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.

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

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

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).

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”.

Note

If you have thousands of jobs, disabling the progress bar (options(batchtools.progress = FALSE)) can significantly increase the performance.

See also

Examples

### Example 1 - reduceResultsList tmp = makeRegistry(file.dir = NA, make.default = FALSE)
#> Sourcing configuration file '~/.batchtools.conf.R' ...
#> Created registry in '/tmp/batchtools-example/reg1' using cluster functions 'Interactive'
batchMap(function(x) x^2, x = 1:10, reg = tmp)
#> Adding 10 jobs ...
submitJobs(reg = tmp)
#> Submitting 10 jobs in 10 chunks using cluster functions 'Interactive' ...
#> ### [bt]: Setting seed to 1 ...
#> ### [bt]: Setting seed to 2 ...
#> ### [bt]: Setting seed to 3 ...
#> ### [bt]: Setting seed to 4 ...
#> ### [bt]: Setting seed to 5 ...
#> ### [bt]: Setting seed to 6 ...
#> ### [bt]: Setting seed to 7 ...
#> ### [bt]: Setting seed to 8 ...
#> ### [bt]: Setting seed to 9 ...
#>
#> Submitting [=============================================>-----] 90% eta: 0s
#> ### [bt]: Setting seed to 10 ...
#>
#> Submitting [===================================================] 100% eta: 0s
#>
#>
waitForJobs(reg = tmp)
#> [1] TRUE
reduceResultsList(fun = sqrt, reg = tmp)
#> [[1]] #> [1] 1 #> #> [[2]] #> [1] 2 #> #> [[3]] #> [1] 3 #> #> [[4]] #> [1] 4 #> #> [[5]] #> [1] 5 #> #> [[6]] #> [1] 6 #> #> [[7]] #> [1] 7 #> #> [[8]] #> [1] 8 #> #> [[9]] #> [1] 9 #> #> [[10]] #> [1] 10 #>
### Example 2 - reduceResultsDataTable tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE)
#> Sourcing configuration file '~/.batchtools.conf.R' ...
#> Created registry in '/tmp/batchtools-example/reg2' using cluster functions 'Interactive'
# add first problem fun = function(job, data, n, mean, sd, ...) rnorm(n, mean = mean, sd = sd) addProblem("rnorm", fun = fun, reg = tmp)
#> Adding problem 'rnorm'
# add second problem fun = function(job, data, n, lambda, ...) rexp(n, rate = lambda) addProblem("rexp", fun = fun, reg = tmp)
#> Adding problem 'rexp'
# add first algorithm fun = function(instance, method, ...) if (method == "mean") mean(instance) else median(instance) addAlgorithm("average", fun = fun, reg = tmp)
#> Adding algorithm 'average'
# add second algorithm fun = function(instance, ...) sd(instance) addAlgorithm("deviation", fun = fun, reg = tmp)
#> Adding algorithm 'deviation'
# define problem and algorithm designs 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)
#> Adding 30 experiments ('rnorm'[15] x 'average'[2] x repls[1]) ...
#> Adding 15 experiments ('rnorm'[15] x 'deviation'[1] x repls[1]) ...
#> Adding 10 experiments ('rexp'[5] x 'average'[2] x repls[1]) ...
#> Adding 5 experiments ('rexp'[5] x 'deviation'[1] x repls[1]) ...
submitJobs(reg = tmp)
#> Submitting 60 jobs in 60 chunks using cluster functions 'Interactive' ...
#> ### [bt]: Generating problem instance for problem 'rnorm' ...
#> ### [bt]: Applying algorithm 'average' on problem 'rnorm' for job 1 (seed = 13297) ...
#> ### [bt]: Generating problem instance for problem 'rnorm' ...
#> ### [bt]: Applying algorithm 'average' on problem 'rnorm' for job 2 (seed = 13298) ...
#> ### [bt]: Generating problem instance for problem 'rnorm' ...
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#> ### [bt]: Generating problem instance for problem 'rnorm' ...
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# 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 = ".")
#> Key: <job.id> #> job.id problem algorithm prob.pars.n prob.pars.mean prob.pars.sd #> <int> <char> <char> <num> <int> <int> #> 1: 1 rnorm average 100 -1 1 #> 2: 2 rnorm average 100 -1 1 #> 3: 3 rnorm average 100 -1 2 #> 4: 4 rnorm average 100 -1 2 #> 5: 5 rnorm average 100 -1 3 #> 6: 6 rnorm average 100 -1 3 #> 7: 7 rnorm average 100 -1 4 #> 8: 8 rnorm average 100 -1 4 #> 9: 9 rnorm average 100 -1 5 #> 10: 10 rnorm average 100 -1 5 #> 11: 11 rnorm average 100 0 1 #> 12: 12 rnorm average 100 0 1 #> 13: 13 rnorm average 100 0 2 #> 14: 14 rnorm average 100 0 2 #> 15: 15 rnorm average 100 0 3 #> 16: 16 rnorm average 100 0 3 #> 17: 17 rnorm average 100 0 4 #> 18: 18 rnorm average 100 0 4 #> 19: 19 rnorm average 100 0 5 #> 20: 20 rnorm average 100 0 5 #> 21: 21 rnorm average 100 1 1 #> 22: 22 rnorm average 100 1 1 #> 23: 23 rnorm average 100 1 2 #> 24: 24 rnorm average 100 1 2 #> 25: 25 rnorm average 100 1 3 #> 26: 26 rnorm average 100 1 3 #> 27: 27 rnorm average 100 1 4 #> 28: 28 rnorm average 100 1 4 #> 29: 29 rnorm average 100 1 5 #> 30: 30 rnorm average 100 1 5 #> 31: 31 rnorm deviation 100 -1 1 #> 32: 32 rnorm deviation 100 -1 2 #> 33: 33 rnorm deviation 100 -1 3 #> 34: 34 rnorm deviation 100 -1 4 #> 35: 35 rnorm deviation 100 -1 5 #> 36: 36 rnorm deviation 100 0 1 #> 37: 37 rnorm deviation 100 0 2 #> 38: 38 rnorm deviation 100 0 3 #> 39: 39 rnorm deviation 100 0 4 #> 40: 40 rnorm deviation 100 0 5 #> 41: 41 rnorm deviation 100 1 1 #> 42: 42 rnorm deviation 100 1 2 #> 43: 43 rnorm deviation 100 1 3 #> 44: 44 rnorm deviation 100 1 4 #> 45: 45 rnorm deviation 100 1 5 #> 46: 46 rexp average 100 NA NA #> 47: 47 rexp average 100 NA NA #> 48: 48 rexp average 100 NA NA #> 49: 49 rexp average 100 NA NA #> 50: 50 rexp average 100 NA NA #> 51: 51 rexp average 100 NA NA #> 52: 52 rexp average 100 NA NA #> 53: 53 rexp average 100 NA NA #> 54: 54 rexp average 100 NA NA #> 55: 55 rexp average 100 NA NA #> 56: 56 rexp deviation 100 NA NA #> 57: 57 rexp deviation 100 NA NA #> 58: 58 rexp deviation 100 NA NA #> 59: 59 rexp deviation 100 NA NA #> 60: 60 rexp deviation 100 NA NA #> job.id problem algorithm prob.pars.n prob.pars.mean prob.pars.sd #> prob.pars.lambda algo.pars.method result.res #> <int> <char> <num> #> 1: NA mean -0.842949204 #> 2: NA median -0.915451498 #> 3: NA mean -1.013829715 #> 4: NA median -1.026744582 #> 5: NA mean -0.544082757 #> 6: NA median -1.228882756 #> 7: NA mean -1.758195152 #> 8: NA median -1.017889502 #> 9: NA mean -0.721865242 #> 10: NA median -1.009905112 #> 11: NA mean -0.066968266 #> 12: NA median -0.001324423 #> 13: NA mean 0.116369676 #> 14: NA median -0.239593821 #> 15: NA mean -0.275914994 #> 16: NA median -0.219673804 #> 17: NA mean -0.390600924 #> 18: NA median -0.431722873 #> 19: NA mean -0.862369986 #> 20: NA median 0.310564934 #> 21: NA mean 0.919685582 #> 22: NA median 1.137638493 #> 23: NA mean 0.717251280 #> 24: NA median 1.000696628 #> 25: NA mean 1.079885160 #> 26: NA median 1.256486092 #> 27: NA mean 0.056569707 #> 28: NA median 1.511908035 #> 29: NA mean 1.772567494 #> 30: NA median 0.576546942 #> 31: NA <NA> 1.066915639 #> 32: NA <NA> 1.956072085 #> 33: NA <NA> 3.129048220 #> 34: NA <NA> 4.091646305 #> 35: NA <NA> 5.567688298 #> 36: NA <NA> 1.033162728 #> 37: NA <NA> 2.290784363 #> 38: NA <NA> 3.011799409 #> 39: NA <NA> 3.724917848 #> 40: NA <NA> 5.164125697 #> 41: NA <NA> 0.913834076 #> 42: NA <NA> 1.840190154 #> 43: NA <NA> 3.225940976 #> 44: NA <NA> 4.250388672 #> 45: NA <NA> 4.820613875 #> 46: 1 mean 0.976800412 #> 47: 1 median 0.561309444 #> 48: 2 mean 0.512651915 #> 49: 2 median 0.332483764 #> 50: 3 mean 0.363899709 #> 51: 3 median 0.282966778 #> 52: 4 mean 0.225608448 #> 53: 4 median 0.167649514 #> 54: 5 mean 0.186122531 #> 55: 5 median 0.128949969 #> 56: 1 <NA> 0.938268024 #> 57: 2 <NA> 0.488269167 #> 58: 3 <NA> 0.318698244 #> 59: 4 <NA> 0.223306907 #> 60: 5 <NA> 0.175971064 #> prob.pars.lambda algo.pars.method result.res