results: batchMapResults(), loadResult(), reduceResults()

Examples

 batchtools:::example_push_temp(2) 
### Example 1 - reduceResultsList
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
batchMap(function(x) x^2, x = 1:10, reg = tmp)
submitJobs(reg = tmp)
waitForJobs(reg = tmp)
reduceResultsList(fun = sqrt, reg = tmp)

### Example 2 - reduceResultsDataTable
tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE)

# add first problem
fun = function(job, data, n, mean, sd, ...) rnorm(n, mean = mean, sd = sd)
addProblem("rnorm", fun = fun, reg = tmp)

# add second problem
fun = function(job, data, n, lambda, ...) rexp(n, rate = lambda)
addProblem("rexp", fun = fun, reg = tmp)

# add first algorithm
fun = function(instance, method, ...) if (method == "mean") mean(instance) else median(instance)
addAlgorithm("average", fun = fun, reg = tmp)

# add second algorithm
fun = function(instance, ...) sd(instance)
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)
algo.designs$average = data.table(method = c("mean", "median"))
algo.designs$deviation = data.table()

# add experiments and submit
addExperiments(prob.designs, algo.designs, reg = tmp)
submitJobs(reg = tmp)

# collect results and join them with problem and algorithm paramters
res = ijoin(
  getJobPars(reg = tmp),
  reduceResultsDataTable(reg = tmp, fun = function(x) list(res = x))
)
unwrap(res, sep = ".")