Apply Functions on Results

Source: R/reduceResults.R

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

reduceResults

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

Examples

 batchtools:::example_push_temp(2) 
### Example 1 - reduceResultsList
tmp = makeRegistry(file.dir = NA, make.default = FALSE)

#> No readable configuration file found
#> 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' ...
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)

#> No readable configuration file found
#> 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
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)

#> 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' ...

# 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 = ".")

#>     job.id problem algorithm prob.pars.n prob.pars.mean prob.pars.sd
#>  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
#>  1:               NA             mean -1.092018851
#>  2:               NA           median -0.863780644
#>  3:               NA             mean -1.084890423
#>  4:               NA           median -1.505171392
#>  5:               NA             mean -1.381319138
#>  6:               NA           median -1.341051423
#>  7:               NA             mean -1.188083630
#>  8:               NA           median  0.029939562
#>  9:               NA             mean -0.987419910
#> 10:               NA           median -1.673002281
#> 11:               NA             mean  0.007267359
#> 12:               NA           median  0.127401909
#> 13:               NA             mean -0.179617350
#> 14:               NA           median  0.046125736
#> 15:               NA             mean -0.057929853
#> 16:               NA           median  0.140366680
#> 17:               NA             mean -0.388849478
#> 18:               NA           median -1.207701427
#> 19:               NA             mean  0.042334184
#> 20:               NA           median -0.381653696
#> 21:               NA             mean  0.907293957
#> 22:               NA           median  1.033215601
#> 23:               NA             mean  1.020619322
#> 24:               NA           median  0.876104940
#> 25:               NA             mean  0.679306393
#> 26:               NA           median  0.977987955
#> 27:               NA             mean  0.675594915
#> 28:               NA           median  1.366615131
#> 29:               NA             mean  1.189950292
#> 30:               NA           median  0.263092706
#> 31:               NA             <NA>  1.040412625
#> 32:               NA             <NA>  2.392300899
#> 33:               NA             <NA>  3.042692900
#> 34:               NA             <NA>  4.263774294
#> 35:               NA             <NA>  5.296092785
#> 36:               NA             <NA>  1.071510659
#> 37:               NA             <NA>  2.072637870
#> 38:               NA             <NA>  3.153765268
#> 39:               NA             <NA>  3.419489935
#> 40:               NA             <NA>  5.558785921
#> 41:               NA             <NA>  1.012589238
#> 42:               NA             <NA>  2.015986064
#> 43:               NA             <NA>  3.170339048
#> 44:               NA             <NA>  4.281061044
#> 45:               NA             <NA>  4.988645174
#> 46:                1             mean  0.945622941
#> 47:                1           median  0.619859008
#> 48:                2             mean  0.446385905
#> 49:                2           median  0.420682183
#> 50:                3             mean  0.397613162
#> 51:                3           median  0.306781776
#> 52:                4             mean  0.277069910
#> 53:                4           median  0.165324176
#> 54:                5             mean  0.182106656
#> 55:                5           median  0.144752066
#> 56:                1             <NA>  0.942306695
#> 57:                2             <NA>  0.453601669
#> 58:                3             <NA>  0.292487926
#> 59:                4             <NA>  0.210416655
#> 60:                5             <NA>  0.189556888
#>     prob.pars.lambda algo.pars.method   result.res